nkasmanoff/hf-dataset-cards · Datasets at Hugging Face

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acronym_identification	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: [] paperswithcode_id: acronym-identification pretty_name: Acronym Identification Dataset tags: - acronym-identification dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: labels sequence: class_label: names: '0': B-long '1': B-short '2': I-long '3': I-short '4': O splits: - name: train num_bytes: 7792803 num_examples: 14006 - name: validation num_bytes: 952705 num_examples: 1717 - name: test num_bytes: 987728 num_examples: 1750 download_size: 8556464 dataset_size: 9733236 train-eval-index: - config: default task: token-classification task_id: entity_extraction splits: eval_split: test col_mapping: tokens: tokens labels: tags --- # Dataset Card for Acronym Identification Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://sites.google.com/view/sdu-aaai21/shared-task - Repository: https://github.com/amirveyseh/AAAI-21-SDU-shared-task-1-AI - Paper: [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://arxiv.org/pdf/2010.14678v1.pdf) - Leaderboard: https://competitions.codalab.org/competitions/26609 - Point of Contact: [More Information Needed] ### Dataset Summary This dataset contains the training, validation, and test data for the Shared Task 1: Acronym Identification of the AAAI-21 Workshop on Scientific Document Understanding. ### Supported Tasks and Leaderboards The dataset supports an `acronym-identification` task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared Task which supported a [leaderboard](https://competitions.codalab.org/competitions/26609). ### Languages The sentences in the dataset are in English (`en`). ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` {'id': 'TR-0', 'labels': [4, 4, 4, 4, 0, 2, 2, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4], 'tokens': ['What', 'is', 'here', 'called', 'controlled', 'natural', 'language', '(', 'CNL', ')', 'has', 'traditionally', 'been', 'given', 'many', 'different', 'names', '.']} ``` Please note that in test set sentences only the `id` and `tokens` fields are available. `labels` can be ignored for test set. Labels in the test set are all `O` ### Data Fields The data instances have the following fields: - `id`: a `string` variable representing the example id, unique across the full dataset - `tokens`: a list of `string` variables representing the word-tokenized sentence - `labels`: a list of `categorical` variables with possible values `["B-long", "B-short", "I-long", "I-short", "O"]` corresponding to a BIO scheme. `-long` corresponds to the expanded acronym, such as controlled natural language here, and `-short` to the abbrviation, `CNL` here. ### Data Splits The training, validation, and test set contain `14,006`, `1,717`, and `1750` sentences respectively. ## Dataset Creation ### Curation Rationale > First, most of the existing datasets for acronym identification (AI) are either limited in their sizes or created using simple rule-based methods. > This is unfortunate as rules are in general not able to capture all the diverse forms to express acronyms and their long forms in text. > Second, most of the existing datasets are in the medical domain, ignoring the challenges in other scientific domains. > In order to address these limitations this paper introduces two new datasets for Acronym Identification. > Notably, our datasets are annotated by human to achieve high quality and have substantially larger numbers of examples than the existing AI datasets in the non-medical domain. ### Source Data #### Initial Data Collection and Normalization > In order to prepare a corpus for acronym annotation, we collect a corpus of 6,786 English papers from arXiv. > These papers consist of 2,031,592 sentences that would be used for data annotation for AI in this work. The dataset paper does not report the exact tokenization method. #### Who are the source language producers? The language was comes from papers hosted on the online digital archive [arXiv](https://arxiv.org/). No more information is available on the selection process or identity of the writers. ### Annotations #### Annotation process > Each sentence for annotation needs to contain at least one word in which more than half of the characters in are capital letters (i.e., acronym candidates). > Afterward, we search for a sub-sequence of words in which the concatenation of the first one, two or three characters of the words (in the order of the words in the sub-sequence could form an acronym candidate. > We call the sub-sequence a long form candidate. If we cannot find any long form candidate, we remove the sentence. > Using this process, we end up with 17,506 sentences to be annotated manually by the annotators from Amazon Mechanical Turk (MTurk). > In particular, we create a HIT for each sentence and ask the workers to annotate the short forms and the long forms in the sentence. > In case of disagreements, if two out of three workers agree on an annotation, we use majority voting to decide the correct annotation. > Otherwise, a fourth annotator is hired to resolve the conflict #### Who are the annotators? Workers were recruited through Amazon MEchanical Turk and paid $0.05 per annotation. No further demographic information is provided. ### Personal and Sensitive Information Papers published on arXiv are unlikely to contain much personal information, although some do include some poorly chosen examples revealing personal details, so the data should be used with care. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset provided for this shared task is licensed under CC BY-NC-SA 4.0 international license. ### Citation Information ``` @inproceedings{Veyseh2020, author = {Amir Pouran Ben Veyseh and Franck Dernoncourt and Quan Hung Tran and Thien Huu Nguyen}, editor = {Donia Scott and N{\'{u}}ria Bel and Chengqing Zong}, title = {What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics, {COLING} 2020, Barcelona, Spain (Online), December 8-13, 2020}, pages = {3285--3301}, publisher = {International Committee on Computational Linguistics}, year = {2020}, url = {https://doi.org/10.18653/v1/2020.coling-main.292}, doi = {10.18653/v1/2020.coling-main.292} } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
ade_corpus_v2	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-classification - token-classification task_ids: - coreference-resolution - fact-checking pretty_name: Adverse Drug Reaction Data v2 configs: - Ade_corpus_v2_classification - Ade_corpus_v2_drug_ade_relation - Ade_corpus_v2_drug_dosage_relation dataset_info: - config_name: Ade_corpus_v2_classification features: - name: text dtype: string - name: label dtype: class_label: names: '0': Not-Related '1': Related splits: - name: train num_bytes: 3403711 num_examples: 23516 download_size: 3791162 dataset_size: 3403711 - config_name: Ade_corpus_v2_drug_ade_relation features: - name: text dtype: string - name: drug dtype: string - name: effect dtype: string - name: indexes struct: - name: drug sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 - name: effect sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 splits: - name: train num_bytes: 1546021 num_examples: 6821 download_size: 3791162 dataset_size: 1546021 - config_name: Ade_corpus_v2_drug_dosage_relation features: - name: text dtype: string - name: drug dtype: string - name: dosage dtype: string - name: indexes struct: - name: drug sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 - name: dosage sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 splits: - name: train num_bytes: 64725 num_examples: 279 download_size: 3791162 dataset_size: 64725 train-eval-index: - config: Ade_corpus_v2_classification task: text-classification task_id: multi_class_classification splits: train_split: train col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Adverse Drug Reaction Data v2 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.sciencedirect.com/science/article/pii/S1532046412000615 - Repository: [Needs More Information] - Paper: https://www.sciencedirect.com/science/article/pii/S1532046412000615 - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data. This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug. DRUG-AE.rel provides relations between drugs and adverse effects. DRUG-DOSE.rel provides relations between drugs and dosages. ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain any drug-related adverse effects. ### Supported Tasks and Leaderboards Sentiment classification, Relation Extraction ### Languages English ## Dataset Structure ### Data Instances #### Config - `Ade_corpus_v2_classification` ``` { 'label': 1, 'text': 'Intravenous azithromycin-induced ototoxicity.' } ``` #### Config - `Ade_corpus_v2_drug_ade_relation` ``` { 'drug': 'azithromycin', 'effect': 'ototoxicity', 'indexes': { 'drug': { 'end_char': [24], 'start_char': [12] }, 'effect': { 'end_char': [44], 'start_char': [33] } }, 'text': 'Intravenous azithromycin-induced ototoxicity.' } ``` #### Config - `Ade_corpus_v2_drug_dosage_relation` ``` { 'dosage': '4 times per day', 'drug': 'insulin', 'indexes': { 'dosage': { 'end_char': [56], 'start_char': [41] }, 'drug': { 'end_char': [40], 'start_char': [33]} }, 'text': 'She continued to receive regular insulin 4 times per day over the following 3 years with only occasional hives.' } ``` ### Data Fields #### Config - `Ade_corpus_v2_classification` - `text` - Input text. - `label` - Whether the adverse drug effect(ADE) related (1) or not (0). - #### Config - `Ade_corpus_v2_drug_ade_relation` - `text` - Input text. - `drug` - Name of drug. - `effect` - Effect caused by the drug. - `indexes.drug.start_char` - Start index of `drug` string in text. - `indexes.drug.end_char` - End index of `drug` string in text. - `indexes.effect.start_char` - Start index of `effect` string in text. - `indexes.effect.end_char` - End index of `effect` string in text. #### Config - `Ade_corpus_v2_drug_dosage_relation` - `text` - Input text. - `drug` - Name of drug. - `dosage` - Dosage of the drug. - `indexes.drug.start_char` - Start index of `drug` string in text. - `indexes.drug.end_char` - End index of `drug` string in text. - `indexes.dosage.start_char` - Start index of `dosage` string in text. - `indexes.dosage.end_char` - End index of `dosage` string in text. ### Data Splits \| Train \| \| ------ \| \| 23516 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @article{GURULINGAPPA2012885, title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports", journal = "Journal of Biomedical Informatics", volume = "45", number = "5", pages = "885 - 892", year = "2012", note = "Text Mining and Natural Language Processing in Pharmacogenomics", issn = "1532-0464", doi = "https://doi.org/10.1016/j.jbi.2012.04.008", url = "http://www.sciencedirect.com/science/article/pii/S1532046412000615", author = "Harsha Gurulingappa and Abdul Mateen Rajput and Angus Roberts and Juliane Fluck and Martin Hofmann-Apitius and Luca Toldo", keywords = "Adverse drug effect, Benchmark corpus, Annotation, Harmonization, Sentence classification", abstract = "A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F1 score of 0.70 indicating a potential useful application of the corpus." } ``` ### Contributions Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
adversarial_qa	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: adversarialqa pretty_name: adversarialQA train-eval-index: - config: adversarialQA task: question-answering task_id: extractive_question_answering splits: train_split: train eval_split: validation col_mapping: question: question context: context answers: text: text answer_start: answer_start metrics: - type: squad name: SQuAD dataset_info: - config_name: adversarialQA features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 27858794 num_examples: 30000 - name: validation num_bytes: 2757128 num_examples: 3000 - name: test num_bytes: 2919643 num_examples: 3000 download_size: 9018914 dataset_size: 33535565 - config_name: dbidaf features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9282518 num_examples: 10000 - name: validation num_bytes: 917943 num_examples: 1000 - name: test num_bytes: 947111 num_examples: 1000 download_size: 9018914 dataset_size: 11147572 - config_name: dbert features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9345557 num_examples: 10000 - name: validation num_bytes: 918192 num_examples: 1000 - name: test num_bytes: 971454 num_examples: 1000 download_size: 9018914 dataset_size: 11235203 - config_name: droberta features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9270719 num_examples: 10000 - name: validation num_bytes: 925065 num_examples: 1000 - name: test num_bytes: 1005406 num_examples: 1000 download_size: 9018914 dataset_size: 11201190 --- # Dataset Card for adversarialQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [adversarialQA homepage](https://adversarialqa.github.io/) - Repository: [adversarialQA repository](https://github.com/maxbartolo/adversarialQA) - Paper: [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293) - Leaderboard: [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall) - Point of Contact: [Max Bartolo]([email protected]) ### Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards `extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Data is provided in the same format as SQuAD 1.1. An example is shown below: ``` { "data": [ { "title": "Oxygen", "paragraphs": [ { "context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.", "qas": [ { "id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3", "question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?", "answers": [ { "answer_start": 36, "text": "organic compounds" } ] }, { "id": "4240a8e708c703796347a3702cf1463eed05584a", "question": "What letter does the abbreviation for acid anhydrides both begin and end in?", "answers": [ { "answer_start": 244, "text": "R" } ] }, { "id": "0681a0a5ec852ec6920d6a30f7ef65dced493366", "question": "Which of the organic compounds, in the article, contains nitrogen?", "answers": [ { "answer_start": 262, "text": "amides" } ] }, { "id": "2990efe1a56ccf81938fa5e18104f7d3803069fb", "question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?", "answers": [ { "answer_start": 262, "text": "amides" } ] } ] } ] } ] } ``` ### Data Fields - title: the title of the Wikipedia page from which the context is sourced - context: the context/passage - id: a string identifier for each question - answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text. Note that no answers are provided in the test set. Indeed, this dataset is part of the DynaBench benchmark, for which you can submit your predictions on the [website](https://dynabench.org/tasks/2#1). ### Data Splits The dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples. ## Dataset Creation ### Curation Rationale This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models. ### Source Data #### Initial Data Collection and Normalization The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250). #### Who are the source language producers? The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions. ### Annotations #### Annotation process The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model. #### Who are the annotators? The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation. ### Personal and Sensitive Information No annotator identifying details are provided. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question. It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application. ### Discussion of Biases The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol. ### Other Known Limitations N/a ## Additional Information ### Dataset Curators This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL). ### Licensing Information This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{bartolo2020beat, author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus}, title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {662-678}, year = {2020}, doi = {10.1162/tacl\_a\_00338}, URL = { https://doi.org/10.1162/tacl_a_00338 }, eprint = { https://doi.org/10.1162/tacl_a_00338 }, abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). } } ``` ### Contributions Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.
aeslc	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: 'AESLC: Annotated Enron Subject Line Corpus' size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: aeslc tags: - aspect-based-summarization - conversations-summarization - multi-document-summarization - email-headline-generation dataset_info: features: - name: email_body dtype: string - name: subject_line dtype: string splits: - name: train num_bytes: 11902668 num_examples: 14436 - name: validation num_bytes: 1660730 num_examples: 1960 - name: test num_bytes: 1384177 num_examples: 1906 download_size: 11643743 dataset_size: 14947575 --- # Dataset Card for "aeslc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: https://github.com/ryanzhumich/AESLC - Paper: [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://arxiv.org/abs/1906.03497) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 11.64 MB - Size of the generated dataset: 14.95 MB - Total amount of disk used: 26.59 MB ### Dataset Summary A collection of email messages of employees in the Enron Corporation. There are two features: - email_body: email body text. - subject_line: email subject text. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages Monolingual English (mainly en-US) with some exceptions. ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 11.64 MB - Size of the generated dataset: 14.95 MB - Total amount of disk used: 26.59 MB An example of 'train' looks as follows. ``` { "email_body": "B/C\n<<some doc>>\n", "subject_line": "Service Agreement" } ``` ### Data Fields The data fields are the same among all splits. #### default - `email_body`: a `string` feature. - `subject_line`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\|14436\| 1960\|1906\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{zhang-tetreault-2019-email, title = "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation", author = "Zhang, Rui and Tetreault, Joel", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1043", doi = "10.18653/v1/P19-1043", pages = "446--456", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
afrikaans_ner_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Afrikaans Ner Corpus license_details: Creative Commons Attribution 2.5 South Africa License dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC config_name: afrikaans_ner_corpus splits: - name: train num_bytes: 4025667 num_examples: 8962 download_size: 25748344 dataset_size: 4025667 --- # Dataset Card for Afrikaans Ner Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Afrikaans Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/299) - Repository: - Paper: - Leaderboard: - Point of Contact: [Martin Puttkammer](mailto:[email protected]) ### Dataset Summary The Afrikaans Ner Corpus is an Afrikaans dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Afrikaans. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [0, 0, 0, 0, 0], 'tokens': ['Vertaling', 'van', 'die', 'inligting', 'in'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Afrikaans. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. [More Information Needed] #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{afrikaans_ner_corpus, author = { Gerhard van Huyssteen and Martin Puttkammer and E.B. Trollip and J.C. Liversage and Roald Eiselen}, title = {NCHLT Afrikaans Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/299}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
ag_news	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - topic-classification paperswithcode_id: ag-news pretty_name: AG’s News Corpus dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': World '1': Sports '2': Business '3': Sci/Tech splits: - name: train num_bytes: 29817351 num_examples: 120000 - name: test num_bytes: 1879478 num_examples: 7600 download_size: 31327765 dataset_size: 31696829 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "ag_news" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html](http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 31.33 MB - Size of the generated dataset: 31.70 MB - Total amount of disk used: 63.02 MB ### Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . The AG's news topic classification dataset is constructed by Xiang Zhang ([email protected]) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 31.33 MB - Size of the generated dataset: 31.70 MB - Total amount of disk used: 63.02 MB An example of 'train' looks as follows. ``` { "label": 3, "text": "New iPad released Just like every other September, this one is no different. Apple is planning to release a bigger, heavier, fatter iPad that..." } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `label`: a classification label, with possible values including `World` (0), `Sports` (1), `Business` (2), `Sci/Tech` (3). ### Data Splits \| name \|train \|test\| \|-------\|-----:\|---:\| \|default\|120000\|7600\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Zhang2015CharacterlevelCN, title={Character-level Convolutional Networks for Text Classification}, author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun}, booktitle={NIPS}, year={2015} } ``` ### Contributions Thanks to [@jxmorris12](https://github.com/jxmorris12), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@lewtun](https://github.com/lewtun) for adding this dataset.
ai2_arc	--- annotations_creators: - found language_creators: - found language: - en language_bcp47: - en-US license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa - multiple-choice-qa paperswithcode_id: null pretty_name: Ai2Arc dataset_info: - config_name: ARC-Challenge features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 351888 num_examples: 1119 - name: test num_bytes: 377740 num_examples: 1172 - name: validation num_bytes: 97254 num_examples: 299 download_size: 680841265 dataset_size: 826882 - config_name: ARC-Easy features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 623254 num_examples: 2251 - name: test num_bytes: 661997 num_examples: 2376 - name: validation num_bytes: 158498 num_examples: 570 download_size: 680841265 dataset_size: 1443749 --- # Dataset Card for "ai2_arc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://allenai.org/data/arc](https://allenai.org/data/arc) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1361.68 MB - Size of the generated dataset: 2.28 MB - Total amount of disk used: 1363.96 MB ### Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### ARC-Challenge - Size of downloaded dataset files: 680.84 MB - Size of the generated dataset: 0.83 MB - Total amount of disk used: 681.67 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` #### ARC-Easy - Size of downloaded dataset files: 680.84 MB - Size of the generated dataset: 1.45 MB - Total amount of disk used: 682.29 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` ### Data Fields The data fields are the same among all splits. #### ARC-Challenge - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. #### ARC-Easy - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------------\|----:\|---------:\|---:\| \|ARC-Challenge\| 1119\| 299\|1172\| \|ARC-Easy \| 2251\| 570\|2376\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{allenai:arc, author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
air_dialogue	--- pretty_name: AirDialogue annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - conversational - text-generation - fill-mask task_ids: - dialogue-generation - dialogue-modeling - language-modeling - masked-language-modeling paperswithcode_id: null dataset_info: - config_name: air_dialogue_data features: - name: action struct: - name: status dtype: string - name: name dtype: string - name: flight sequence: int32 - name: intent struct: - name: return_month dtype: string - name: return_day dtype: string - name: max_price dtype: int32 - name: departure_airport dtype: string - name: max_connections dtype: int32 - name: departure_day dtype: string - name: goal dtype: string - name: departure_month dtype: string - name: name dtype: string - name: return_airport dtype: string - name: timestamps sequence: int64 - name: dialogue sequence: string - name: expected_action struct: - name: status dtype: string - name: name dtype: string - name: flight sequence: int32 - name: search_info list: - name: button_name dtype: string - name: field_name dtype: string - name: field_value dtype: string - name: timestmamp dtype: int64 - name: correct_sample dtype: bool_ splits: - name: train num_bytes: 353721137 num_examples: 321459 - name: validation num_bytes: 44442238 num_examples: 40363 download_size: 272898923 dataset_size: 398163375 - config_name: air_dialogue_kb features: - name: kb list: - name: airline dtype: string - name: class dtype: string - name: departure_airport dtype: string - name: departure_day dtype: string - name: departure_month dtype: string - name: departure_time_num dtype: int32 - name: flight_number dtype: int32 - name: num_connections dtype: int32 - name: price dtype: int32 - name: return_airport dtype: string - name: return_day dtype: string - name: return_month dtype: string - name: return_time_num dtype: int32 - name: reservation dtype: int32 splits: - name: train num_bytes: 782592158 num_examples: 321459 - name: validation num_bytes: 98269789 num_examples: 40363 download_size: 272898923 dataset_size: 880861947 --- # Dataset Card for air_dialogue ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 - Repository: https://github.com/google/airdialogue - Paper: https://www.aclweb.org/anthology/D18-1419/ - Leaderboard: https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 - Point of Contact: [AirDialogue-Google](mailto:[email protected]) [Aakash Gupta](mailto:[email protected]) ### Dataset Summary AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. ### Supported Tasks and Leaderboards We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores The inference competition & leaderboard can be found here: https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 ### Languages The text in the dataset is in English. The BCP 47 code is `en` ## Dataset Structure ### Data Instances The data is provided in two set of files. The first one has the dialogues (`air_dialogue_data`) and the knowledge-base (`air_dialogue_kb`) BuilderConfig: `air_dialogue_data` ``` {"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true} ``` BuilderConfig: `air_dialogue_kb` ``` {"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0} ``` ### Data Fields BuilderConfig: `air_dialogue_data`: Provides for customer context, dialogue states and environment key name \| Description \| \|---\|---\| \|'search_action' \| search action performed by customer \| \|'action' \| Action taken by the agent \| \|'intent' \| Intents from the conversation \| \|'timestamps' \| Timestamp for each of the dialogues \| \|'dialogue' \| Dialogue recorded between agent & customer \| \|'expected_action' \| Expected action from agent (human-annotated)\| \|'correct_sample' \| whether action performed by agent was same as expected_action \| BuilderConfig: `air_dialogue_kb`: Provides for the Agent Context _ca_ = (_db_, _r_ ) key name \| Description \| \|---\|---\| \|'kb' \| Available flights in the database \| \|'reservation' \| whether customer has an existing reservation\| ### Data Splits Data is split into Train/Dev & Test in the ration of 80%, 10% and 10% ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information No personal and sensitive information is stored ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [AirDialogue team](mailto:[email protected]) For issues regarding HuggingFace Dataset Hub implementation [Aakash Gupta](mailto:[email protected]) ### Licensing Information cc-by-nc-4.0 ### Citation Information @inproceedings{wei-etal-2018-airdialogue, title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research", author = "Wei, Wei and Le, Quoc and Dai, Andrew and Li, Jia", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1419", doi = "10.18653/v1/D18-1419", pages = "3844--3854", abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.", } ### Contributions Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.
ajgt_twitter_ar	--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Arabic Jordanian General Tweets dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Negative '1': Positive config_name: plain_text splits: - name: train num_bytes: 175424 num_examples: 1800 download_size: 107395 dataset_size: 175424 --- # Dataset Card for Arabic Jordanian General Tweets ## Table of Contents - [Dataset Card for Arabic Jordanian General Tweets](#dataset-card-for-arabic-jordanian-general-tweets) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [\|split\|num examples\|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Arabic Jordanian General Tweets](https://github.com/komari6/Arabic-twitter-corpus-AJGT) - Paper: [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66) - Point of Contact: [Khaled Alomari]([email protected]) ### Dataset Summary Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66). ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A binary datset with with negative and positive sentiments. ### Data Fields - `text` (str): Tweet text. - `label` (int): Sentiment. ### Data Splits The dataset is not split. \| \| train \| \|----------\|------:\| \| no split \| 1,800 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Contains 1,800 tweets collected from twitter. #### Who are the source language producers? From tweeter. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{alomari2017arabic, title={Arabic tweets sentimental analysis using machine learning}, author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled}, booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}, pages={602--610}, year={2017}, organization={Springer} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
allegro_reviews	--- annotations_creators: - found language_creators: - found language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-scoring - text-scoring paperswithcode_id: allegro-reviews pretty_name: Allegro Reviews dataset_info: features: - name: text dtype: string - name: rating dtype: float32 splits: - name: train num_bytes: 4899539 num_examples: 9577 - name: test num_bytes: 514527 num_examples: 1006 - name: validation num_bytes: 515785 num_examples: 1002 download_size: 2314847 dataset_size: 5929851 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://klejbenchmark.com/ - Repository: https://github.com/allegro/klejbenchmark-allegroreviews - Paper: KLEJ: Comprehensive Benchmark for Polish Language Understanding (Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz) - Leaderboard: https://klejbenchmark.com/leaderboard/ - Point of Contact: [email protected] ### Dataset Summary Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review). We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com. ### Supported Tasks and Leaderboards Product reviews sentiment analysis. https://klejbenchmark.com/leaderboard/ ### Languages Polish ## Dataset Structure ### Data Instances Two tsv files (train, dev) with two columns (text, rating) and one (test) with just one (text). ### Data Fields - text: a product review of at least 50 words - rating: product rating of a scale of one (negative review) to five (positive review) ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale This dataset is one of nine evaluation tasks to improve polish language processing. ### Source Data #### Initial Data Collection and Normalization The Allegro Reviews is a set of product reviews from a popular e-commerce marketplace (Allegro.pl). #### Who are the source language producers? Customers of an e-commerce marketplace. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Allegro Machine Learning Research team [email protected] ### Licensing Information Dataset licensed under CC BY-SA 4.0 ### Citation Information @inproceedings{rybak-etal-2020-klej, title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding", author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.111", pages = "1191--1201", } ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.
allocine	--- annotations_creators: - no-annotation language_creators: - found language: - fr license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: allocine pretty_name: Allociné dataset_info: features: - name: review dtype: string - name: label dtype: class_label: names: '0': neg '1': pos config_name: allocine splits: - name: train num_bytes: 91330696 num_examples: 160000 - name: validation num_bytes: 11546250 num_examples: 20000 - name: test num_bytes: 11547697 num_examples: 20000 download_size: 66625305 dataset_size: 114424643 train-eval-index: - config: allocine task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: review: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Allociné ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: [Allociné dataset repository](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/tree/master/allocine_dataset) - Paper: - Leaderboard: - Point of Contact: [Théophile Blard](mailto:[email protected]) ### Dataset Summary The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the [Allociné.fr](https://www.allocine.fr/) community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k). ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset can be used to train a model for sentiment classification. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. A BERT-based model, [tf-allociné](https://huggingface.co/tblard/tf-allocine), achieves 97.44% accuracy on the test set. ### Languages The text is in French, as spoken by users of the [Allociné.fr](https://www.allocine.fr/) website. The BCP-47 code for French is fr. ## Dataset Structure ### Data Instances Each data instance contains the following features: _review_ and _label_. In the Hugging Face distribution of the dataset, the _label_ has 2 possible values, _0_ and _1_, which correspond to _negative_ and _positive_ respectively. See the [Allociné corpus viewer](https://huggingface.co/datasets/viewer/?dataset=allocine) to explore more examples. An example from the Allociné train set looks like the following: ``` {'review': 'Premier film de la saga Kozure Okami, "Le Sabre de la vengeance" est un très bon film qui mêle drame et action, et qui, en 40 ans, n'a pas pris une ride.', 'label': 1} ``` ### Data Fields - 'review': a string containing the review text - 'label': an integer, either _0_ or _1_, indicating a _negative_ or _positive_ review, respectively ### Data Splits The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews. \| Dataset Split \| Number of Instances in Split \| Percent Negative Reviews \| Percent Positive Reviews \| \| ------------- \| ---------------------------- \| ------------------------ \| ------------------------ \| \| Train \| 160,000 \| 49.6% \| 50.4% \| \| Validation \| 20,000 \| 51.0% \| 49.0% \| \| Test \| 20,000 \| 52.0% \| 48.0% \| ## Dataset Creation ### Curation Rationale The Allociné dataset was developed to support large-scale sentiment analysis in French. It was released alongside the [tf-allociné](https://huggingface.co/tblard/tf-allocine) model and used to compare the performance of several language models on this task. ### Source Data #### Initial Data Collection and Normalization The reviews and ratings were collected using a list of [film page urls](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_films_urls.txt) and the [allocine_scraper.py](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_scraper.py) tool. Up to 30 reviews were collected for each film. The reviews were originally labeled with a rating from 0.5 to 5.0 with a step of 0.5 between each rating. Ratings less than or equal to 2 are labeled as negative and ratings greater than or equal to 4 are labeled as positive. Only reviews with less than 2000 characters are included in the dataset. #### Who are the source language producers? The dataset contains movie reviews produced by the online community of the [Allociné.fr](https://www.allocine.fr/) website. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Reviewer usernames or personal information were not collected with the reviews, but could potentially be recovered. The content of each review may include information and opinions about the film's actors, film crew, and plot. ## Considerations for Using the Data ### Social Impact of Dataset Sentiment classification is a complex task which requires sophisticated language understanding skills. Successful models can support decision-making based on the outcome of the sentiment analysis, though such models currently require a high degree of domain specificity. It should be noted that the community represented in the dataset may not represent any downstream application's potential users, and the observed behavior of a model trained on this dataset may vary based on the domain and use case. ### Discussion of Biases The Allociné website lists a number of topics which violate their [terms of service](https://www.allocine.fr/service/conditions.html#charte). Further analysis is needed to determine the extent to which moderators have successfully removed such content. ### Other Known Limitations The limitations of the Allociné dataset have not yet been investigated, however [Staliūnaitė and Bonfil (2017)](https://www.aclweb.org/anthology/W17-5410.pdf) detail linguistic phenomena that are generally present in sentiment analysis but difficult for models to accurately label, such as negation, adverbial modifiers, and reviewer pragmatics. ## Additional Information ### Dataset Curators The Allociné dataset was collected by Théophile Blard. ### Licensing Information The Allociné dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information > Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert> ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@TheophileBlard](https://github.com/TheophileBlard), [@lewtun](https://github.com/lewtun) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.
alt	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - bn - en - fil - hi - id - ja - km - lo - ms - my - th - vi - zh license: - cc-by-4.0 multilinguality: - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation - token-classification task_ids: - parsing paperswithcode_id: alt pretty_name: Asian Language Treebank configs: - alt-en - alt-jp - alt-km - alt-my - alt-my-transliteration - alt-my-west-transliteration - alt-parallel dataset_info: - config_name: alt-parallel features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: translation dtype: translation: languages: - bg - en - en_tok - fil - hi - id - ja - khm - lo - ms - my - th - vi - zh splits: - name: train num_bytes: 68445916 num_examples: 18088 - name: validation num_bytes: 3710979 num_examples: 1000 - name: test num_bytes: 3814431 num_examples: 1019 download_size: 21285784 dataset_size: 75971326 - config_name: alt-en features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: status dtype: string - name: value dtype: string splits: - name: train num_bytes: 10075569 num_examples: 17889 - name: validation num_bytes: 544719 num_examples: 988 - name: test num_bytes: 567272 num_examples: 1017 download_size: 3871379 dataset_size: 11187560 - config_name: alt-jp features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: status dtype: string - name: value dtype: string - name: word_alignment dtype: string - name: jp_tokenized dtype: string - name: en_tokenized dtype: string splits: - name: train num_bytes: 21888277 num_examples: 17202 - name: validation num_bytes: 1181555 num_examples: 953 - name: test num_bytes: 1175592 num_examples: 931 download_size: 13191239 dataset_size: 24245424 - config_name: alt-my features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: value dtype: string splits: - name: train num_bytes: 20433275 num_examples: 18088 - name: validation num_bytes: 1111410 num_examples: 1000 - name: test num_bytes: 1135209 num_examples: 1018 download_size: 3028302 dataset_size: 22679894 - config_name: alt-km features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: km_pos_tag dtype: string - name: km_tokenized dtype: string splits: - name: train num_bytes: 12015411 num_examples: 18088 - name: validation num_bytes: 655232 num_examples: 1000 - name: test num_bytes: 673753 num_examples: 1018 download_size: 2410832 dataset_size: 13344396 - config_name: alt-my-transliteration features: - name: en dtype: string - name: my sequence: string splits: - name: train num_bytes: 4249424 num_examples: 84022 download_size: 1232127 dataset_size: 4249424 - config_name: alt-my-west-transliteration features: - name: en dtype: string - name: my sequence: string splits: - name: train num_bytes: 7412043 num_examples: 107121 download_size: 2830071 dataset_size: 7412043 --- # Dataset Card for Asian Language Treebank (ALT) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ - Leaderboard: - Paper: [Introduction of the Asian Language Treebank](https://ieeexplore.ieee.org/abstract/document/7918974) - Point of Contact: [ALT info]([email protected]) ### Dataset Summary The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under [ASEAN IVO](https://www.nict.go.jp/en/asean_ivo/index.html) as described in this Web page. The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages. ### Supported Tasks and Leaderboards Machine Translation, Dependency Parsing ### Languages It supports 13 language: * Bengali * English * Filipino * Hindi * Bahasa Indonesia * Japanese * Khmer * Lao * Malay * Myanmar (Burmese) * Thai * Vietnamese * Chinese (Simplified Chinese). ## Dataset Structure ### Data Instances #### ALT Parallel Corpus ``` { "SNT.URLID": "80188", "SNT.URLID.SNTID": "1", "url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal", "bg": "[translated sentence]", "en": "[translated sentence]", "en_tok": "[translated sentence]", "fil": "[translated sentence]", "hi": "[translated sentence]", "id": "[translated sentence]", "ja": "[translated sentence]", "khm": "[translated sentence]", "lo": "[translated sentence]", "ms": "[translated sentence]", "my": "[translated sentence]", "th": "[translated sentence]", "vi": "[translated sentence]", "zh": "[translated sentence]" } ``` #### ALT Treebank ``` { "SNT.URLID": "80188", "SNT.URLID.SNTID": "1", "url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal", "status": "draft/reviewed", "value": "(S (S (BASENP (NNP Italy)) (VP (VBP have) (VP (VP (VP (VBN defeated) (BASENP (NNP Portugal))) (ADVP (RB 31-5))) (PP (IN in) (NP (BASENP (NNP Pool) (NNP C)) (PP (IN of) (NP (BASENP (DT the) (NN 2007) (NNP Rugby) (NNP World) (NNP Cup)) (PP (IN at) (NP (BASENP (NNP Parc) (FW des) (NNP Princes)) (COMMA ,) (BASENP (NNP Paris) (COMMA ,) (NNP France))))))))))) (PERIOD .))" } ``` #### ALT Myanmar transliteration ``` { "en": "CASINO", "my": [ "ကက်စီနို", "ကစီနို", "ကာစီနို", "ကာဆီနို" ] } ``` ### Data Fields #### ALT Parallel Corpus - SNT.URLID: URL link to the source article listed in [URL.txt](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ALT-Parallel-Corpus-20191206/URL.txt) - SNT.URLID.SNTID: index number from 1 to 20000. It is a seletected sentence from `SNT.URLID` and bg, en, fil, hi, id, ja, khm, lo, ms, my, th, vi, zh correspond to the target language #### ALT Treebank - status: it indicates how a sentence is annotated; `draft` sentences are annotated by one annotater and `reviewed` sentences are annotated by two annotater The annotatation is different from language to language, please see [their guildlines](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/) for more detail. ### Data Splits \| \| train \| valid \| test \| \|-----------\|-------\|-------\|-------\| \| # articles \| 1698 \| 98 \| 97 \| \| # sentences \| 18088 \| 1000 \| 1018 \| ## Dataset Creation ### Curation Rationale The ALT project was initiated by the [National Institute of Information and Communications Technology, Japan](https://www.nict.go.jp/en/) (NICT) in 2014. NICT started to build Japanese and English ALT and worked with the University of Computer Studies, Yangon, Myanmar (UCSY) to build Myanmar ALT in 2014. Then, the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT), the Institute for Infocomm Research, Singapore (I2R), the Institute of Information Technology, Vietnam (IOIT), and the National Institute of Posts, Telecoms and ICT, Cambodia (NIPTICT) joined to make ALT for Indonesian, Malay, Vietnamese, and Khmer in 2015. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset is sampled from the English Wikinews in 2014. These will be annotated with word segmentation, POS tags, and syntax information, in addition to the word alignment information by linguistic experts from * National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English * University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar * the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian * the Institute for Infocomm Research, Singapore (I2R) for Malay * the Institute of Information Technology, Vietnam (IOIT) for Vietnamese * the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators * National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English * University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar * the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian * the Institute for Infocomm Research, Singapore (I2R) for Malay * the Institute of Information Technology, Vietnam (IOIT) for Vietnamese * the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer ### Licensing Information [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) ### Citation Information Please cite the following if you make use of the dataset: Hammam Riza, Michael Purwoadi, Gunarso, Teduh Uliniansyah, Aw Ai Ti, Sharifah Mahani Aljunied, Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Vichet Chea, Rapid Sun, Sethserey Sam, Sopheap Seng, Khin Mar Soe, Khin Thandar Nwet, Masao Utiyama, Chenchen Ding. (2016) "Introduction of the Asian Language Treebank" Oriental COCOSDA. BibTeX: ``` @inproceedings{riza2016introduction, title={Introduction of the asian language treebank}, author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others}, booktitle={2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)}, pages={1--6}, year={2016}, organization={IEEE} } ``` ### Contributions Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset.
amazon_polarity	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Amazon Review Polarity dataset_info: features: - name: label dtype: class_label: names: '0': negative '1': positive - name: title dtype: string - name: content dtype: string config_name: amazon_polarity splits: - name: train num_bytes: 1604364432 num_examples: 3600000 - name: test num_bytes: 178176193 num_examples: 400000 download_size: 688339454 dataset_size: 1782540625 train-eval-index: - config: amazon_polarity task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: content: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Amazon Review Polarity ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://registry.opendata.aws/ - Repository: https://github.com/zhangxiangxiao/Crepe - Paper: https://arxiv.org/abs/1509.01626 - Leaderboard: [Needs More Information] - Point of Contact: [Xiang Zhang](mailto:[email protected]) ### Dataset Summary The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating. ### Languages Mainly English. ## Dataset Structure ### Data Instances A typical data point, comprises of a title, a content and the corresponding label. An example from the AmazonPolarity test set looks as follows: ``` { 'title':'Great CD', 'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""", 'label':1 } ``` ### Data Fields - 'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'label': either 1 (positive) or 0 (negative) rating. ### Data Splits The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples. ## Dataset Creation ### Curation Rationale The Amazon reviews polarity dataset is constructed by Xiang Zhang ([email protected]). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Apache License 2.0 ### Citation Information McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013. Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015) ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.
amazon_reviews_multi	--- annotations_creators: - found language_creators: - found language: - de - en - es - fr - ja - zh license: - other multilinguality: - monolingual - multilingual size_categories: - 100K<n<1M - 1M<n<10M source_datasets: - original task_categories: - summarization - text-generation - fill-mask - text-classification task_ids: - text-scoring - language-modeling - masked-language-modeling - sentiment-classification - sentiment-scoring - topic-classification paperswithcode_id: null pretty_name: The Multilingual Amazon Reviews Corpus configs: - all_languages - de - en - es - fr - ja - zh dataset_info: - config_name: all_languages features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 364405048 num_examples: 1200000 - name: validation num_bytes: 9047533 num_examples: 30000 - name: test num_bytes: 9099141 num_examples: 30000 download_size: 640320386 dataset_size: 382551722 - config_name: de features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 64485678 num_examples: 200000 - name: validation num_bytes: 1605727 num_examples: 5000 - name: test num_bytes: 1611044 num_examples: 5000 download_size: 94802490 dataset_size: 67702449 - config_name: en features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 58601089 num_examples: 200000 - name: validation num_bytes: 1474672 num_examples: 5000 - name: test num_bytes: 1460565 num_examples: 5000 download_size: 86094112 dataset_size: 61536326 - config_name: es features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 52375658 num_examples: 200000 - name: validation num_bytes: 1303958 num_examples: 5000 - name: test num_bytes: 1312347 num_examples: 5000 download_size: 81345461 dataset_size: 54991963 - config_name: fr features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 54593565 num_examples: 200000 - name: validation num_bytes: 1340763 num_examples: 5000 - name: test num_bytes: 1364510 num_examples: 5000 download_size: 85917293 dataset_size: 57298838 - config_name: ja features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 82401390 num_examples: 200000 - name: validation num_bytes: 2035391 num_examples: 5000 - name: test num_bytes: 2048048 num_examples: 5000 download_size: 177773783 dataset_size: 86484829 - config_name: zh features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 51947668 num_examples: 200000 - name: validation num_bytes: 1287106 num_examples: 5000 - name: test num_bytes: 1302711 num_examples: 5000 download_size: 114387247 dataset_size: 54537485 --- # Dataset Card for The Multilingual Amazon Reviews Corpus ## Table of Contents - [Dataset Card for amazon_reviews_multi](#dataset-card-for-amazon_reviews_multi) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [plain_text](#plain_text) - [Data Fields](#data-fields) - [plain_text](#plain_text-1) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Webpage: https://registry.opendata.aws/amazon-reviews-ml/ - Paper: https://arxiv.org/abs/2010.02573 - Point of Contact: [[email protected]](mailto:[email protected]) ### Dataset Summary We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long. Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish. ## Dataset Structure ### Data Instances Each data instance corresponds to a review. The original JSON for an instance looks like so (German example): ```json { "review_id": "de_0784695", "product_id": "product_de_0572654", "reviewer_id": "reviewer_de_0645436", "stars": "1", "review_body": "Leider, leider nach einmal waschen ausgeblichen . Es sieht super h\u00fcbsch aus , nur leider stinkt es ganz schrecklich und ein Waschgang in der Maschine ist notwendig ! Nach einem mal waschen sah es aus als w\u00e4re es 10 Jahre alt und hatte 1000 e von Waschg\u00e4ngen hinter sich :( echt schade !", "review_title": "Leider nicht zu empfehlen", "language": "de", "product_category": "home" } ``` ### Data Fields - `review_id`: A string identifier of the review. - `product_id`: A string identifier of the product being reviewed. - `reviewer_id`: A string identifier of the reviewer. - `stars`: An int between 1-5 indicating the number of stars. - `review_body`: The text body of the review. - `review_title`: The text title of the review. - `language`: The string identifier of the review language. - `product_category`: String representation of the product's category. ### Data Splits Each language configuration comes with its own `train`, `validation`, and `test` splits. The `all_languages` split is simply a concatenation of the corresponding split across all languages. That is, the `train` split for `all_languages` is a concatenation of the `train` splits for each of the languages and likewise for `validation` and `test`. ## Dataset Creation ### Curation Rationale The dataset is motivated by the desire to advance sentiment analysis and text classification in other (non-English) languages. ### Source Data #### Initial Data Collection and Normalization The authors gathered the reviews from the marketplaces in the US, Japan, Germany, France, Spain, and China for the English, Japanese, German, French, Spanish, and Chinese languages, respectively. They then ensured the correct language by applying a language detection algorithm, only retaining those of the target language. In a random sample of the resulting reviews, the authors observed a small percentage of target languages that were incorrectly filtered out and a very few mismatched languages that were incorrectly retained. #### Who are the source language producers? The original text comes from Amazon customers reviewing products on the marketplace across a variety of product categories. ### Annotations #### Annotation process Each of the fields included are submitted by the user with the review or otherwise associated with the review. No manual or machine-driven annotation was necessary. #### Who are the annotators? N/A ### Personal and Sensitive Information According to the original dataset [license terms](https://docs.opendata.aws/amazon-reviews-ml/license.txt), you may not: - link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or - attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures. Unfortunately, each of the languages included here is relatively high resource and well studied. ### Discussion of Biases The dataset contains only reviews from verified purchases (as described in the paper, section 2.1), and the reviews should conform the [Amazon Community Guidelines](https://www.amazon.com/gp/help/customer/display.html?nodeId=GLHXEX85MENUE4XF). ### Other Known Limitations The dataset is constructed so that the distribution of star ratings is balanced. This feature has some advantages for purposes of classification, but some types of language may be over or underrepresented relative to the original distribution of reviews to achieve this balance. ## Additional Information ### Dataset Curators Published by Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. Managed by Amazon. ### Licensing Information Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc. A copy of the agreement can be found at the dataset webpage here: https://docs.opendata.aws/amazon-reviews-ml/license.txt By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions: In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. ### Citation Information Please cite the following paper (arXiv) if you found this dataset useful: Phillip Keung, Yichao Lu, György Szarvas and Noah A. Smith. “The Multilingual Amazon Reviews Corpus.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020. ``` @inproceedings{marc_reviews, title={The Multilingual Amazon Reviews Corpus}, author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing}, year={2020} } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
amazon_us_reviews	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - summarization - text-generation - fill-mask - text-classification task_ids: - text-scoring - language-modeling - masked-language-modeling - sentiment-classification - sentiment-scoring - topic-classification pretty_name: Amazon US Reviews dataset_info: - config_name: Books_v1_01 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 6997552259 num_examples: 6106719 download_size: 2692708591 dataset_size: 6997552259 - config_name: Watches_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 458976082 num_examples: 960872 download_size: 162973819 dataset_size: 458976082 - config_name: Personal_Care_Appliances_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 49036547 num_examples: 85981 download_size: 17634794 dataset_size: 49036547 - config_name: Mobile_Electronics_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 63293377 num_examples: 104975 download_size: 22870508 dataset_size: 63293377 - config_name: Digital_Video_Games_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 80176851 num_examples: 145431 download_size: 27442648 dataset_size: 80176851 - config_name: Digital_Software_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 58782931 num_examples: 102084 download_size: 18997559 dataset_size: 58782931 - config_name: Major_Appliances_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 67642424 num_examples: 96901 download_size: 24359816 dataset_size: 67642424 - config_name: Gift_Card_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 47188062 num_examples: 149086 download_size: 12134676 dataset_size: 47188062 - config_name: Video_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 356264426 num_examples: 380604 download_size: 138929896 dataset_size: 356264426 - config_name: Luggage_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 167354173 num_examples: 348657 download_size: 60320191 dataset_size: 167354173 - config_name: Software_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 266020595 num_examples: 341931 download_size: 94010685 dataset_size: 266020595 - config_name: Video_Games_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1291054668 num_examples: 1785997 download_size: 475199894 dataset_size: 1291054668 - config_name: Furniture_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 405212374 num_examples: 792113 download_size: 148982796 dataset_size: 405212374 - config_name: Musical_Instruments_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 518908568 num_examples: 904765 download_size: 193389086 dataset_size: 518908568 - config_name: Digital_Music_Purchase_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 710546079 num_examples: 1688884 download_size: 253570168 dataset_size: 710546079 - config_name: Books_v1_02 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3387034903 num_examples: 3105520 download_size: 1329539135 dataset_size: 3387034903 - config_name: Home_Entertainment_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 534333848 num_examples: 705889 download_size: 193168458 dataset_size: 534333848 - config_name: Grocery_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1072289473 num_examples: 2402458 download_size: 401337166 dataset_size: 1072289473 - config_name: Outdoors_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1172986088 num_examples: 2302401 download_size: 448963100 dataset_size: 1172986088 - config_name: Pet_Products_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1355659812 num_examples: 2643619 download_size: 515815253 dataset_size: 1355659812 - config_name: Video_DVD_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3953234561 num_examples: 5069140 download_size: 1512355451 dataset_size: 3953234561 - config_name: Apparel_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2256558450 num_examples: 5906333 download_size: 648641286 dataset_size: 2256558450 - config_name: PC_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3982684438 num_examples: 6908554 download_size: 1512903923 dataset_size: 3982684438 - config_name: Tools_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 872273119 num_examples: 1741100 download_size: 333782939 dataset_size: 872273119 - config_name: Jewelry_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 703275869 num_examples: 1767753 download_size: 247022254 dataset_size: 703275869 - config_name: Baby_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 956952590 num_examples: 1752932 download_size: 357392893 dataset_size: 956952590 - config_name: Home_Improvement_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1329688315 num_examples: 2634781 download_size: 503339178 dataset_size: 1329688315 - config_name: Camera_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1187101912 num_examples: 1801974 download_size: 442653086 dataset_size: 1187101912 - config_name: Lawn_and_Garden_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1272255987 num_examples: 2557288 download_size: 486772662 dataset_size: 1272255987 - config_name: Office_Products_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1370685534 num_examples: 2642434 download_size: 512323500 dataset_size: 1370685534 - config_name: Electronics_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1875406721 num_examples: 3093869 download_size: 698828243 dataset_size: 1875406721 - config_name: Automotive_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1520191087 num_examples: 3514942 download_size: 582145299 dataset_size: 1520191087 - config_name: Digital_Video_Download_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1484214187 num_examples: 4057147 download_size: 506979922 dataset_size: 1484214187 - config_name: Mobile_Apps_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1627857158 num_examples: 5033376 download_size: 557959415 dataset_size: 1627857158 - config_name: Shoes_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1781283508 num_examples: 4366916 download_size: 642255314 dataset_size: 1781283508 - config_name: Toys_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2197820069 num_examples: 4864249 download_size: 838451398 dataset_size: 2197820069 - config_name: Sports_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2241349145 num_examples: 4850360 download_size: 872478735 dataset_size: 2241349145 - config_name: Kitchen_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2453735305 num_examples: 4880466 download_size: 930744854 dataset_size: 2453735305 - config_name: Beauty_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2399292506 num_examples: 5115666 download_size: 914070021 dataset_size: 2399292506 - config_name: Music_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3900138839 num_examples: 4751577 download_size: 1521994296 dataset_size: 3900138839 - config_name: Health_Personal_Care_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2679427491 num_examples: 5331449 download_size: 1011180212 dataset_size: 2679427491 - config_name: Digital_Ebook_Purchase_v1_01 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3470453859 num_examples: 5101693 download_size: 1294879074 dataset_size: 3470453859 - config_name: Home_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2796680249 num_examples: 6221559 download_size: 1081002012 dataset_size: 2796680249 - config_name: Wireless_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 4633213433 num_examples: 9002021 download_size: 1704713674 dataset_size: 4633213433 - config_name: Books_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 7197687124 num_examples: 10319090 download_size: 2740337188 dataset_size: 7197687124 - config_name: Digital_Ebook_Purchase_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 7302303804 num_examples: 12520722 download_size: 2689739299 dataset_size: 7302303804 --- # Dataset Card for "amazon_us_reviews" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://s3.amazonaws.com/amazon-reviews-pds/readme.html](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 32377.29 MB - Size of the generated dataset: 82820.19 MB - Total amount of disk used: 115197.49 MB ### Dataset Summary Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews. Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters). Each Dataset contains the following columns : marketplace - 2 letter country code of the marketplace where the review was written. customer_id - Random identifier that can be used to aggregate reviews written by a single author. review_id - The unique ID of the review. product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id. product_parent - Random identifier that can be used to aggregate reviews for the same product. product_title - Title of the product. product_category - Broad product category that can be used to group reviews (also used to group the dataset into coherent parts). star_rating - The 1-5 star rating of the review. helpful_votes - Number of helpful votes. total_votes - Number of total votes the review received. vine - Review was written as part of the Vine program. verified_purchase - The review is on a verified purchase. review_headline - The title of the review. review_body - The review text. review_date - The date the review was written. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### Apparel_v1_00 - Size of downloaded dataset files: 648.64 MB - Size of the generated dataset: 2254.36 MB - Total amount of disk used: 2903.00 MB An example of 'train' looks as follows. ``` { "customer_id": "45223824", "helpful_votes": 0, "marketplace": "US", "product_category": "Apparel", "product_id": "B016PUU3VO", "product_parent": "893588059", "product_title": "Fruit of the Loom Boys' A-Shirt (Pack of 4)", "review_body": "I ordered the same size as I ordered last time, and these shirts were much larger than the previous order. They were also about 6 inches longer. It was like they sent men's shirts instead of boys' shirts. I'll be returning these...", "review_date": "2015-01-01", "review_headline": "Sizes not correct, too big overall and WAY too long", "review_id": "R1N3Z13931J3O9", "star_rating": 2, "total_votes": 0, "verified_purchase": 1, "vine": 0 } ``` #### Automotive_v1_00 - Size of downloaded dataset files: 582.15 MB - Size of the generated dataset: 1518.88 MB - Total amount of disk used: 2101.03 MB An example of 'train' looks as follows. ``` { "customer_id": "16825098", "helpful_votes": 0, "marketplace": "US", "product_category": "Automotive", "product_id": "B000E4PCGE", "product_parent": "694793259", "product_title": "00-03 NISSAN SENTRA MIRROR RH (PASSENGER SIDE), Power, Non-Heated (2000 00 2001 01 2002 02 2003 03) NS35ER 963015M000", "review_body": "Product was as described, new and a great look. Only bad thing is that one of the screws was stripped so I couldn't tighten all three.", "review_date": "2015-08-31", "review_headline": "new and a great look. Only bad thing is that one of ...", "review_id": "R2RUIDUMDKG7P", "star_rating": 3, "total_votes": 0, "verified_purchase": 1, "vine": 0 } ``` #### Baby_v1_00 - Size of downloaded dataset files: 357.40 MB - Size of the generated dataset: 956.30 MB - Total amount of disk used: 1313.70 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "customer_id": "23299101", "helpful_votes": 2, "marketplace": "US", "product_category": "Baby", "product_id": "B00SN6F9NG", "product_parent": "3470998", "product_title": "Rhoost Nail Clipper for Baby - Ergonomically Designed and Easy to Use Baby Nail Clipper, Natural Wooden Bamboo - Baby Health and Personal Care Kits", "review_body": "\"This is an absolute MUST item to have! I was scared to death to clip my baby's nails. I tried other baby nail clippers and th...", "review_date": "2015-08-31", "review_headline": "If fits so comfortably in my hand and I feel like I have ...", "review_id": "R2DRL5NRODVQ3Z", "star_rating": 5, "total_votes": 2, "verified_purchase": 1, "vine": 0 } ``` #### Beauty_v1_00 - Size of downloaded dataset files: 914.08 MB - Size of the generated dataset: 2397.39 MB - Total amount of disk used: 3311.47 MB An example of 'train' looks as follows. ``` { "customer_id": "24655453", "helpful_votes": 1, "marketplace": "US", "product_category": "Beauty", "product_id": "B00SAQ9DZY", "product_parent": "292127037", "product_title": "12 New, High Quality, Amber 2 ml (5/8 Dram) Glass Bottles, with Orifice Reducer and Black Cap.", "review_body": "These are great for small mixtures for EO's, especially for traveling. I only gave this 4 stars because of the orifice reducer. The hole is so small it is hard to get the oil out. Just needs to be slightly bigger.", "review_date": "2015-08-31", "review_headline": "Good Product", "review_id": "R2A30ALEGLMCGN", "star_rating": 4, "total_votes": 1, "verified_purchase": 1, "vine": 0 } ``` #### Books_v1_00 - Size of downloaded dataset files: 2740.34 MB - Size of the generated dataset: 7193.86 MB - Total amount of disk used: 9934.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "customer_id": "49735028", "helpful_votes": 0, "marketplace": "US", "product_category": "Books", "product_id": "0664254969", "product_parent": "248307276", "product_title": "Presbyterian Creeds: A Guide to the Book of Confessions", "review_body": "\"The Presbyterian Book of Confessions contains multiple Creeds for use by the denomination. This guidebook helps he lay person t...", "review_date": "2015-08-31", "review_headline": "The Presbyterian Book of Confessions contains multiple Creeds for use ...", "review_id": "R2G519UREHRO8M", "star_rating": 3, "total_votes": 1, "verified_purchase": 1, "vine": 0 } ``` ### Data Fields The data fields are the same among all splits. #### Apparel_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Automotive_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Baby_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Beauty_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Books_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. ### Data Splits \| name \| train \| \|----------------\|-------:\| \|Apparel_v1_00 \| 5906333\| \|Automotive_v1_00 \| 3514942\| \|Baby_v1_00 \| 1752932\| \|Beauty_v1_00 \| 5115666\| \|Books_v1_00 \| 10319090\| \|Books_v1_01 \| 6106719\| \|Books_v1_02 \| 3105520\| \|Camera_v1_00 \| 1801974\| \|Digital_Ebook_Purchase_v1_00 \| 12520722\| \|Digital_Ebook_Purchase_v1_01 \| 5101693\| \|Digital_Music_Purchase_v1_00 \| 1688884\| \|Digital_Software_v1_00 \| 102084\| \|Digital_Video_Download_v1_00 \| 4057147\| \|Digital_Video_Games_v1_00 \| 145431\| \|Electronics_v1_00 \| 3093869\| \|Furniture_v1_00 \| 792113\| \|Gift_Card_v1_00 \| 149086\| \|Grocery_v1_00 \| 2402458\| \|Health_Personal_Care_v1_00 \| 5331449\| \|Home_Entertainment_v1_00 \| 705889\| \|Home_Improvement_v1_00 \| 2634781\| \|Home_v1_00 \| 6221559\| \|Jewelry_v1_00 \| 1767753\| \|Kitchen_v1_00 \| 4880466\| \|Lawn_and_Garden_v1_00 \| 2557288\| \|Luggage_v1_00 \| 348657\| \|Major_Appliances_v1_00 \| 96901\| \|Mobile_Apps_v1_00 \| 5033376\| \|Mobile_Electronics_v1_00 \| 104975\| \|Music_v1_00 \| 4751577\| \|Musical_Instruments_v1_00 \| 904765\| \|Office_Products_v1_00 \| 2642434\| \|Outdoors_v1_00 \| 2302401\| \|PC_v1_00 \| 6908554\| \|Personal_Care_Appliances_v1_00 \| 85981\| \|Pet_Products_v1_00 \| 2643619\| \|Shoes_v1_00 \| 4366916\| \|Software_v1_00 \| 341931\| \|Sports_v1_00 \| 4850360\| \|Tools_v1_00 \| 1741100\| \|Toys_v1_00 \| 4864249\| \|Video_DVD_v1_00 \| 5069140\| \|Video_Games_v1_00 \| 1785997\| \|Video_v1_00 \| 380604\| \|Watches_v1_00 \| 960872\| \|Wireless_v1_00 \| 9002021\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information https://s3.amazonaws.com/amazon-reviews-pds/LICENSE.txt By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the Reviews Library is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions: In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Library for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Library or its contents, including use of the Reviews Library for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Library with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Library. If you violate any of the foregoing conditions, your license to access and use the Reviews Library will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. ### Citation Information No citation information. ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
ambig_qa	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|natural_questions - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: ambigqa pretty_name: 'AmbigQA: Answering Ambiguous Open-domain Questions' dataset_info: - config_name: light features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string splits: - name: train num_bytes: 2739732 num_examples: 10036 - name: validation num_bytes: 805808 num_examples: 2002 download_size: 19700900 dataset_size: 3545540 - config_name: full features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string - name: viewed_doc_titles sequence: string - name: used_queries sequence: - name: query dtype: string - name: results sequence: - name: title dtype: string - name: snippet dtype: string - name: nq_answer sequence: string - name: nq_doc_title dtype: string splits: - name: train num_bytes: 43538733 num_examples: 10036 - name: validation num_bytes: 15383368 num_examples: 2002 download_size: 19700900 dataset_size: 58922101 --- # Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [Homepage:](https://nlp.cs.washington.edu/ambigqa/) - [Repository:](https://github.com/shmsw25/AmbigQA) - [Paper:](https://arxiv.org/pdf/2004.10645.pdf) ### Dataset Summary AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with 14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity. We provide two distributions of our new dataset AmbigNQ: a `full` version with all annotation metadata and a `light` version with only inputs and outputs. ### Supported Tasks and Leaderboards `question-answering` ### Languages English ## Dataset Structure ### Data Instances An example from the data set looks as follows: ``` {'annotations': {'answer': [[]], 'qaPairs': [{'answer': [['April 19, 1987'], ['December 17, 1989']], 'question': ['When did the Simpsons first air on television as an animated short on the Tracey Ullman Show?', 'When did the Simpsons first air as a half-hour prime time show?']}], 'type': ['multipleQAs']}, 'id': '-4469503464110108318', 'nq_answer': ['December 17 , 1989'], 'nq_doc_title': 'The Simpsons', 'question': 'When did the simpsons first air on television?', 'used_queries': {'query': ['When did the simpsons first air on television?'], 'results': [{'snippet': ['The <b>Simpsons</b> is an American animated <b>television</b> sitcom starring the animated \nSimpson family, ... Since its <b>debut</b> on December 17, 1989, the show <b>has</b> \nbroadcast 673 episodes and its 30th season started ... The <b>Simpsons first</b> season \n<b>was</b> the Fox network's <b>first TV</b> series to rank among a season's top 30 highest-\nrated shows.', 'The <b>Simpsons</b> is an American animated sitcom created by Matt Groening for the \nFox ... Since its <b>debut</b> on December 17, 1989, 674 episodes of The <b>Simpsons</b> \nhave been broadcast. ... When producer James L. Brooks <b>was</b> working on the \n<b>television</b> variety show The Tracey Ullman Show, he decided to include small \nanimated ...', '... in shorts from The Tracey Ullman Show as their <b>television debut</b> in 1987. The \n<b>Simpsons</b> shorts are a series of animated shorts that <b>aired</b> as a recurring \nsegment on Fox variety <b>television</b> series The Tracey ... The final short to <b>air was</b> "\n<b>TV Simpsons</b>", originally airing on May 14, 1989. The <b>Simpsons</b> later debuted on\n ...', 'The <b>first</b> season of the American animated <b>television</b> series The <b>Simpsons</b> \noriginally <b>aired</b> on the Fox network between December 17, 1989, and May 13, \n1990, beginning with the Christmas special "<b>Simpsons</b> Roasting on an Open Fire\n". The executive producers for the <b>first</b> production season <b>were</b> Matt Groening, ...', 'The <b>Simpsons</b> is an American animated <b>television</b> sitcom created by Matt \nGroening for the Fox ... Since its <b>debut</b> on December 17, 1989, The <b>Simpsons</b> \n<b>has</b> broadcast 674 episodes. The show holds several American <b>television</b> \nlongevity ...', 'The opening sequence of the American animated <b>television</b> series The <b>Simpsons</b> \nis among the most popular opening sequences in <b>television</b> and is accompanied \nby one of <b>television's</b> most recognizable theme songs. The <b>first</b> episode to use \nthis intro <b>was</b> the series' second episode "Bart the ... <b>was</b> the <b>first</b> episode of The \n<b>Simpsons</b> to <b>air</b> in 720p high-definition <b>television</b>, ...', '"<b>Simpsons</b> Roasting on an Open Fire", titled onscreen as "The <b>Simpsons</b> \nChristmas Special", is the premiere episode of the American animated <b>TV</b> series \nThe <b>Simpsons</b>, ... The show <b>was</b> originally intended to <b>debut</b> earlier in 1989 with "\nSome Enchanted Evening", but due to animation problems with that episode, the \nshow ...', '"Stark Raving Dad" is the <b>first</b> episode of the third season of the American \nanimated <b>television</b> series The <b>Simpsons</b>. It <b>first aired</b> on the Fox network in the \nUnited States on September 19, 1991. ... The <b>Simpsons was</b> the second highest \nrated show on Fox the week it <b>aired</b>, behind Married... with Children. "Stark \nRaving Dad," ...', 'The <b>Simpsons</b>' twentieth season <b>aired</b> on Fox from September 28, 2008 to May \n17, 2009. With this season, the show tied Gunsmoke as the longest-running \nAmerican primetime <b>television</b> series in terms of total number ... It <b>was</b> the <b>first</b>-\never episode of the show to <b>air</b> in Europe before being seen in the United States.', 'The animated <b>TV</b> show The <b>Simpsons</b> is an American English language \nanimated sitcom which ... The <b>Simpsons was</b> dubbed for the <b>first</b> time in Punjabi \nand <b>aired</b> on Geo <b>TV</b> in Pakistan. The name of the localised Punjabi version is \nTedi Sim ...'], 'title': ['History of The Simpsons', 'The Simpsons', 'The Simpsons shorts', 'The Simpsons (season 1)', 'List of The Simpsons episodes', 'The Simpsons opening sequence', 'Simpsons Roasting on an Open Fire', 'Stark Raving Dad', 'The Simpsons (season 20)', 'Non-English versions of The Simpsons']}]}, 'viewed_doc_titles': ['The Simpsons']} ``` ### Data Fields Full ``` {'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'annotations': Sequence(feature={'type': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'qaPairs': Sequence(feature={'question': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, length=-1, id=None)}, length=-1, id=None), 'viewed_doc_titles': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'used_queries': Sequence(feature={'query': Value(dtype='string', id=None), 'results': Sequence(feature={'title': Value(dtype='string', id=None), 'snippet': Value(dtype='string', id=None)}, length=-1, id=None)}, length=-1, id=None), 'nq_answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'nq_doc_title': Value(dtype='string', id=None)} ``` In the original data format `annotations` have different keys depending on the `type` field = `singleAnswer` or `multipleQAs`. But this implementation uses an empty list `[]` for the unavailable keys please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. ``` for example in train_light_dataset: for i,t in enumerate(example['annotations']['type']): if t =='singleAnswer': # use the example['annotations']['answer'][i] # example['annotations']['qaPairs'][i] - > is [] print(example['annotations']['answer'][i]) else: # use the example['annotations']['qaPairs'][i] # example['annotations']['answer'][i] - > is [] print(example['annotations']['qaPairs'][i]) ``` please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. Light version only has `id`, `question`, `annotations` fields ### Data Splits - train: 10036 - validation: 2002 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data - Wikipedia - NQ-open: ``` @article{ kwiatkowski2019natural, title={ Natural questions: a benchmark for question answering research}, author={ Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others }, journal={ Transactions of the Association for Computational Linguistics }, year={ 2019 } } ``` #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) ### Citation Information ``` @inproceedings{ min2020ambigqa, title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions }, author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke }, booktitle={ EMNLP }, year={2020} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
americas_nli	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ay - bzd - cni - gn - hch - nah - oto - qu - shp - tar license: - unknown multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended\|xnli task_categories: - text-classification task_ids: - natural-language-inference pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.' dataset_info: - config_name: aym features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 117538 num_examples: 743 - name: test num_bytes: 115259 num_examples: 750 download_size: 2256093 dataset_size: 232797 - config_name: bzd features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 143362 num_examples: 743 - name: test num_bytes: 127684 num_examples: 750 download_size: 2256093 dataset_size: 271046 - config_name: cni features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 113264 num_examples: 658 - name: test num_bytes: 116292 num_examples: 750 download_size: 2256093 dataset_size: 229556 - config_name: gn features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 115143 num_examples: 743 - name: test num_bytes: 101956 num_examples: 750 download_size: 2256093 dataset_size: 217099 - config_name: hch features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 127974 num_examples: 743 - name: test num_bytes: 120865 num_examples: 750 download_size: 2256093 dataset_size: 248839 - config_name: nah features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 50749 num_examples: 376 - name: test num_bytes: 102961 num_examples: 738 download_size: 2256093 dataset_size: 153710 - config_name: oto features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 27018 num_examples: 222 - name: test num_bytes: 119658 num_examples: 748 download_size: 2256093 dataset_size: 146676 - config_name: quy features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 125644 num_examples: 743 - name: test num_bytes: 112758 num_examples: 750 download_size: 2256093 dataset_size: 238402 - config_name: shp features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 124508 num_examples: 743 - name: test num_bytes: 118942 num_examples: 750 download_size: 2256093 dataset_size: 243450 - config_name: tar features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 139504 num_examples: 743 - name: test num_bytes: 122632 num_examples: 750 download_size: 2256093 dataset_size: 262136 - config_name: all_languages features: - name: language dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 1129092 num_examples: 6457 - name: test num_bytes: 1210591 num_examples: 7486 download_size: 2256093 dataset_size: 2339683 --- # Dataset Card for AmericasNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [Needs More Information] - Repository: https://github.com/nala-cub/AmericasNLI - Paper: https://arxiv.org/abs/2104.08726 - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages - aym - bzd - cni - gn - hch - nah - oto - quy - shp - tar ## Dataset Structure ### Data Instances #### all_languages An example of the test split looks as follows: ``` {'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p arlxapxti.', 'label': 2} ``` #### aym An example of the test split looks as follows: ``` {'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label ': 2} ``` #### bzd An example of the test split looks as follows: ``` {'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2} ``` #### cni An example of the test split looks as follows: ``` {'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2} ``` #### gn An example of the test split looks as follows: ``` {'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2} ``` #### hch An example of the test split looks as follows: ``` {'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2} ``` #### nah An example of the test split looks as follows: ``` {'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2} ``` #### oto An example of the test split looks as follows: ``` {'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2} ``` #### quy An example of the test split looks as follows: ``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani .', 'label': 2} ``` #### shp An example of the test split looks as follows: ``` {'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2} ``` #### tar An example of the test split looks as follows: ``` {'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2} ``` ### Data Fields #### all_languages - language: a multilingual string variable, with languages including ar, bg, de, el, en. - premise: a multilingual string variable, with languages including ar, bg, de, el, en. - hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### aym - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### bzd - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### cni - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### hch - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### nah - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### oto - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### quy - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### shp - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### tar - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). ### Data Splits \| Language \| ISO \| Family \| Dev \| Test \| \|-------------------\|-----\|:-------------\|-----:\|-----:\| \| all_languages \| -- \| -- \| 6457 \| 7486 \| \| Aymara \| aym \| Aymaran \| 743 \| 750 \| \| Ashaninka \| cni \| Arawak \| 658 \| 750 \| \| Bribri \| bzd \| Chibchan \| 743 \| 750 \| \| Guarani \| gn \| Tupi-Guarani \| 743 \| 750 \| \| Nahuatl \| nah \| Uto-Aztecan \| 376 \| 738 \| \| Otomi \| oto \| Oto-Manguean \| 222 \| 748 \| \| Quechua \| quy \| Quechuan \| 743 \| 750 \| \| Raramuri \| tar \| Uto-Aztecan \| 743 \| 750 \| \| Shipibo-Konibo \| shp \| Panoan \| 743 \| 750 \| \| Wixarika \| hch \| Uto-Aztecan \| 743 \| 750 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data The authors translate from the Spanish subset of XNLI. > AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version. As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process The dataset comprises expert translations from Spanish XNLI. > Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.” As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @article{DBLP:journals/corr/abs-2104-08726, author = {Abteen Ebrahimi and Manuel Mager and Arturo Oncevay and Vishrav Chaudhary and Luis Chiruzzo and Angela Fan and John Ortega and Ricardo Ramos and Annette Rios and Ivan Vladimir and Gustavo A. Gim{\'{e}}nez{-}Lugo and Elisabeth Mager and Graham Neubig and Alexis Palmer and Rolando A. Coto Solano and Ngoc Thang Vu and Katharina Kann}, title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages}, journal = {CoRR}, volume = {abs/2104.08726}, year = {2021}, url = {https://arxiv.org/abs/2104.08726}, eprinttype = {arXiv}, eprint = {2104.08726}, timestamp = {Mon, 26 Apr 2021 17:25:10 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset.
ami	--- pretty_name: AMI Corpus annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] dataset_info: - config_name: microphone-single features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 42013753 num_examples: 134 - name: validation num_bytes: 5110497 num_examples: 18 - name: test num_bytes: 4821283 num_examples: 16 download_size: 11387715153 dataset_size: 51945533 - config_name: microphone-multi features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file-1-1 dtype: string - name: file-1-2 dtype: string - name: file-1-3 dtype: string - name: file-1-4 dtype: string - name: file-1-5 dtype: string - name: file-1-6 dtype: string - name: file-1-7 dtype: string - name: file-1-8 dtype: string splits: - name: train num_bytes: 42126341 num_examples: 134 - name: validation num_bytes: 5125645 num_examples: 18 - name: test num_bytes: 4834751 num_examples: 16 download_size: 90941506169 dataset_size: 52086737 - config_name: headset-single features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 42491091 num_examples: 136 - name: validation num_bytes: 5110497 num_examples: 18 - name: test num_bytes: 4821283 num_examples: 16 download_size: 11505070978 dataset_size: 52422871 - config_name: headset-multi features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file-0 dtype: string - name: file-1 dtype: string - name: file-2 dtype: string - name: file-3 dtype: string splits: - name: train num_bytes: 42540063 num_examples: 136 - name: validation num_bytes: 5116989 num_examples: 18 - name: test num_bytes: 4827055 num_examples: 16 download_size: 45951596391 dataset_size: 52484107 --- # Dataset Card for AMI Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> This legacy dataset is outdated. Please, use <a href="https://huggingface.co/datasets/edinburghcstr/ami"> edinburghcstr/ami </a> instead.</p> </div> ## Dataset Description - Homepage: [AMI corpus](https://groups.inf.ed.ac.uk/ami/corpus/) - Repository: [Needs More Information] - Paper: [Needs More Information] - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers. ### Dataset Preprocessing Individual samples of the AMI dataset contain very large audio files (between 10 and 60 minutes). Such lengths are unfeasible for most speech recognition models. In the following, we show how the dataset can effectively be chunked into multiple segments as defined by the dataset creators. The following function cuts the long audio files into the defined segment lengths: ```python import librosa import math from datasets import load_dataset SAMPLE_RATE = 16_000 def chunk_audio(batch): new_batch = { "audio": [], "words": [], "speaker": [], "lengths": [], "word_start_times": [], "segment_start_times": [], } audio, _ = librosa.load(batch["file"][0], sr=SAMPLE_RATE) word_idx = 0 num_words = len(batch["words"][0]) for segment_idx in range(len(batch["segment_start_times"][0])): words = [] word_start_times = [] start_time = batch["segment_start_times"][0][segment_idx] end_time = batch["segment_end_times"][0][segment_idx] # go back and forth with word_idx since segments overlap with each other while (word_idx > 1) and (start_time < batch["word_end_times"][0][word_idx - 1]): word_idx -= 1 while word_idx < num_words and (start_time > batch["word_start_times"][0][word_idx]): word_idx += 1 new_batch["audio"].append(audio[int(start_time * SAMPLE_RATE): int(end_time * SAMPLE_RATE)]) while word_idx < num_words and batch["word_start_times"][0][word_idx] < end_time: words.append(batch["words"][0][word_idx]) word_start_times.append(batch["word_start_times"][0][word_idx]) word_idx += 1 new_batch["lengths"].append(end_time - start_time) new_batch["words"].append(words) new_batch["speaker"].append(batch["segment_speakers"][0][segment_idx]) new_batch["word_start_times"].append(word_start_times) new_batch["segment_start_times"].append(batch["segment_start_times"][0][segment_idx]) return new_batch ami = load_dataset("ami", "headset-single") ami = ami.map(chunk_audio, batched=True, batch_size=1, remove_columns=ami["train"].column_names) ``` The segmented audio files can still be as long as a minute. To further chunk the data into shorter audio chunks, you can use the following script. ```python MAX_LENGTH_IN_SECONDS = 20.0 def chunk_into_max_n_seconds(batch): new_batch = { "audio": [], "text": [], } sample_length = batch["lengths"][0] segment_start = batch["segment_start_times"][0] if sample_length > MAX_LENGTH_IN_SECONDS: num_chunks_per_sample = math.ceil(sample_length / MAX_LENGTH_IN_SECONDS) avg_chunk_length = sample_length / num_chunks_per_sample num_words = len(batch["words"][0]) # start chunking by times start_word_idx = end_word_idx = 0 chunk_start_time = 0 for n in range(num_chunks_per_sample): while (end_word_idx < num_words - 1) and (batch["word_start_times"][0][end_word_idx] < segment_start + (n + 1) * avg_chunk_length): end_word_idx += 1 chunk_end_time = int((batch["word_start_times"][0][end_word_idx] - segment_start) * SAMPLE_RATE) new_batch["audio"].append(batch["audio"][0][chunk_start_time: chunk_end_time]) new_batch["text"].append(" ".join(batch["words"][0][start_word_idx: end_word_idx])) chunk_start_time = chunk_end_time start_word_idx = end_word_idx else: new_batch["audio"].append(batch["audio"][0]) new_batch["text"].append(" ".join(batch["words"][0])) return new_batch ami = ami.map(chunk_into_max_n_seconds, batched=True, batch_size=1, remove_columns=ami["train"].column_names, num_proc=64) ``` A segmented and chunked dataset of the config `"headset-single"`can be found [here](https://huggingface.co/datasets/ami-wav2vec2/ami_single_headset_segmented_and_chunked). ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task does not have an active leaderboard at the moment. - `speaker-diarization`: The dataset can be used to train model for Speaker Diarization (SD). The model is presented with an audio file and asked to predict which speaker spoke at what time. ### Languages The audio is in English. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file (or files in the case of the multi-headset or multi-microphone dataset), called `file` and its transcription as a list of words, called `words`. Additional information about the `speakers`, the `word_start_time`, `word_end_time`, `segment_start_time`, `segment_end_time` is given. In addition and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'word_ids': ["ES2004a.D.words1", "ES2004a.D.words2", ...], 'word_start_times': [0.3700000047683716, 0.949999988079071, ...], 'word_end_times': [0.949999988079071, 1.5299999713897705, ...], 'word_speakers': ['A', 'A', ...], 'segment_ids': ["ES2004a.sync.1", "ES2004a.sync.2", ...] 'segment_start_times': [10.944000244140625, 17.618999481201172, ...], 'segment_end_times': [17.618999481201172, 18.722000122070312, ...], 'segment_speakers': ['A', 'B', ...], 'words', ["hmm", "hmm", ...] 'channels': [0, 0, ..], 'file': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f" 'audio': {'path': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f", 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, } ``` ### Data Fields - word_ids: a list of the ids of the words - word_start_times: a list of the start times of when the words were spoken in seconds - word_end_times: a list of the end times of when the words were spoken in seconds - word_speakers: a list of speakers one for each word - segment_ids: a list of the ids of the segments - segment_start_times: a list of the start times of when the segments start - segment_end_times: a list of the start times of when the segments ends - segment_speakers: a list of speakers one for each segment - words: a list of all the spoken words - channels: a list of all channels that were used for each word - file: a path to the audio file - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. ### Data Splits The dataset consists of several configurations, each one having train/validation/test splits: - headset-single: Close talking audio of single headset. This configuration only includes audio belonging to the headset of the person currently speaking. - headset-multi (4 channels): Close talking audio of four individual headset. This configuration includes audio belonging to four individual headsets. For each annotation there are 4 audio files 0, 1, 2, 3. - microphone-single: Far field audio of single microphone. This configuration only includes audio belonging the first microphone, i.e. 1-1, of the microphone array. - microphone-multi (8 channels): Far field audio of microphone array. This configuration includes audio of the first microphone array 1-1, 1-2, ..., 1-8. In general, `headset-single` and `headset-multi` include significantly less noise than `microphone-single` and `microphone-multi`. \| \| Train \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| headset-single \| 136 (80h) \| 18 (9h) \| 16 (9h) \| \| headset-multi (4 channels) \| 136 (320h) \| 18 (36h) \| 16 (36h) \| \| microphone-single \| 136 (80h) \| 18 (9h) \| 16 (9h) \| \| microphone-multi (8 channels) \| 136 (640h) \| 18 (72h) \| 16 (72h) \| Note that each sample contains between 10 and 60 minutes of audio data which makes it impractical for direct transcription. One should make use of the segment and word start times and end times to chunk the samples into smaller samples of manageable size. ## Dataset Creation All information about the dataset creation can be found [here](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml) ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information CC BY 4.0 ### Citation Information #### TODO ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) and [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. #### TODO
amttl	--- annotations_creators: - crowdsourced language_creators: - found language: - zh license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - parsing pretty_name: AMTTL dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: class_label: names: '0': B '1': I '2': E '3': S config_name: amttl splits: - name: train num_bytes: 1132212 num_examples: 3063 - name: validation num_bytes: 324374 num_examples: 822 - name: test num_bytes: 328525 num_examples: 908 download_size: 685534 dataset_size: 1785111 --- # Dataset Card for AMTTL ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - Repository: [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - Paper: [Aclweb](http://aclweb.org/anthology/C18-1307) - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @inproceedings{xing2018adaptive, title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text}, author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian}, booktitle={Proceedings of the 27th International Conference on Computational Linguistics}, pages={3619--3630}, year={2018} } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.
anli	--- annotations_creators: - crowdsourced - machine-generated language_creators: - found language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original - extended\|hotpot_qa task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification paperswithcode_id: anli pretty_name: Adversarial NLI dataset_info: features: - name: uid dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: reason dtype: string config_name: plain_text splits: - name: train_r1 num_bytes: 8006920 num_examples: 16946 - name: dev_r1 num_bytes: 573444 num_examples: 1000 - name: test_r1 num_bytes: 574933 num_examples: 1000 - name: train_r2 num_bytes: 20801661 num_examples: 45460 - name: dev_r2 num_bytes: 556082 num_examples: 1000 - name: test_r2 num_bytes: 572655 num_examples: 1000 - name: train_r3 num_bytes: 44720895 num_examples: 100459 - name: dev_r3 num_bytes: 663164 num_examples: 1200 - name: test_r3 num_bytes: 657602 num_examples: 1200 download_size: 18621352 dataset_size: 77127356 --- # Dataset Card for "anli" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: [https://github.com/facebookresearch/anli/](https://github.com/facebookresearch/anli/) - Paper: [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://arxiv.org/abs/1910.14599) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 18.62 MB - Size of the generated dataset: 77.12 MB - Total amount of disk used: 95.75 MB ### Dataset Summary The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 18.62 MB - Size of the generated dataset: 77.12 MB - Total amount of disk used: 95.75 MB An example of 'train_r2' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.", "label": 0, "premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...", "reason": "", "uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `uid`: a `string` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `reason`: a `string` feature. ### Data Splits \| name \|train_r1\|dev_r1\|train_r2\|dev_r2\|train_r3\|dev_r3\|test_r1\|test_r2\|test_r3\| \|----------\|-------:\|-----:\|-------:\|-----:\|-------:\|-----:\|------:\|------:\|------:\| \|plain_text\| 16946\| 1000\| 45460\| 1000\| 100459\| 1200\| 1000\| 1000\| 1200\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [cc-4 Attribution-NonCommercial](https://github.com/facebookresearch/anli/blob/main/LICENSE) ### Citation Information ``` @InProceedings{nie2019adversarial, title={Adversarial NLI: A New Benchmark for Natural Language Understanding}, author={Nie, Yixin and Williams, Adina and Dinan, Emily and Bansal, Mohit and Weston, Jason and Kiela, Douwe}, booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", year = "2020", publisher = "Association for Computational Linguistics", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@easonnie](https://github.com/easonnie), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
app_reviews	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-scoring paperswithcode_id: null pretty_name: AppReviews dataset_info: features: - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int8 splits: - name: train num_bytes: 32769079 num_examples: 288065 download_size: 42592679 dataset_size: 32769079 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Home Page](https://github.com/sealuzh/user_quality) - Repository: [Repo Link](https://github.com/sealuzh/user_quality) - Paper: [Link](https://giograno.me/assets/pdf/workshop/wama17.pdf) - Leaderboard: - Point of Contact:** [Darshan Gandhi]([email protected]) ### Dataset Summary It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches) ### Supported Tasks and Leaderboards The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective. ### Languages The dataset is a monolingual dataset which has the messages English. ## Dataset Structure ### Data Instances The dataset consists of a message in English. {'package_name': 'com.mantz_it.rfanalyzer', 'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.", 'date': 'October 12 2016', 'star': 4} ### Data Fields * package_name : Name of the Software Application Package * review : Message of the user * date : date when the user posted the review * star : rating provied by the user for the application ### Data Splits There is training data, with a total of : 288065 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues. ### Discussion of Biases The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio) ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{Zurich Open Repository and Archive:dataset, title = {Software Applications User Reviews}, authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo; Panichella, Sebastiano}, year={2017} } ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.
aqua_rat	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: aqua-rat pretty_name: Algebra Question Answering with Rationales dataset_info: - config_name: raw features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 42333259 num_examples: 97467 - name: test num_bytes: 116779 num_examples: 254 - name: validation num_bytes: 118636 num_examples: 254 download_size: 47833135 dataset_size: 42568674 - config_name: tokenized features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 46493843 num_examples: 97467 - name: test num_bytes: 126283 num_examples: 254 - name: validation num_bytes: 128873 num_examples: 254 download_size: 52003894 dataset_size: 46748999 --- # Dataset Card for AQUA-RAT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA) - Repository: [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA) - Paper: [https://arxiv.org/pdf/1705.04146.pdf](https://arxiv.org/pdf/1705.04146.pdf) ### Dataset Summary A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question. ### Supported Tasks and Leaderboards ### Languages en ## Dataset Structure ### Data Instances ``` { "question": "A grocery sells a bag of ice for $1.25, and makes 20% profit. If it sells 500 bags of ice, how much total profit does it make?", "options": ["A)125", "B)150", "C)225", "D)250", "E)275"], "rationale": "Profit per bag = 1.25 * 0.20 = 0.25\nTotal profit = 500 * 0.25 = 125\nAnswer is A.", "correct": "A" } ``` ### Data Fields - `question` : (str) A natural language definition of the problem to solve - `options` : (list(str)) 5 possible options (A, B, C, D and E), among which one is correct - `rationale` : (str) A natural language description of the solution to the problem - `correct` : (str) The correct option ### Data Splits \| \| Train \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| Examples \| 97467 \| 254 \| 254 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ### Citation Information ``` @article{ling2017program, title={Program induction by rationale generation: Learning to solve and explain algebraic word problems}, author={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil}, journal={ACL}, year={2017} } ``` ### Contributions Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset.
aquamuse	--- annotations_creators: - crowdsourced - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|natural_questions - extended\|other-Common-Crawl - original task_categories: - other - question-answering - text2text-generation task_ids: - abstractive-qa - extractive-qa paperswithcode_id: aquamuse pretty_name: AQuaMuSe tags: - query-based-multi-document-summarization dataset_info: - config_name: abstractive features: - name: query dtype: string - name: input_urls sequence: string - name: target dtype: string splits: - name: train num_bytes: 6434909 num_examples: 6253 - name: test num_bytes: 843181 num_examples: 811 - name: validation num_bytes: 689109 num_examples: 661 download_size: 7755161 dataset_size: 7967199 - config_name: extractive features: - name: query dtype: string - name: input_urls sequence: string - name: target dtype: string splits: - name: train num_bytes: 6434909 num_examples: 6253 - name: test num_bytes: 843181 num_examples: 811 - name: validation num_bytes: 689109 num_examples: 661 download_size: 7755161 dataset_size: 7967199 --- # Dataset Card for AQuaMuSe ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/google-research-datasets/aquamuse - Repository: https://github.com/google-research-datasets/aquamuse - Paper: https://arxiv.org/pdf/2010.12694.pdf - Leaderboard: - Point of Contact: ### Dataset Summary AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl) This dataset contains versions of automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf). ### Supported Tasks and Leaderboards - Abstractive and Extractive query-based multi-document summarization - Question Answering ### Languages en : English ## Dataset Structure ### Data Instances - `input_urls`: a `list` of `string` features. - `query`: a `string` feature. - `target`: a `string` feature Example: ``` { 'input_urls': ['https://boxofficebuz.com/person/19653-charles-michael-davis'], 'query': 'who is the actor that plays marcel on the originals', 'target': "In February 2013, it was announced that Davis was cast in a lead role on The CW's new show The Originals, a spinoff of The Vampire Diaries, centered on the Original Family as they move to New Orleans, where Davis' character (a vampire named Marcel) currently rules." } ``` ### Data Fields - `input_urls`: a `list` of `string` features. - List of URLs to input documents pointing to [Common Crawl](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available) to be summarized. - Dependencies: Documents URLs references the [Common Crawl June 2017 Archive](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available). - `query`: a `string` feature. - Input query to be used as summarization context. This is derived from [Natural Questions](https://ai.google.com/research/NaturalQuestions/) user queries. - `target`: a `string` feature - Summarization target, derived from [Natural Questions](https://ai.google.com/research/NaturalQuestions/) long answers. ### Data Splits - This dataset has two high-level configurations `abstractive` and `extractive` - Each configuration has the data splits of `train`, `dev` and `test` - The original format of the data was in [TFrecords](https://www.tensorflow.org/tutorials/load_data/tfrecord), which has been parsed to the format as specified in [Data Instances](#data-instances) ## Dataset Creation ### Curation Rationale The dataset is automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf). ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset curator is [sayalikulkarni](https://github.com/google-research-datasets/aquamuse/commits?author=sayalikulkarni), who is the contributor for the official GitHub repository for this dataset and also one of the authors of this dataset’s paper. As the account handles of other authors are not available currently who were also part of the curation of this dataset, the authors of the paper are mentioned here as follows, Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, and Eugene Ie. ### Licensing Information [More Information Needed] ### Citation Information @misc{kulkarni2020aquamuse, title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset.
ar_cov19	--- annotations_creators: - no-annotation language_creators: - found language: - ar multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: arcov-19 pretty_name: ArCOV19 tags: - data-mining dataset_info: features: - name: tweetID dtype: int64 config_name: ar_cov19 splits: - name: train num_bytes: 8724728 num_examples: 1090591 download_size: 54902390 dataset_size: 8724728 --- # Dataset Card for ArCOV19 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: https://gitlab.com/bigirqu/ArCOV-19 - Paper: [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://arxiv.org/abs/2004.05861) - Leaderboard: [More Information Needed] - Point of Contact: [Fatima Haouari](mailto:[email protected]) ### Dataset Summary ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes over 1M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and-liked). The propagation networks include both retweets and conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world. In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Arabic ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields tweet_id: the Twitter assigned ID for the tweet object. ### Data Splits [More Information Needed] ## Dataset Creation The dataset collection approach is presented in the following paper: [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://arxiv.org/abs/2004.05861) ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations No annotation was provided with the dataset. #### Annotation process No annotation was provided with the dataset. #### Who are the annotators? No annotation was provided with the dataset. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Team: [bigIR](https://sites.google.com/view/bigir) from Qatar University ([@bigIR_group](https://twitter.com/bigIR_group)) - [Fatima Haouari](mailto:[email protected]) - [Maram Hasanain](mailto:[email protected]) - [Reem Suwaileh](mailto:[email protected]) - [Dr. Tamer Elsayed](mailto:[email protected]) ### Licensing Information [More Information Needed] ### Citation Information @article{haouari2020arcov19, title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks}, author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed}, journal={arXiv preprint arXiv:2004.05861}, year={2020} } ### Contributions Thanks to [@Fatima-Haouari](https://github.com/Fatima-Haouari) for adding this dataset.
ar_res_reviews	--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: ArRestReviews dataset_info: features: - name: polarity dtype: class_label: names: '0': negative '1': positive - name: text dtype: string - name: restaurant_id dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 3617097 num_examples: 8364 download_size: 3503230 dataset_size: 3617097 --- # Dataset Card for ArRestReviews ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces) - Repository: [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces) - Paper: [ Building Large Arabic Multi-domain Resources for Sentiment Analysis](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces/blob/master/Paper%20-%20Building%20Large%20Arabic%20Multi-domain%20Resources%20for%20Sentiment%20Analysis.pdf) - Point of Contact: [hady elsahar]([email protected]) ### Dataset Summary Dataset of 8364 restaurant reviews from qaym.com in Arabic for sentiment analysis ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A typical data point comprises of the following: - "polarity": which is a string value of either 0 or 1 indicating the sentiment around the review - "text": is the review plain text of a restaurant in Arabic - "restaurant_id": the restaurant ID on the website - "user_id": the user ID on the website example: ``` { 'polarity': 0, # negative 'restaurant_id': '1412', 'text': 'عادي جدا مامن زود', 'user_id': '21294' } ``` ### Data Fields - "polarity": is a string value of either 0 or 1 indicating the sentiment around the review - "text": is the review plain text of a restaurant in Arabic - "restaurant_id": the restaurant ID on the website (string) - "user_id": the user ID on the website (string) ### Data Splits The dataset is not split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Contains 8364 restaurant reviews from qaym.com #### Who are the source language producers? From tweeter. ### Annotations The polarity field provides a label of 1 or -1 pertaining to the sentiment of the review #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Discussion of Social Impact and Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{10.1007/978-3-319-18117-2_2, author="ElSahar, Hady and El-Beltagy, Samhaa R.", editor="Gelbukh, Alexander", title="Building Large Arabic Multi-domain Resources for Sentiment Analysis", booktitle="Computational Linguistics and Intelligent Text Processing", year="2015", publisher="Springer International Publishing", address="Cham", pages="23--34", isbn="978-3-319-18117-2" } ### Contributions Thanks to [@abdulelahsm](https://github.com/abdulelahsm) for adding this dataset.
ar_sarcasm	--- annotations_creators: - no-annotation language_creators: - found language: - ar license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-semeval_2017 - extended\|other-astd task_categories: - text-classification task_ids: - sentiment-classification pretty_name: ArSarcasm tags: - sarcasm-detection dataset_info: features: - name: dialect dtype: class_label: names: '0': egypt '1': gulf '2': levant '3': magreb '4': msa - name: sarcasm dtype: class_label: names: '0': non-sarcastic '1': sarcastic - name: sentiment dtype: class_label: names: '0': negative '1': neutral '2': positive - name: original_sentiment dtype: class_label: names: '0': negative '1': neutral '2': positive - name: tweet dtype: string - name: source dtype: string splits: - name: train num_bytes: 1829167 num_examples: 8437 - name: test num_bytes: 458218 num_examples: 2110 download_size: 750717 dataset_size: 2287385 --- # Dataset Card for ArSarcasm ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [GitHub](https://github.com/iabufarha/ArSarcasm) - Paper: https://www.aclweb.org/anthology/2020.osact-1.5/ ### Dataset Summary ArSarcasm is a new Arabic sarcasm detection dataset. The dataset was created using previously available Arabic sentiment analysis datasets ([SemEval 2017](https://www.aclweb.org/anthology/S17-2088.pdf) and [ASTD](https://www.aclweb.org/anthology/D15-1299.pdf)) and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic. For more details, please check the paper [From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://www.aclweb.org/anthology/2020.osact-1.5/) ### Supported Tasks and Leaderboards You can get more information about an Arabic sarcasm tasks and leaderboard [here](https://sites.google.com/view/ar-sarcasm-sentiment-detection/). ### Languages Arabic (multiple dialects) ## Dataset Structure ### Data Instances ```javascript {'dialect': 1, 'original_sentiment': 0, 'sarcasm': 0, 'sentiment': 0, 'source': 'semeval', 'tweet': 'نصيحه ما عمرك اتنزل لعبة سوبر ماريو مش زي ما كنّا متوقعين الله يرحم ايامات السيقا والفاميلي #SuperMarioRun'} ``` ### Data Fields - tweet: the original tweet text - sarcasm: 0 for non-sarcastic, 1 for sarcastic - sentiment: 0 for negative, 1 for neutral, 2 for positive - original_sentiment: 0 for negative, 1 for neutral, 2 for positive - source: the original source of tweet: SemEval or ASTD - dialect: 0 for Egypt, 1 for Gulf, 2 for Levant, 3 for Magreb, 4 for Modern Standard Arabic (MSA) ### Data Splits The training set contains 8,437 tweets, while the test set contains 2,110 tweets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them. #### Who are the source language producers? SemEval 2017 and ASTD ### Annotations #### Annotation process For the annotation process, we used Figure-Eight crowdsourcing platform. Our main objective was to annotate the data for sarcasm detection, but due to the challenges imposed by dialectal variations, we decided to add the annotation for dialects. We also include a new annotation for sentiment labels in order to have a glimpse of the variability and subjectivity between different annotators. Thus, the annotators were asked to provide three labels for each tweet as the following: - Sarcasm: sarcastic or non-sarcastic. - Sentiment: positive, negative or neutral. - Dialect: Egyptian, Gulf, Levantine, Maghrebi or Modern Standard Arabic (MSA). #### Who are the annotators? Figure-Eight crowdsourcing platform ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators - Ibrahim Abu-Farha - Walid Magdy ### Licensing Information MIT ### Citation Information ``` @inproceedings{abu-farha-magdy-2020-arabic, title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset", author = "Abu Farha, Ibrahim and Magdy, Walid", booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resource Association", url = "https://www.aclweb.org/anthology/2020.osact-1.5", pages = "32--39", language = "English", ISBN = "979-10-95546-51-1", } ``` ### Contributions Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset.
arabic_billion_words	--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Arabic Billion Words configs: - Alittihad - Almasryalyoum - Almustaqbal - Alqabas - Echoroukonline - Ryiadh - Sabanews - SaudiYoum - Techreen - Youm7 dataset_info: - config_name: Alittihad features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1601790302 num_examples: 349342 download_size: 348259999 dataset_size: 1601790302 - config_name: Almasryalyoum features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1056197870 num_examples: 291723 download_size: 242604438 dataset_size: 1056197870 - config_name: Almustaqbal features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1545659336 num_examples: 446873 download_size: 350826797 dataset_size: 1545659336 - config_name: Alqabas features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 2631729746 num_examples: 817274 download_size: 595274646 dataset_size: 2631729746 - config_name: Echoroukonline features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 464386206 num_examples: 139732 download_size: 108184378 dataset_size: 464386206 - config_name: Ryiadh features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 3101294859 num_examples: 858188 download_size: 691264971 dataset_size: 3101294859 - config_name: Sabanews features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 198019614 num_examples: 92149 download_size: 38214558 dataset_size: 198019614 - config_name: SaudiYoum features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 2723291416 num_examples: 888068 download_size: 605537923 dataset_size: 2723291416 - config_name: Techreen features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1103458209 num_examples: 314597 download_size: 252976781 dataset_size: 1103458209 - config_name: Youm7 features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 3004689464 num_examples: 1172136 download_size: 617708074 dataset_size: 3004689464 --- # Dataset Card for Arabic Billion Words Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus - Repository: - Paper: https://arxiv.org/pdf/1611.04033 - Leaderboard: - Point of Contact:[Ibrahim Abu El-Khair]([email protected]) ### Dataset Summary Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles. It contains over a billion and a half words in total, out of which, there are about three million unique words. The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two mark-up languages, namely: SGML, and XML. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Arabic ## Dataset Structure ### Data Instances This is an example of the "Almasryalyoum" configuration subset: ```python { "url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300", "head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد", "date": "19/5/2007", "text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره. وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري. يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري. وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""", } ``` ### Data Fields The data fields are: - "url": string, original url of the article, - "head_line": string, headline of the article, - "date": string, date of the article, - "text": string, text content of the article, ### Data Splits There is only one "training" split for all configuration subsets, containing the following number of examples: \| \| Number of examples \| \|:---------------\|-------------------:\| \| Alittihad \| 349342 \| \| Almasryalyoum \| 291723 \| \| Almustaqbal \| 446873 \| \| Alqabas \| 817274 \| \| Echoroukonline \| 139732 \| \| Ryiadh \| 858188 \| \| Sabanews \| 92149 \| \| SaudiYoum \| 888068 \| \| Techreen \| 314597 \| \| Youm7 \| 1172136 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @article{el20161, title={1.5 billion words arabic corpus}, author={El-Khair, Ibrahim Abu}, journal={arXiv preprint arXiv:1611.04033}, year={2016} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
arabic_pos_dialect	--- annotations_creators: - expert-generated language_creators: - found language: - ar license: - apache-2.0 multilinguality: - multilingual size_categories: - n<1K source_datasets: - extended task_categories: - token-classification task_ids: - part-of-speech paperswithcode_id: null pretty_name: Arabic POS Dialect dataset_info: - config_name: egy features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 269657 num_examples: 350 download_size: 1043655 dataset_size: 269657 - config_name: lev features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 263130 num_examples: 350 download_size: 1043655 dataset_size: 263130 - config_name: glf features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 239911 num_examples: 350 download_size: 1043655 dataset_size: 239911 - config_name: mgr features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 245745 num_examples: 350 download_size: 1043655 dataset_size: 245745 --- # Dataset Card for Arabic POS Dialect ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://alt.qcri.org/resources/da_resources/ - Repository: https://github.com/qcri/dialectal_arabic_resources - Paper: http://www.lrec-conf.org/proceedings/lrec2018/pdf/562.pdf - Contacts: - Ahmed Abdelali < aabdelali @ hbku dot edu dot qa > - Kareem Darwish < kdarwish @ hbku dot edu dot qa > - Hamdy Mubarak < hmubarak @ hbku dot edu dot qa > ### Dataset Summary This dataset was created to support part of speech (POS) tagging in dialects of Arabic. It contains sets of 350 manually segmented and POS tagged tweets for each of four dialects: Egyptian, Levantine, Gulf, and Maghrebi. ### Supported Tasks and Leaderboards The dataset can be used to train a model for Arabic token segmentation and part of speech tagging in Arabic dialects. Success on this task is typically measured by achieving a high accuracy over a held out dataset. Darwish et al. (2018) train a CRF model across all four dialects and achieve an average accuracy of 89.3%. ### Languages The BCP-47 code is ar-Arab. The dataset consists of four dialects of Arabic, Egyptian (EGY), Levantine (LEV), Gulf (GLF), and Maghrebi (MGR), written in Arabic script. ## Dataset Structure ### Data Instances Below is a partial example from the Egyptian set: ``` - `Fold`: 4 - `SubFold`: A - `Word`: [ليه, لما, تحب, حد, من, قلبك, ...] - `Segmentation`: [ليه, لما, تحب, حد, من, قلب+ك, ...] - `POS`: [PART, PART, V, NOUN, PREP, NOUN+PRON, ...] ``` ### Data Fields The `fold` and the `subfold` fields refer to the crossfold validation splits used by Darwish et al., which can be generated using this [script](https://github.com/qcri/dialectal_arabic_resources/blob/master/generate_splits.sh). - `fold`: An int32 indicating which fold the instance was in for the crossfold validation - `subfold`: A string, either 'A' or 'B', indicating which subfold the instance was in for the crossfold validation - `words`: A sequence of strings of the unsegmented token - `segments`: A sequence of strings consisting of the segments of the word separated by '+' if there is more than one segment - `pos_tags`: A sequence of strings of the part of speech tags of the segments separated by '+' if there is more than one segment The POS tags consist of a set developed by [Darwish et al. (2017)](https://www.aclweb.org/anthology/W17-1316.pdf) for Modern Standard Arabic (MSA) plus an additional 6 tags (2 dialect-specific tags and 4 tweet-specific tags). \| Tag \| Purpose \| Description \| \| ----- \| ------ \| ----- \| \| ADV \| MSA \| Adverb \| \| ADJ \| MSA \| Adjective \| \| CONJ \| MSA \| Conjunction \| \| DET \| MSA \| Determiner \| \| NOUN \| MSA \| Noun \| \| NSUFF \| MSA \| Noun suffix \| \| NUM \| MSA \| Number \| \| PART \| MSA \| Particle \| \| PREP \| MSA \| Preposition \| \| PRON \| MSA \| Pronoun \| \| PUNC \| MSA \| Preposition \| \| V \| MSA \| Verb \| \| ABBREV \| MSA \| Abbreviation \| \| CASE \| MSA \| Alef of tanween fatha \| \| JUS \| MSA \| Jussification attached to verbs \| \| VSUFF \| MSA \| Verb Suffix \| \| FOREIGN \| MSA \| Non-Arabic as well as non-MSA words \| \| FUR_PART \| MSA \| Future particle "s" prefix and "swf" \| \| PROG_PART \| Dialect \| Progressive particle \| \| NEG_PART \| Dialect \| Negation particle \| \| HASH \| Tweet \| Hashtag \| \| EMOT \| Tweet \| Emoticon/Emoji \| \| MENTION \| Tweet \| Mention \| \| URL \| Tweet \| URL \| ### Data Splits The dataset is split by dialect. \| Dialect \| Tweets \| Words \| \| ----- \| ------ \| ----- \| \| Egyptian (EGY) \| 350 \| 7481 \| \| Levantine (LEV) \| 350 \| 7221 \| \| Gulf (GLF) \| 350 \| 6767 \| \| Maghrebi (MGR) \| 350 \| 6400 \| ## Dataset Creation ### Curation Rationale This dataset was created to address the lack of computational resources available for dialects of Arabic. These dialects are typically used in speech, while written forms of the language are typically in Modern Standard Arabic. Social media, however, has provided a venue for people to use dialects in written format. ### Source Data This dataset builds off of the work of [Eldesouki et al. (2017)](https://arxiv.org/pdf/1708.05891.pdf) and [Samih et al. (2017b)](https://www.aclweb.org/anthology/K17-1043.pdf) who originally collected the tweets. #### Initial Data Collection and Normalization They started with 175 million Arabic tweets returned by the Twitter API using the query "lang:ar" in March 2014. They then filtered this set using author-identified locations and tokens that are unique to each dialect. Finally, they had native speakers of each dialect select 350 tweets that were heavily accented. #### Who are the source language producers? The source language producers are people who posted on Twitter in Arabic using dialectal words from countries where the dialects of interest were spoken, as identified in [Mubarak and Darwish (2014)](https://www.aclweb.org/anthology/W14-3601.pdf). ### Annotations #### Annotation process The segmentation guidelines are available at https://alt.qcri.org/resources1/da_resources/seg-guidelines.pdf. The tagging guidelines are not provided, but Darwish at al. note that there were multiple rounds of quality control and revision. #### Who are the annotators? The POS tags were annotated by native speakers of each dialect. Further information is not known. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset Darwish et al find that the accuracy on the Maghrebi dataset suffered the most when the training set was from another dialect, and conversely training on Maghrebi yielded the worst results for all the other dialects. They suggest that Egyptian, Levantine, and Gulf may be more similar to each other and Maghrebi the most dissimilar to all of them. They also find that training on Modern Standard Arabic (MSA) and testing on dialects yielded significantly lower results compared to training on dialects and testing on MSA. This suggests that dialectal variation should be a significant consideration for future work in Arabic NLP applications, particularly when working with social media text. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Kareem Darwish, Hamdy Mubarak, Mohamed Eldesouki and Ahmed Abdelali with the Qatar Computing Research Institute (QCRI), Younes Samih and Laura Kallmeyer with the University of Dusseldorf, Randah Alharbi and Walid Magdy with the University of Edinburgh, and Mohammed Attia with Google. No funding information was included. ### Licensing Information This dataset is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy and Laura Kallmeyer (2018) Multi-Dialect Arabic POS Tagging: A CRF Approach. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 7-12, 2018. Miyazaki, Japan. ``` @InProceedings{DARWISH18.562, author = {Kareem Darwish ,Hamdy Mubarak ,Ahmed Abdelali ,Mohamed Eldesouki ,Younes Samih ,Randah Alharbi ,Mohammed Attia ,Walid Magdy and Laura Kallmeyer}, title = {Multi-Dialect Arabic POS Tagging: A CRF Approach}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-00-9}, language = {english} } ``` ### Contributions Thanks to [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.
arabic_speech_corpus	--- pretty_name: Arabic Speech Corpus annotations_creators: - expert-generated language_creators: - crowdsourced language: - ar license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: arabic-speech-corpus size_categories: - 1K<n<10K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] train-eval-index: - config: clean task: automatic-speech-recognition task_id: speech_recognition splits: train_split: train eval_split: test col_mapping: file: path text: text metrics: - type: wer name: WER - type: cer name: CER dataset_info: features: - name: file dtype: string - name: text dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: phonetic dtype: string - name: orthographic dtype: string config_name: clean splits: - name: train num_bytes: 1002365 num_examples: 1813 - name: test num_bytes: 65784 num_examples: 100 download_size: 1192302846 dataset_size: 1068149 --- # Dataset Card for Arabic Speech Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Arabic Speech Corpus](http://en.arabicspeechcorpus.com/) - Repository: [Needs More Information] - Paper: [Modern standard Arabic phonetics for speech synthesis](http://en.arabicspeechcorpus.com/Nawar%20Halabi%20PhD%20Thesis%20Revised.pdf) - Leaderboard: [Paperswithcode Leaderboard][Needs More Information] - Point of Contact: [Nawar Halabi](mailto:[email protected]) ### Dataset Summary This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton. The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The audio is in Arabic. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. An example from the dataset is: ``` { 'file': '/Users/username/.cache/huggingface/datasets/downloads/extracted/baebe85e2cb67579f6f88e7117a87888c1ace390f4f14cb6c3e585c517ad9db0/arabic-speech-corpus/wav/ARA NORM 0002.wav', 'audio': {'path': '/Users/username/.cache/huggingface/datasets/downloads/extracted/baebe85e2cb67579f6f88e7117a87888c1ace390f4f14cb6c3e585c517ad9db0/arabic-speech-corpus/wav/ARA NORM 0002.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000}, 'orthographic': 'waraj~aHa Alt~aqoriyru Al~aiy >aEad~ahu maEohadu >aboHaA^i haDabapi Alt~ibiti fiy Alo>akaAdiymiy~api AlS~iyniy~api liloEuluwmi - >ano tasotamir~a darajaAtu AloHaraArapi wamusotawayaAtu Alr~uTuwbapi fiy Alo<irotifaAEi TawaAla haaA Aloqarono', 'phonetic': "sil w a r a' jj A H a tt A q r ii0' r u0 ll a * i0 < a E a' dd a h u0 m a' E h a d u0 < a b H aa' ^ i0 h A D A' b a t i0 tt i1' b t i0 f i0 l < a k aa d ii0 m ii0' y a t i0 SS II0 n ii0' y a t i0 l u0 l E u0 l uu0' m i0 sil < a' n t a s t a m i0' rr a d a r a j aa' t u0 l H a r aa' r a t i0 w a m u0 s t a w a y aa' t u0 rr U0 T UU0' b a t i0 f i0 l Ah i0 r t i0 f aa' E i0 T A' w A l a h aa' * a l q A' r n sil", 'text': '\ufeffwaraj~aHa Alt~aqoriyru Al~aTHiy >aEad~ahu maEohadu >aboHaA^i haDabapi Alt~ibiti fiy Alo>akaAdiymiy~api AlS~iyniy~api liloEuluwmi - >ano tasotamir~a darajaAtu AloHaraArapi wamusotawayaAtu Alr~uTuwbapi fiy Alo<irotifaAEi TawaAla haTHaA Aloqarono' } ``` ### Data Fields - file: A path to the downloaded audio file in .wav format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - phonetic: the transcription in phonentics format. - orthographic: the transcriptions written in orthographic format. ### Data Splits \| \| Train \| Test \| \| ----- \| ----- \| ---- \| \| dataset \| 1813 \| 100 \| ## Dataset Creation ### Curation Rationale The corpus was created with Speech Synthesis as the main application in mind. Although it has been used as part of a larger corpus for speech recognition and speech denoising. Here are some explanations why the corpus was built the way it is: * Corpus size: Budget limitations and the research goal resulted in the decision not to gather more data. The goal was to show that high quality speech synthesis is possible with smaller corpora. * Phonetic diversity: Just like with many corpora, the phonetic diversity was acheived using greedy methods. Start with a core set of utterances and add more utterances which contribute to adding more phonetic diversity the most iterativly. The measure of diversity is based on the diphone frequency. * Content: News, sports, economics, fully diacritised content from the internet was gathered. The choice of utterances was random to avoid copyright issues. Because of corpus size, acheiving diversity of content type was difficult and was not the goal. * Non-sense utterances: The corpus contains a large set of utterances that are generated computationally to compensate for the diphones missing in the main part of the corpus. The usefullness of non-sense utterances was not proven in the PhD thesis. * The talent: The voice talent had a Syrian dialect from Damascus and spoke in formal Arabic. Please refer to [PhD thesis](#Citation-Information) for more detailed information. ### Source Data #### Initial Data Collection and Normalization News, sports, economics, fully diacritised content from the internet was gathered. The choice of utterances was random to avoid copyright issues. Because of corpus size, acheiving diversity of content type was difficult and was not the goal. We were restricted to content which was fully diacritised to make the annotation process easier. Just like with many corpora, the phonetic diversity was acheived using greedy methods. Start with a core set of utterances and add more utterances which contribute to adding more phonetic diversity the most iterativly. The measure of diversity is based on the diphone frequency. Please refer to [PhD thesis](#Citation-Information). #### Who are the source language producers? Please refer to [PhD thesis](#Citation-Information). ### Annotations #### Annotation process Three annotators aligned audio with phonemes with the help of HTK forced alignment. They worked on overlapping parts as well to assess annotator agreement and the quality of the annotations. The entire corpus was checked by human annotators. Please refer to [PhD thesis](#Citation-Information). #### Who are the annotators? Nawar Halabi and two anonymous Arabic language teachers. ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. The voice talent agreed in writing for their voice to be used in speech technologies as long as they stay anonymous. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio by Nawar Halabi. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @phdthesis{halabi2016modern, title={Modern standard Arabic phonetics for speech synthesis}, author={Halabi, Nawar}, year={2016}, school={University of Southampton} } ``` ### Contributions This dataset was created by: * Nawar Halabi [@nawarhalabi](https://github.com/nawarhalabi) main creator and annotator. * Two anonymous Arabic langauge teachers as annotators. * One anonymous voice talent. * Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset.
arcd	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar language_bcp47: - ar-SA license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: arcd pretty_name: ARCD dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: plain_text splits: - name: train num_bytes: 811064 num_examples: 693 - name: validation num_bytes: 885648 num_examples: 702 download_size: 1942399 dataset_size: 1696712 --- # Dataset Card for "arcd" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/husseinmozannar/SOQAL/tree/master/data](https://github.com/husseinmozannar/SOQAL/tree/master/data) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.94 MB - Size of the generated dataset: 1.70 MB - Total amount of disk used: 3.64 MB ### Dataset Summary Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 1.94 MB - Size of the generated dataset: 1.70 MB - Total amount of disk used: 3.64 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": "{\"answer_start\": [34], \"text\": [\"صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر،\"]}...", "context": "\"حمزة بن عبد المطلب الهاشمي القرشي صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر، وهو خير أع...", "id": "621723207492", "question": "من هو حمزة بن عبد المطلب؟", "title": "حمزة بن عبد المطلب" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits \| name \| train \| validation \| \| ---------- \| ----: \| ---------: \| \| plain_text \| 693 \| 702 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@tayciryahmed](https://github.com/tayciryahmed) for adding this dataset.
arsentd_lev	--- annotations_creators: - crowdsourced language_creators: - found language: - apc - ajp license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - topic-classification paperswithcode_id: arsentd-lev pretty_name: ArSenTD-LEV dataset_info: features: - name: Tweet dtype: string - name: Country dtype: class_label: names: '0': jordan '1': lebanon '2': syria '3': palestine - name: Topic dtype: string - name: Sentiment dtype: class_label: names: '0': negative '1': neutral '2': positive '3': very_negative '4': very_positive - name: Sentiment_Expression dtype: class_label: names: '0': explicit '1': implicit '2': none - name: Sentiment_Target dtype: string splits: - name: train num_bytes: 1233980 num_examples: 4000 download_size: 392666 dataset_size: 1233980 --- # Dataset Card for ArSenTD-LEV ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [ArSenTD-LEV homepage](http://oma-project.com/) - Paper: [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830) ### Dataset Summary The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria. ### Supported Tasks and Leaderboards Sentriment analysis ### Languages Arabic Levantine Dualect ## Dataset Structure ### Data Instances {'Country': 0, 'Sentiment': 3, 'Sentiment_Expression': 0, 'Sentiment_Target': 'هاي سوالف عصابات ارهابية', 'Topic': 'politics', 'Tweet': 'ثلاث تفجيرات في #كركوك الحصيلة قتيل و 16 جريح بدأت اكلاوات كركوك كانت امان قبل دخول القوات العراقية ، هاي سوالف عصابات ارهابية'} ### Data Fields `Tweet`: the text content of the tweet \ `Country`: the country from which the tweet was collected ('jordan', 'lebanon', 'syria', 'palestine')\ `Topic`: the topic being discussed in the tweet (personal, politics, religion, sports, entertainment and others) \ `Sentiment`: the overall sentiment expressed in the tweet (very_negative, negative, neutral, positive and very_positive) \ `Sentiment_Expression`: the way how the sentiment was expressed: explicit, implicit, or none (the latter when sentiment is neutral) \ `Sentiment_Target`: the segment from the tweet to which sentiment is expressed. If sentiment is neutral, this field takes the 'none' value. ### Data Splits No standard splits are provided ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Make sure to read and agree to the [license](http://oma-project.com/ArSenL/ArSenTD_Lev_Intro) ### Citation Information ``` @article{baly2019arsentd, title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets}, author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir}, journal={arXiv preprint arXiv:1906.01830}, year={2019} } ``` ### Contributions Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset.
art	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - multiple-choice - text-classification task_ids: - natural-language-inference paperswithcode_id: art-dataset pretty_name: Abductive Reasoning in narrative Text tags: - abductive-natural-language-inference dataset_info: features: - name: observation_1 dtype: string - name: observation_2 dtype: string - name: hypothesis_1 dtype: string - name: hypothesis_2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' config_name: anli splits: - name: validation num_bytes: 312314 num_examples: 1532 - name: train num_bytes: 34046304 num_examples: 169654 download_size: 5118294 dataset_size: 34358618 --- # Dataset Card for "art" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://leaderboard.allenai.org/anli/submissions/get-started](https://leaderboard.allenai.org/anli/submissions/get-started) - Repository: https://github.com/allenai/abductive-commonsense-reasoning - Paper: [Abductive Commonsense Reasoning](https://arxiv.org/abs/1908.05739) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 5.12 MB - Size of the generated dataset: 34.36 MB - Total amount of disk used: 39.48 MB ### Dataset Summary ART consists of over 20k commonsense narrative contexts and 200k explanations. The Abductive Natural Language Inference Dataset from AI2. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### anli - Size of downloaded dataset files: 5.12 MB - Size of the generated dataset: 34.36 MB - Total amount of disk used: 39.48 MB An example of 'train' looks as follows. ``` { "hypothesis_1": "Chad's car had all sorts of other problems besides alignment.", "hypothesis_2": "Chad's car had all sorts of benefits other than being sexy.", "label": 1, "observation_1": "Chad went to get the wheel alignment measured on his car.", "observation_2": "The mechanic provided a working alignment with new body work." } ``` ### Data Fields The data fields are the same among all splits. #### anli - `observation_1`: a `string` feature. - `observation_2`: a `string` feature. - `hypothesis_1`: a `string` feature. - `hypothesis_2`: a `string` feature. - `label`: a classification label, with possible values including `0` (0), `1` (1), `2` (2). ### Data Splits \|name\|train \|validation\| \|----\|-----:\|---------:\| \|anli\|169654\| 1532\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Bhagavatula2020Abductive, title={Abductive Commonsense Reasoning}, author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi}, booktitle={International Conference on Learning Representations}, year={2020}, url={https://openreview.net/forum?id=Byg1v1HKDB} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
arxiv_dataset	--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation - summarization - text-retrieval task_ids: - document-retrieval - entity-linking-retrieval - explanation-generation - fact-checking-retrieval - text-simplification paperswithcode_id: null pretty_name: arXiv Dataset dataset_info: features: - name: id dtype: string - name: submitter dtype: string - name: authors dtype: string - name: title dtype: string - name: comments dtype: string - name: journal-ref dtype: string - name: doi dtype: string - name: report-no dtype: string - name: categories dtype: string - name: license dtype: string - name: abstract dtype: string - name: update_date dtype: string splits: - name: train num_bytes: 2246545603 num_examples: 1796911 download_size: 0 dataset_size: 2246545603 --- # Dataset Card for arXiv Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Kaggle arXiv Dataset Homepage](https://www.kaggle.com/Cornell-University/arxiv) - Repository: - Paper: [On the Use of ArXiv as a Dataset](https://arxiv.org/abs/1905.00075) - Leaderboard: - Point of Contact: [Matt Bierbaum](mailto:[email protected]) ### Dataset Summary A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is English ## Dataset Structure ### Data Instances This dataset is a mirror of the original ArXiv data. Because the full dataset is rather large (1.1TB and growing), this dataset provides only a metadata file in the json format. An example is given below ``` {'id': '0704.0002', 'submitter': 'Louis Theran', 'authors': 'Ileana Streinu and Louis Theran', 'title': 'Sparsity-certifying Graph Decompositions', 'comments': 'To appear in Graphs and Combinatorics', 'journal-ref': None, 'doi': None, 'report-no': None, 'categories': 'math.CO cs.CG', 'license': 'http://arxiv.org/licenses/nonexclusive-distrib/1.0/', 'abstract': ' We describe a new algorithm, the $(k,\\ell)$-pebble game with colors, and use\nit obtain a characterization of the family of $(k,\\ell)$-sparse graphs and\nalgorithmic solutions to a family of problems concerning tree decompositions of\ngraphs. Special instances of sparse graphs appear in rigidity theory and have\nreceived increased attention in recent years. In particular, our colored\npebbles generalize and strengthen the previous results of Lee and Streinu and\ngive a new proof of the Tutte-Nash-Williams characterization of arboricity. We\nalso present a new decomposition that certifies sparsity based on the\n$(k,\\ell)$-pebble game with colors. Our work also exposes connections between\npebble game algorithms and previous sparse graph algorithms by Gabow, Gabow and\nWestermann and Hendrickson.\n', 'update_date': '2008-12-13'} ``` ### Data Fields - `id`: ArXiv ID (can be used to access the paper) - `submitter`: Who submitted the paper - `authors`: Authors of the paper - `title`: Title of the paper - `comments`: Additional info, such as number of pages and figures - `journal-ref`: Information about the journal the paper was published in - `doi`: [Digital Object Identifier](https://www.doi.org) - `report-no`: Report Number - `abstract`: The abstract of the paper - `categories`: Categories / tags in the ArXiv system ### Data Splits The data was not splited. ## Dataset Creation ### Curation Rationale For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming depth. In these times of unique global challenges, efficient extraction of insights from data is essential. To help make the arXiv more accessible, a free, open pipeline on Kaggle to the machine-readable arXiv dataset: a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more is presented to empower new use cases that can lead to the exploration of richer machine learning techniques that combine multi-modal features towards applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces. ### Source Data This data is based on arXiv papers. [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations This dataset contains no annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The original data is maintained by [ArXiv](https://arxiv.org/) ### Licensing Information The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/) ### Citation Information ``` @misc{clement2019arxiv, title={On the Use of ArXiv as a Dataset}, author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi}, year={2019}, eprint={1905.00075}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` ### Contributions Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset.
ascent_kb	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: ascentkb pretty_name: Ascent KB tags: - knowledge-base dataset_info: - config_name: canonical features: - name: arg1 dtype: string - name: rel dtype: string - name: arg2 dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2976697816 num_examples: 8904060 download_size: 710727536 dataset_size: 2976697816 - config_name: open features: - name: subject dtype: string - name: predicate dtype: string - name: object dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2882678298 num_examples: 8904060 download_size: 710727536 dataset_size: 2882678298 --- # Dataset Card for Ascent KB ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://ascent.mpi-inf.mpg.de/ - Repository: https://github.com/phongnt570/ascent - Paper: https://arxiv.org/abs/2011.00905 - Point of Contact: http://tuan-phong.com ### Dataset Summary This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). The focus of this dataset is on everyday concepts such as elephant, car, laptop, etc. The current version of Ascent KB (v1.0.0) is approximately 19 times larger than ConceptNet (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded). For more details, take a look at [the research paper](https://arxiv.org/abs/2011.00905) and [the website](https://ascent.mpi-inf.mpg.de). ### Supported Tasks and Leaderboards The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances There are two configurations available for this dataset: 1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>` assertions where the relations (`rel`) were mapped to [ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations) with slight modifications: - Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`. - All `/r/HasA` relations were replaced with `/r/HasAspect`. This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020) schema, although they grouped all `/r/HasA` and `/r/HasProperty` into `/r/HasProperty`. - The `/r/UsedFor` relation was replaced with `/r/ObjectUse` which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.). This is also taken from ATOMIC-2020. 2. `open`: This part contains open assertions of the form `<subject ; predicate ; object>` extracted directly from web contents. This is the original form of the `canonical` triples. In both configurations, each assertion is equipped with extra information including: a set of semantic `facets` (e.g., LOCATION, TEMPORAL, etc.), its `support` (i.e., number of occurrences), and a list of `source_sentences`. An example row in the `canonical` configuration: ```JSON { "arg1": "elephant", "rel": "/r/HasProperty", "arg2": "intelligent", "support": 15, "facets": [ { "value": "extremely", "type": "DEGREE", "support": 11 } ], "source_sentences": [ { "text": "Elephants are extremely intelligent animals.", "source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/" }, { "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.", "source": "https://www.elephantsforafrica.org/elephant-facts/" } ] } ``` ### Data Fields - For `canonical` configuration - `arg1`: the first argument to the relationship, e.g., elephant - `rel`: the canonical relation, e.g., /r/HasProperty - `arg2`: the second argument to the relationship, e.g., intelligence - `support`: the number of occurrences of the assertion, e.g., 15 - `facets`: an array of semantic facets, each contains - `value`: facet value, e.g., extremely - `type`: facet type, e.g., DEGREE - `support`: the number of occurrences of the facet, e.g., 11 - `source_sentences`: an array of source sentences from which the assertion was extracted, each contains - `text`: the raw text of the sentence - `source`: the URL to its parent document - For `open` configuration - The fields of this configuration are the same as the `canonical` configuration's, except that the (`arg1`, `rel`, `arg2`) fields are replaced with the (`subject`, `predicate`, `object`) fields which are free text phrases extracted directly from the source sentences using an Open Information Extraction (OpenIE) tool. ### Data Splits There are no splits. All data points come to a default split called `train`. ## Dataset Creation ### Curation Rationale The commonsense knowledge base was created to assist in development of robust and reliable AI. ### Source Data #### Initial Data Collection and Normalization Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions. The assertions were then grouped into semantically equivalent clusters. Take a look at the research paper for more details. #### Who are the source language producers? Web users. ### Annotations #### Annotation process None. #### Who are the annotators? None. ### Personal and Sensitive Information Unknown. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The knowledge base has been developed by researchers at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments. ### Licensing Information [The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @InProceedings{nguyen2021www, title={Advanced Semantics for Commonsense Knowledge Extraction}, author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, year={2021}, booktitle={The Web Conference 2021}, } ``` ### Contributions Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.
aslg_pc12	--- annotations_creators: - crowdsourced - expert-generated language: - ase - en language_creators: - found license: - cc-by-nc-4.0 multilinguality: - translation pretty_name: English-ASL Gloss Parallel Corpus 2012 size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: aslg-pc12 dataset_info: features: - name: gloss dtype: string - name: text dtype: string splits: - name: train num_bytes: 13497111 num_examples: 87710 download_size: 12773431 dataset_size: 13497111 --- # Dataset Card for "aslg_pc12" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://achrafothman.net/site/asl-smt/](https://achrafothman.net/site/asl-smt/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 12.77 MB - Size of the generated dataset: 13.50 MB - Total amount of disk used: 26.27 MB ### Dataset Summary Synthetic English-ASL Gloss Parallel Corpus 2012 ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 12.77 MB - Size of the generated dataset: 13.50 MB - Total amount of disk used: 26.27 MB An example of 'train' looks as follows. ``` { "gloss": "WRITE STATEMENT AND DESC-ORAL QUESTION TABLE SEE MINUTE\n", "text": "written statements and oral questions tabling see minutes\n" } ``` ### Data Fields The data fields are the same among all splits. #### default - `gloss`: a `string` feature. - `text`: a `string` feature. ### Data Splits \| name \|train\| \|-------\|----:\| \|default\|87710\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{othman2012english, title={English-asl gloss parallel corpus 2012: Aslg-pc12}, author={Othman, Achraf and Jemni, Mohamed}, booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC}, year={2012} } ``` ### Contributions Thanks to [@AmitMY](https://github.com/AmitMY) for adding this dataset.
asnq	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - extended\|natural_questions task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: asnq pretty_name: Answer Sentence Natural Questions (ASNQ) dataset_info: features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': neg '1': pos - name: sentence_in_long_answer dtype: bool - name: short_answer_in_sentence dtype: bool splits: - name: train num_bytes: 3656881376 num_examples: 20377568 - name: validation num_bytes: 168005155 num_examples: 930062 download_size: 3563857920 dataset_size: 3824886531 --- # Dataset Card for "asnq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq](https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://arxiv.org/abs/1911.04118) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 3.56 GB - Size of the generated dataset: 3.82 GB - Total amount of disk used: 7.39 GB ### Dataset Summary ASNQ is a dataset for answer sentence selection derived from Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). Each example contains a question, candidate sentence, label indicating whether or not the sentence answers the question, and two additional features -- sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence. For more details please see https://arxiv.org/abs/1911.04118 and https://research.google/pubs/pub47761/ ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 3.56 GB - Size of the generated dataset: 3.82 GB - Total amount of disk used: 7.39 GB An example of 'validation' looks as follows. ``` { "label": 0, "question": "when did somewhere over the rainbow come out", "sentence": "In films and TV shows ( edit ) In the film Third Finger , Left Hand ( 1940 ) with Myrna Loy , Melvyn Douglas , and Raymond Walburn , the tune played throughout the film in short sequences .", "sentence_in_long_answer": false, "short_answer_in_sentence": false } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `sentence`: a `string` feature. - `label`: a classification label, with possible values including `neg` (0), `pos` (1). - `sentence_in_long_answer`: a `bool` feature. - `short_answer_in_sentence`: a `bool` feature. ### Data Splits \| name \| train \|validation\| \|-------\|-------:\|---------:\| \|default\|20377568\| 930062\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License: https://github.com/alexa/wqa_tanda/blob/master/LICENSE ### Citation Information ``` @article{Garg_2020, title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection}, volume={34}, ISSN={2159-5399}, url={http://dx.doi.org/10.1609/AAAI.V34I05.6282}, DOI={10.1609/aaai.v34i05.6282}, number={05}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, publisher={Association for the Advancement of Artificial Intelligence (AAAI)}, author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro}, year={2020}, month={Apr}, pages={7780–7788} } ``` ### Contributions Thanks to [@mkserge](https://github.com/mkserge) for adding this dataset.
asset	--- annotations_creators: - machine-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original - extended\|other-turkcorpus task_categories: - text-classification - text2text-generation task_ids: - text-simplification paperswithcode_id: asset pretty_name: ASSET configs: - ratings - simplification tags: - simplification-evaluation dataset_info: - config_name: simplification features: - name: original dtype: string - name: simplifications sequence: string splits: - name: validation num_bytes: 2303496 num_examples: 2000 - name: test num_bytes: 411031 num_examples: 359 download_size: 3639353 dataset_size: 2714527 - config_name: ratings features: - name: original dtype: string - name: simplification dtype: string - name: original_sentence_id dtype: int32 - name: aspect dtype: class_label: names: '0': meaning '1': fluency '2': simplicity - name: worker_id dtype: int32 - name: rating dtype: int32 splits: - name: full num_bytes: 1036853 num_examples: 4500 download_size: 3639353 dataset_size: 1036853 --- # Dataset Card for ASSET ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [ASSET Github repository](https://github.com/facebookresearch/asset) - Paper: [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://www.aclweb.org/anthology/2020.acl-main.424/) - Point of Contact: [Louis Martin]([email protected]) ### Dataset Summary [ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus]( https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations. ### Supported Tasks and Leaderboards The dataset supports the evaluation of `text-simplification` systems. Success in this tasks is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf). ### Languages The text in this dataset is in English (`en`). ## Dataset Structure ### Data Instances - `simplification` configuration: an instance consists in an original sentence and 10 possible reference simplifications. - `ratings` configuration: a data instance consists in an original sentence, a simplification obtained by an automated system, and a judgment of quality along one of three axes by a crowd worker. ### Data Fields - `original`: an original sentence from the source datasets - `simplifications`: in the `simplification` config, a set of reference simplifications produced by crowd workers. - `simplification`: in the `ratings` config, a simplification of the original obtained by an automated system - `aspect`: in the `ratings` config, the aspect on which the simplification is evaluated, one of `meaning`, `fluency`, `simplicity` - `rating`: a quality rating between 0 and 100 ### Data Splits ASSET does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) for training. Each input sentence has 10 associated reference simplified sentences. The statistics of ASSET are given below. \| \| Dev \| Test \| Total \| \| ----- \| ------ \| ---- \| ----- \| \| Input Sentences \| 2000 \| 359 \| 2359 \| \| Reference Simplifications \| 20000 \| 3590 \| 23590 \| The test and validation sets are the same as those of TurkCorpus. The split was random. There are 19.04 tokens per reference on average (lower than 21.29 and 25.49 for TurkCorpus and HSplit, respectively). Most (17,245) of the referece sentences do not involve sentence splitting. ## Dataset Creation ### Curation Rationale ASSET was created in order to improve the evaluation of sentence simplification. It uses the same input sentences as the [TurkCorpus]( https://github.com/cocoxu/simplification/) dataset from [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). The 2,359 input sentences of TurkCorpus are a sample of "standard" (not simple) sentences from the [Parallel Wikipedia Simplification (PWKP)](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/sentence_simplification/simple_complex_sentence_pairs/index.en.jsp) dataset [(Zhu et al., 2010)](https://www.aclweb.org/anthology/C10-1152.pdf), which come from the August 22, 2009 version of Wikipedia. The sentences of TurkCorpus were chosen to be of similar length [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). No further information is provided on the sampling strategy. The TurkCorpus dataset was developed in order to overcome some of the problems with sentence pairs from Standard and Simple Wikipedia: a large fraction of sentences were misaligned, or not actually simpler [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). However, TurkCorpus mainly focused on lexical paraphrasing, and so cannot be used to evaluate simplifications involving compression (deletion) or sentence splitting. HSplit [(Sulem et al., 2018)](https://www.aclweb.org/anthology/D18-1081.pdf), on the other hand, can only be used to evaluate sentence splitting. The reference sentences in ASSET include a wider variety of sentence rewriting strategies, combining splitting, compression and paraphrasing. Annotators were given examples of each kind of transformation individually, as well as all three transformations used at once, but were allowed to decide which transformations to use for any given sentence. An example illustrating the differences between TurkCorpus, HSplit and ASSET is given below: > Original: He settled in London, devoting himself chiefly to practical teaching. > > TurkCorpus: He rooted in London, devoting himself mainly to practical teaching. > > HSplit: He settled in London. He devoted himself chiefly to practical teaching. > > ASSET: He lived in London. He was a teacher. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The input sentences are from English Wikipedia (August 22, 2009 version). No demographic information is available for the writers of these sentences. However, most Wikipedia editors are male (Lam, 2011; Graells-Garrido, 2015), which has an impact on the topics covered (see also [the Wikipedia page on Wikipedia gender bias](https://en.wikipedia.org/wiki/Gender_bias_on_Wikipedia)). In addition, Wikipedia editors are mostly white, young, and from the Northern Hemisphere [(Wikipedia: Systemic bias)](https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias). Reference sentences were written by 42 workers on Amazon Mechanical Turk (AMT). The requirements for being an annotator were: - Passing a Qualification Test (appropriately simplifying sentences). Out of 100 workers, 42 passed the test. - Being a resident of the United States, United Kingdom or Canada. - Having a HIT approval rate over 95%, and over 1000 HITs approved. No other demographic or compensation information is provided in the ASSET paper. ### Annotations #### Annotation process The instructions given to the annotators are available [here](https://github.com/facebookresearch/asset/blob/master/crowdsourcing/AMT_AnnotationInstructions.pdf). #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases (Schmahl et al., 2020) and racial biases (Adams et al., 2019). > Adams, Julia, Hannah Brückner, and Cambria Naslund. "Who Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test”." Socius 5 (2019): 2378023118823946. > Schmahl, Katja Geertruida, et al. "Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings." Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science. 2020. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators ASSET was developed by researchers at the University of Sheffield, Inria, Facebook AI Research, and Imperial College London. The work was partly supported by Benoît Sagot's chair in the PRAIRIE institute, funded by the French National Research Agency (ANR) as part of the "Investissements d’avenir" program (reference ANR-19-P3IA-0001). ### Licensing Information [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) ### Citation Information ``` @inproceedings{alva-manchego-etal-2020-asset, title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations", author = "Alva-Manchego, Fernando and Martin, Louis and Bordes, Antoine and Scarton, Carolina and Sagot, Beno{\^\i}t and Specia, Lucia", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.424", pages = "4668--4679", } ``` This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r). ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
assin	--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - natural-language-inference - semantic-similarity-scoring paperswithcode_id: assin pretty_name: ASSIN dataset_info: - config_name: full features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 986507 num_examples: 5000 - name: test num_bytes: 767312 num_examples: 4000 - name: validation num_bytes: 196829 num_examples: 1000 download_size: 749735 dataset_size: 1950648 - config_name: ptpt features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 523002 num_examples: 2500 - name: test num_bytes: 392888 num_examples: 2000 - name: validation num_bytes: 105626 num_examples: 500 download_size: 749735 dataset_size: 1021516 - config_name: ptbr features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 463513 num_examples: 2500 - name: test num_bytes: 374432 num_examples: 2000 - name: validation num_bytes: 91211 num_examples: 500 download_size: 749735 dataset_size: 929156 --- # Dataset Card for ASSIN ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [ASSIN homepage](http://nilc.icmc.usp.br/assin/) - Repository: [ASSIN repository](http://nilc.icmc.usp.br/assin/) - Paper: [ASSIN: Evaluation of Semantic Similarity and Textual Inference](http://propor2016.di.fc.ul.pt/wp-content/uploads/2015/10/assin-overview.pdf) - Point of Contact: [Erick Rocha Fonseca](mailto:[email protected]) ### Dataset Summary The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal and Brazil, respectively. To create the corpus, the authors started by collecting a set of news articles describing the same event (one news article from Google News Portugal and another from Google News Brazil) from Google News. Then, they employed Latent Dirichlet Allocation (LDA) models to retrieve pairs of similar sentences between sets of news articles that were grouped together around the same topic. For that, two LDA models were trained (for EP and for BP) on external and large-scale collections of unannotated news articles from Portuguese and Brazilian news providers, respectively. Then, the authors defined a lower and upper threshold for the sentence similarity score of the retrieved pairs of sentences, taking into account that high similarity scores correspond to sentences that contain almost the same content (paraphrase candidates), and low similarity scores correspond to sentences that are very different in content from each other (no-relation candidates). From the collection of pairs of sentences obtained at this stage, the authors performed some manual grammatical corrections and discarded some of the pairs wrongly retrieved. Furthermore, from a preliminary analysis made to the retrieved sentence pairs the authors noticed that the number of contradictions retrieved during the previous stage was very low. Additionally, they also noticed that event though paraphrases are not very frequent, they occur with some frequency in news articles. Consequently, in contrast with the majority of the currently available corpora for other languages, which consider as labels “neutral”, “entailment” and “contradiction” for the task of RTE, the authors of the ASSIN corpus decided to use as labels “none”, “entailment” and “paraphrase”. Finally, the manual annotation of pairs of sentences was performed by human annotators. At least four annotators were randomly selected to annotate each pair of sentences, which is done in two steps: (i) assigning a semantic similarity label (a score between 1 and 5, from unrelated to very similar); and (ii) providing an entailment label (one sentence entails the other, sentences are paraphrases, or no relation). Sentence pairs where at least three annotators do not agree on the entailment label were considered controversial and thus discarded from the gold standard annotations. The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese (ptbr) and half in European Portuguese (ptpt). Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the ASSIN dataset looks as follows: ``` { "entailment_judgment": 0, "hypothesis": "André Gomes entra em campo quatro meses depois de uma lesão na perna esquerda o ter afastado dos relvados.", "premise": "Relembre-se que o atleta estava afastado dos relvados desde maio, altura em que contraiu uma lesão na perna esquerda.", "relatedness_score": 3.5, "sentence_pair_id": 1 } ``` ### Data Fields - `sentence_pair_id`: a `int64` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `relatedness_score`: a `float32` feature. - `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`, `PARAPHRASE`. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: \| \| Train \| Val \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| full \| 5000 \| 1000 \| 4000 \| \| ptbr \| 2500 \| 500 \| 2000 \| \| ptpt \| 2500 \| 500 \| 2000 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
assin2	--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - natural-language-inference - semantic-similarity-scoring paperswithcode_id: assin2 pretty_name: ASSIN 2 dataset_info: features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT splits: - name: train num_bytes: 864816 num_examples: 6500 - name: test num_bytes: 339580 num_examples: 2448 - name: validation num_bytes: 66895 num_examples: 500 download_size: 2113646 dataset_size: 1271291 --- # Dataset Card for ASSIN 2 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [ASSIN 2 homepage](https://sites.google.com/view/assin2) - Repository: [ASSIN 2 repository](https://sites.google.com/view/assin2) - Paper: [The ASSIN 2 shared task: a quick overview](https://drive.google.com/file/d/1ft1VU6xiVm-N58dfAp6FHWjQ4IvcXgqp/view) - Point of Contact: [Livy Real](mailto:[email protected]) ### Dataset Summary The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1. The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese, annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same annotation. All data were manually annotated. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the ASSIN 2 dataset looks as follows: ``` { "entailment_judgment": 1, "hypothesis": "Uma criança está segurando uma pistola de água", "premise": "Uma criança risonha está segurando uma pistola de água e sendo espirrada com água", "relatedness_score": 4.5, "sentence_pair_id": 1 } ``` ### Data Fields - `sentence_pair_id`: a `int64` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `relatedness_score`: a `float32` feature. - `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: \| Train \| Val \| Test \| \| ------ \| ----- \| ---- \| \| 6500 \| 500 \| 2448 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
atomic	--- pretty_name: ATOMIC annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: atomic tags: - common-sense-if-then-reasoning dataset_info: features: - name: event dtype: string - name: oEffect sequence: string - name: oReact sequence: string - name: oWant sequence: string - name: xAttr sequence: string - name: xEffect sequence: string - name: xIntent sequence: string - name: xNeed sequence: string - name: xReact sequence: string - name: xWant sequence: string - name: prefix sequence: string - name: split dtype: string config_name: atomic splits: - name: train num_bytes: 32441878 num_examples: 202271 - name: test num_bytes: 3995624 num_examples: 24856 - name: validation num_bytes: 3629768 num_examples: 22620 download_size: 19083782 dataset_size: 40067270 --- # Dataset Card for An Atlas of Machine Commonsense for If-Then Reasoning - Atomic Common Sense Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://homes.cs.washington.edu/~msap/atomic/ - Repository: https://homes.cs.washington.edu/~msap/atomic/ - Paper: Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith & Yejin Choi (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI ### Dataset Summary This dataset provides the template sentences and relationships defined in the ATOMIC common sense dataset. There are three splits - train, test, and dev. From the authors. Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us ([email protected]) if you have any concerns. For more information, see: https://homes.cs.washington.edu/~msap/atomic/ ### Supported Tasks and Leaderboards [More Information Needed] ### Languages en ## Dataset Structure ### Data Instances Here is one example from the atomic dataset: `` {'event': "PersonX uses PersonX's ___ to obtain", 'oEffect': [], 'oReact': ['annoyed', 'angry', 'worried'], 'oWant': [], 'prefix': ['uses', 'obtain'], 'split': 'trn', 'xAttr': [], 'xEffect': [], 'xIntent': ['to have an advantage', 'to fulfill a desire', 'to get out of trouble'], 'xNeed': [], 'xReact': ['pleased', 'smug', 'excited'], 'xWant': []} `` ### Data Fields Notes from the authors: * event: just a string representation of the event. * oEffect,oReact,oWant,xAttr,xEffect,xIntent,xNeed,xReact,xWant: annotations for each of the dimensions, stored in a json-dumped string. Note: "none" means the worker explicitly responded with the empty response, whereas [] means the worker did not annotate this dimension. * prefix: json-dumped string that represents the prefix of content words (used to make a better trn/dev/tst split). * split: string rep of which split the event belongs to. ### Data Splits The atomic dataset has three splits: test, train and dev of the form: ## Dataset Creation ### Curation Rationale This dataset was gathered and created over to assist in common sense reasoning. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created by the University of Washington using crowd sourced data #### Who are the source language producers? The Atomic authors and crowd source. ### Annotations #### Annotation process Human annotations directed by forms. #### Who are the annotators? Human annotations. ### Personal and Sensitive Information Unkown, but likely none. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines understand common sense. ### Discussion of Biases Since the data is human annotators, there is likely to be baised. From the authors: Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us ([email protected]) if you have any concerns. ### Other Known Limitations While there are many relationships, the data is quite sparse. Also, each item of the dataset could be expanded into multiple sentences along the vsrious dimensions, oEffect, oRect, etc. For example, given event: "PersonX uses PersonX's ___ to obtain" and dimension oReact: "annoyed", this could be transformed into an entry: "PersonX uses PersonX's ___ to obtain => PersonY is annoyed" ## Additional Information ### Dataset Curators The authors of Aotmic at The University of Washington ### Licensing Information The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/ ### Citation Information @article{Sap2019ATOMICAA, title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning}, author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi}, journal={ArXiv}, year={2019}, volume={abs/1811.00146} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.
autshumato	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - tn - ts - zu license: - cc-by-2.5 multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: autshumato configs: - autshumato-en-tn - autshumato-en-ts - autshumato-en-ts-manual - autshumato-en-zu - autshumato-tn - autshumato-ts dataset_info: - config_name: autshumato-en-tn features: - name: translation dtype: translation: languages: - en - tn splits: - name: train num_bytes: 28826392 num_examples: 159000 download_size: 9458762 dataset_size: 28826392 - config_name: autshumato-en-zu features: - name: translation dtype: translation: languages: - en - zu splits: - name: train num_bytes: 7188970 num_examples: 35489 download_size: 2068891 dataset_size: 7188970 - config_name: autshumato-en-ts features: - name: translation dtype: translation: languages: - en - ts splits: - name: train num_bytes: 50803849 num_examples: 450000 download_size: 15145915 dataset_size: 50803849 - config_name: autshumato-en-ts-manual features: - name: translation dtype: translation: languages: - en - ts splits: - name: train num_bytes: 10408757 num_examples: 92396 download_size: 2876924 dataset_size: 10408757 - config_name: autshumato-tn features: - name: text dtype: string splits: - name: train num_bytes: 5132267 num_examples: 38206 download_size: 1599029 dataset_size: 5132267 - config_name: autshumato-ts features: - name: text dtype: string splits: - name: train num_bytes: 3399674 num_examples: 58398 download_size: 974488 dataset_size: 3399674 --- # Dataset Card for autshumato ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://repo.sadilar.org/handle/20.500.12185/7/discover]() - Repository: []() - Paper: []() - Leaderboard: []() - Point of Contact: []() ### Dataset Summary Multilingual information access is stipulated in the South African constitution. In practise, this is hampered by a lack of resources and capacity to perform the large volumes of translation work required to realise multilingual information access. One of the aims of the Autshumato project is to develop machine translation systems for three South African languages pairs. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators [More Information Needed] ### Licensing Information ### Citation Information ``` @article{groenewald2010processing, title={Processing parallel text corpora for three South African language pairs in the Autshumato project}, author={Groenewald, Hendrik J and du Plooy, Liza}, journal={AfLaT 2010}, pages={27}, year={2010} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
facebook/babi_qa	--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - cc-by-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: babi-1 pretty_name: BabiQa configs: - en-10k-qa1 - en-10k-qa10 - en-10k-qa11 - en-10k-qa12 - en-10k-qa13 - en-10k-qa14 - en-10k-qa15 - en-10k-qa16 - en-10k-qa17 - en-10k-qa18 - en-10k-qa19 - en-10k-qa2 - en-10k-qa20 - en-10k-qa3 - en-10k-qa4 - en-10k-qa5 - en-10k-qa6 - en-10k-qa7 - en-10k-qa8 - en-10k-qa9 - en-qa1 - en-qa10 - en-qa11 - en-qa12 - en-qa13 - en-qa14 - en-qa15 - en-qa16 - en-qa17 - en-qa18 - en-qa19 - en-qa2 - en-qa20 - en-qa3 - en-qa4 - en-qa5 - en-qa6 - en-qa7 - en-qa8 - en-qa9 - en-valid-10k-qa1 - en-valid-10k-qa10 - en-valid-10k-qa11 - en-valid-10k-qa12 - en-valid-10k-qa13 - en-valid-10k-qa14 - en-valid-10k-qa15 - en-valid-10k-qa16 - en-valid-10k-qa17 - en-valid-10k-qa18 - en-valid-10k-qa19 - en-valid-10k-qa2 - en-valid-10k-qa20 - en-valid-10k-qa3 - en-valid-10k-qa4 - en-valid-10k-qa5 - en-valid-10k-qa6 - en-valid-10k-qa7 - en-valid-10k-qa8 - en-valid-10k-qa9 - en-valid-qa1 - en-valid-qa10 - en-valid-qa11 - en-valid-qa12 - en-valid-qa13 - en-valid-qa14 - en-valid-qa15 - en-valid-qa16 - en-valid-qa17 - en-valid-qa18 - en-valid-qa19 - en-valid-qa2 - en-valid-qa20 - en-valid-qa3 - en-valid-qa4 - en-valid-qa5 - en-valid-qa6 - en-valid-qa7 - en-valid-qa8 - en-valid-qa9 - hn-10k-qa1 - hn-10k-qa10 - hn-10k-qa11 - hn-10k-qa12 - hn-10k-qa13 - hn-10k-qa14 - hn-10k-qa15 - hn-10k-qa16 - hn-10k-qa17 - hn-10k-qa18 - hn-10k-qa19 - hn-10k-qa2 - hn-10k-qa20 - hn-10k-qa3 - hn-10k-qa4 - hn-10k-qa5 - hn-10k-qa6 - hn-10k-qa7 - hn-10k-qa8 - hn-10k-qa9 - hn-qa1 - hn-qa10 - hn-qa11 - hn-qa12 - hn-qa13 - hn-qa14 - hn-qa15 - hn-qa16 - hn-qa17 - hn-qa18 - hn-qa19 - hn-qa2 - hn-qa20 - hn-qa3 - hn-qa4 - hn-qa5 - hn-qa6 - hn-qa7 - hn-qa8 - hn-qa9 - shuffled-10k-qa1 - shuffled-10k-qa10 - shuffled-10k-qa11 - shuffled-10k-qa12 - shuffled-10k-qa13 - shuffled-10k-qa14 - shuffled-10k-qa15 - shuffled-10k-qa16 - shuffled-10k-qa17 - shuffled-10k-qa18 - shuffled-10k-qa19 - shuffled-10k-qa2 - shuffled-10k-qa20 - shuffled-10k-qa3 - shuffled-10k-qa4 - shuffled-10k-qa5 - shuffled-10k-qa6 - shuffled-10k-qa7 - shuffled-10k-qa8 - shuffled-10k-qa9 - shuffled-qa1 - shuffled-qa10 - shuffled-qa11 - shuffled-qa12 - shuffled-qa13 - shuffled-qa14 - shuffled-qa15 - shuffled-qa16 - shuffled-qa17 - shuffled-qa18 - shuffled-qa19 - shuffled-qa2 - shuffled-qa20 - shuffled-qa3 - shuffled-qa4 - shuffled-qa5 - shuffled-qa6 - shuffled-qa7 - shuffled-qa8 - shuffled-qa9 tags: - chained-qa dataset_info: - config_name: en-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165386 num_examples: 200 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 330903 - config_name: en-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 302888 num_examples: 200 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 609519 - config_name: en-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 887756 num_examples: 200 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 1770943 - config_name: en-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 205510 num_examples: 1000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 410944 - config_name: en-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 337349 num_examples: 200 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 687806 - config_name: en-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 173053 num_examples: 200 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 345302 - config_name: en-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 224778 num_examples: 200 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 440290 - config_name: en-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 212517 num_examples: 200 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 428761 - config_name: en-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 168350 num_examples: 200 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 336598 - config_name: en-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170257 num_examples: 200 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 340929 - config_name: en-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 178560 num_examples: 200 - name: test num_bytes: 178840 num_examples: 200 download_size: 15719851 dataset_size: 357400 - config_name: en-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 185600 num_examples: 200 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 371129 - config_name: en-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 190556 num_examples: 200 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 381040 - config_name: en-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 234355 num_examples: 200 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 467559 - config_name: en-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 163728 num_examples: 250 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 327537 - config_name: en-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 456374 num_examples: 1000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 912622 - config_name: en-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 103636 num_examples: 125 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 207254 - config_name: en-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 162875 num_examples: 198 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 324141 - config_name: en-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 404536 num_examples: 1000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 809025 - config_name: en-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 115812 num_examples: 94 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 231675 - config_name: hn-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 168605 num_examples: 200 - name: test num_bytes: 168572 num_examples: 200 download_size: 15719851 dataset_size: 337177 - config_name: hn-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 296391 num_examples: 200 - name: test num_bytes: 288429 num_examples: 200 download_size: 15719851 dataset_size: 584820 - config_name: hn-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 842184 num_examples: 167 - name: test num_bytes: 808460 num_examples: 167 download_size: 15719851 dataset_size: 1650644 - config_name: hn-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 231303 num_examples: 1000 - name: test num_bytes: 231230 num_examples: 1000 download_size: 15719851 dataset_size: 462533 - config_name: hn-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 320859 num_examples: 200 - name: test num_bytes: 315396 num_examples: 200 download_size: 15719851 dataset_size: 636255 - config_name: hn-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170796 num_examples: 200 - name: test num_bytes: 171360 num_examples: 200 download_size: 15719851 dataset_size: 342156 - config_name: hn-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 206981 num_examples: 200 - name: test num_bytes: 208080 num_examples: 200 download_size: 15719851 dataset_size: 415061 - config_name: hn-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 211584 num_examples: 200 - name: test num_bytes: 222232 num_examples: 200 download_size: 15719851 dataset_size: 433816 - config_name: hn-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 187718 num_examples: 200 - name: test num_bytes: 187341 num_examples: 200 download_size: 15719851 dataset_size: 375059 - config_name: hn-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 183583 num_examples: 200 - name: test num_bytes: 182932 num_examples: 200 download_size: 15719851 dataset_size: 366515 - config_name: hn-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 179698 num_examples: 200 - name: test num_bytes: 180461 num_examples: 200 download_size: 15719851 dataset_size: 360159 - config_name: hn-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 187731 num_examples: 200 - name: test num_bytes: 187954 num_examples: 200 download_size: 15719851 dataset_size: 375685 - config_name: hn-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 191395 num_examples: 125 - name: test num_bytes: 191747 num_examples: 125 download_size: 15719851 dataset_size: 383142 - config_name: hn-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 240659 num_examples: 200 - name: test num_bytes: 240436 num_examples: 200 download_size: 15719851 dataset_size: 481095 - config_name: hn-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170358 num_examples: 250 - name: test num_bytes: 170259 num_examples: 250 download_size: 15719851 dataset_size: 340617 - config_name: hn-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 523093 num_examples: 1000 - name: test num_bytes: 523032 num_examples: 1000 download_size: 15719851 dataset_size: 1046125 - config_name: hn-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 103878 num_examples: 125 - name: test num_bytes: 104061 num_examples: 125 download_size: 15719851 dataset_size: 207939 - config_name: hn-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 173056 num_examples: 198 - name: test num_bytes: 176824 num_examples: 198 download_size: 15719851 dataset_size: 349880 - config_name: hn-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 470225 num_examples: 1000 - name: test num_bytes: 470479 num_examples: 1000 download_size: 15719851 dataset_size: 940704 - config_name: hn-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 115021 num_examples: 93 - name: test num_bytes: 115088 num_examples: 94 download_size: 15719851 dataset_size: 230109 - config_name: en-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1654288 num_examples: 2000 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 1819805 - config_name: en-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3062580 num_examples: 2000 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 3369211 - config_name: en-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8921215 num_examples: 2000 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 9804402 - config_name: en-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2055105 num_examples: 10000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 2260539 - config_name: en-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3592157 num_examples: 2000 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 3942614 - config_name: en-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1726716 num_examples: 2000 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 1898965 - config_name: en-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2228087 num_examples: 2000 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 2443599 - config_name: en-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2141880 num_examples: 2000 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 2358124 - config_name: en-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1681213 num_examples: 2000 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 1849461 - config_name: en-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1707675 num_examples: 2000 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 1878347 - config_name: en-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1786179 num_examples: 2000 - name: test num_bytes: 178840 num_examples: 200 download_size: 15719851 dataset_size: 1965019 - config_name: en-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1854745 num_examples: 2000 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 2040274 - config_name: en-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1903149 num_examples: 2000 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 2093633 - config_name: en-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2321511 num_examples: 2000 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 2554715 - config_name: en-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1637398 num_examples: 2500 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 1801207 - config_name: en-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4562844 num_examples: 10000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 5019092 - config_name: en-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1034333 num_examples: 1250 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 1137951 - config_name: en-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1641650 num_examples: 1978 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 1802916 - config_name: en-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4045086 num_examples: 10000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 4449575 - config_name: en-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1157351 num_examples: 933 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 1273214 - config_name: en-valid-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 148887 num_examples: 180 - name: test num_bytes: 165517 num_examples: 200 - name: validation num_bytes: 16539 num_examples: 20 download_size: 15719851 dataset_size: 330943 - config_name: en-valid-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 275106 num_examples: 180 - name: test num_bytes: 306631 num_examples: 200 - name: validation num_bytes: 27822 num_examples: 20 download_size: 15719851 dataset_size: 609559 - config_name: en-valid-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 794565 num_examples: 180 - name: test num_bytes: 883187 num_examples: 200 - name: validation num_bytes: 93231 num_examples: 20 download_size: 15719851 dataset_size: 1770983 - config_name: en-valid-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 184992 num_examples: 900 - name: test num_bytes: 205434 num_examples: 1000 - name: validation num_bytes: 20558 num_examples: 100 download_size: 15719851 dataset_size: 410984 - config_name: en-valid-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 305472 num_examples: 180 - name: test num_bytes: 350457 num_examples: 200 - name: validation num_bytes: 31917 num_examples: 20 download_size: 15719851 dataset_size: 687846 - config_name: en-valid-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 155845 num_examples: 180 - name: test num_bytes: 172249 num_examples: 200 - name: validation num_bytes: 17248 num_examples: 20 download_size: 15719851 dataset_size: 345342 - config_name: en-valid-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 203642 num_examples: 180 - name: test num_bytes: 215512 num_examples: 200 - name: validation num_bytes: 21176 num_examples: 20 download_size: 15719851 dataset_size: 440330 - config_name: en-valid-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 191599 num_examples: 180 - name: test num_bytes: 216244 num_examples: 200 - name: validation num_bytes: 20958 num_examples: 20 download_size: 15719851 dataset_size: 428801 - config_name: en-valid-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 151458 num_examples: 180 - name: test num_bytes: 168248 num_examples: 200 - name: validation num_bytes: 16932 num_examples: 20 download_size: 15719851 dataset_size: 336638 - config_name: en-valid-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 153240 num_examples: 180 - name: test num_bytes: 170672 num_examples: 200 - name: validation num_bytes: 17057 num_examples: 20 download_size: 15719851 dataset_size: 340969 - config_name: en-valid-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 160701 num_examples: 180 - name: test num_bytes: 178840 num_examples: 200 - name: validation num_bytes: 17899 num_examples: 20 download_size: 15719851 dataset_size: 357440 - config_name: en-valid-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 167031 num_examples: 180 - name: test num_bytes: 185529 num_examples: 200 - name: validation num_bytes: 18609 num_examples: 20 download_size: 15719851 dataset_size: 371169 - config_name: en-valid-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 171527 num_examples: 180 - name: test num_bytes: 190484 num_examples: 200 - name: validation num_bytes: 19069 num_examples: 20 download_size: 15719851 dataset_size: 381080 - config_name: en-valid-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 210650 num_examples: 180 - name: test num_bytes: 233204 num_examples: 200 - name: validation num_bytes: 23745 num_examples: 20 download_size: 15719851 dataset_size: 467599 - config_name: en-valid-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 147356 num_examples: 225 - name: test num_bytes: 163809 num_examples: 250 - name: validation num_bytes: 16412 num_examples: 25 download_size: 15719851 dataset_size: 327577 - config_name: en-valid-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 410711 num_examples: 900 - name: test num_bytes: 456248 num_examples: 1000 - name: validation num_bytes: 45703 num_examples: 100 download_size: 15719851 dataset_size: 912662 - config_name: en-valid-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 93596 num_examples: 113 - name: test num_bytes: 103618 num_examples: 125 - name: validation num_bytes: 10080 num_examples: 12 download_size: 15719851 dataset_size: 207294 - config_name: en-valid-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 147338 num_examples: 179 - name: test num_bytes: 161266 num_examples: 199 - name: validation num_bytes: 15577 num_examples: 19 download_size: 15719851 dataset_size: 324181 - config_name: en-valid-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 364090 num_examples: 900 - name: test num_bytes: 404489 num_examples: 1000 - name: validation num_bytes: 40486 num_examples: 100 download_size: 15719851 dataset_size: 809065 - config_name: en-valid-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 104706 num_examples: 85 - name: test num_bytes: 115863 num_examples: 93 - name: validation num_bytes: 11146 num_examples: 9 download_size: 15719851 dataset_size: 231715 - config_name: en-valid-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1488751 num_examples: 1800 - name: test num_bytes: 165517 num_examples: 200 - name: validation num_bytes: 165577 num_examples: 200 download_size: 15719851 dataset_size: 1819845 - config_name: en-valid-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2746462 num_examples: 1800 - name: test num_bytes: 306631 num_examples: 200 - name: validation num_bytes: 316158 num_examples: 200 download_size: 15719851 dataset_size: 3369251 - config_name: en-valid-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8021847 num_examples: 1800 - name: test num_bytes: 883187 num_examples: 200 - name: validation num_bytes: 899408 num_examples: 200 download_size: 15719851 dataset_size: 9804442 - config_name: en-valid-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1849497 num_examples: 9000 - name: test num_bytes: 205434 num_examples: 1000 - name: validation num_bytes: 205648 num_examples: 1000 download_size: 15719851 dataset_size: 2260579 - config_name: en-valid-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3234186 num_examples: 1800 - name: test num_bytes: 350457 num_examples: 200 - name: validation num_bytes: 358011 num_examples: 200 download_size: 15719851 dataset_size: 3942654 - config_name: en-valid-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1553957 num_examples: 1800 - name: test num_bytes: 172249 num_examples: 200 - name: validation num_bytes: 172799 num_examples: 200 download_size: 15719851 dataset_size: 1899005 - config_name: en-valid-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2003820 num_examples: 1800 - name: test num_bytes: 215512 num_examples: 200 - name: validation num_bytes: 224307 num_examples: 200 download_size: 15719851 dataset_size: 2443639 - config_name: en-valid-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1926339 num_examples: 1800 - name: test num_bytes: 216244 num_examples: 200 - name: validation num_bytes: 215581 num_examples: 200 download_size: 15719851 dataset_size: 2358164 - config_name: en-valid-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1512917 num_examples: 1800 - name: test num_bytes: 168248 num_examples: 200 - name: validation num_bytes: 168336 num_examples: 200 download_size: 15719851 dataset_size: 1849501 - config_name: en-valid-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1536416 num_examples: 1800 - name: test num_bytes: 170672 num_examples: 200 - name: validation num_bytes: 171299 num_examples: 200 download_size: 15719851 dataset_size: 1878387 - config_name: en-valid-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1607505 num_examples: 1800 - name: test num_bytes: 178840 num_examples: 200 - name: validation num_bytes: 178714 num_examples: 200 download_size: 15719851 dataset_size: 1965059 - config_name: en-valid-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1669198 num_examples: 1800 - name: test num_bytes: 185529 num_examples: 200 - name: validation num_bytes: 185587 num_examples: 200 download_size: 15719851 dataset_size: 2040314 - config_name: en-valid-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1712558 num_examples: 1800 - name: test num_bytes: 190484 num_examples: 200 - name: validation num_bytes: 190631 num_examples: 200 download_size: 15719851 dataset_size: 2093673 - config_name: en-valid-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2091491 num_examples: 1800 - name: test num_bytes: 233204 num_examples: 200 - name: validation num_bytes: 230060 num_examples: 200 download_size: 15719851 dataset_size: 2554755 - config_name: en-valid-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1473615 num_examples: 2250 - name: test num_bytes: 163809 num_examples: 250 - name: validation num_bytes: 163823 num_examples: 250 download_size: 15719851 dataset_size: 1801247 - config_name: en-valid-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4106444 num_examples: 9000 - name: test num_bytes: 456248 num_examples: 1000 - name: validation num_bytes: 456440 num_examples: 1000 download_size: 15719851 dataset_size: 5019132 - config_name: en-valid-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 930465 num_examples: 1125 - name: test num_bytes: 103618 num_examples: 125 - name: validation num_bytes: 103908 num_examples: 125 download_size: 15719851 dataset_size: 1137991 - config_name: en-valid-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1477467 num_examples: 1781 - name: test num_bytes: 161266 num_examples: 199 - name: validation num_bytes: 164223 num_examples: 197 download_size: 15719851 dataset_size: 1802956 - config_name: en-valid-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3640527 num_examples: 9000 - name: test num_bytes: 404489 num_examples: 1000 - name: validation num_bytes: 404599 num_examples: 1000 download_size: 15719851 dataset_size: 4449615 - config_name: en-valid-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1041856 num_examples: 840 - name: test num_bytes: 115863 num_examples: 93 - name: validation num_bytes: 115535 num_examples: 93 download_size: 15719851 dataset_size: 1273254 - config_name: hn-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1684003 num_examples: 2000 - name: test num_bytes: 168572 num_examples: 200 download_size: 15719851 dataset_size: 1852575 - config_name: hn-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2934642 num_examples: 2000 - name: test num_bytes: 288429 num_examples: 200 download_size: 15719851 dataset_size: 3223071 - config_name: hn-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8440008 num_examples: 1667 - name: test num_bytes: 808460 num_examples: 167 download_size: 15719851 dataset_size: 9248468 - config_name: hn-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2312075 num_examples: 10000 - name: test num_bytes: 231230 num_examples: 1000 download_size: 15719851 dataset_size: 2543305 - config_name: hn-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3301271 num_examples: 2000 - name: test num_bytes: 315396 num_examples: 200 download_size: 15719851 dataset_size: 3616667 - config_name: hn-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1703863 num_examples: 2000 - name: test num_bytes: 171360 num_examples: 200 download_size: 15719851 dataset_size: 1875223 - config_name: hn-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2091460 num_examples: 2000 - name: test num_bytes: 208080 num_examples: 200 download_size: 15719851 dataset_size: 2299540 - config_name: hn-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2178277 num_examples: 2000 - name: test num_bytes: 222232 num_examples: 200 download_size: 15719851 dataset_size: 2400509 - config_name: hn-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1874753 num_examples: 2000 - name: test num_bytes: 187341 num_examples: 200 download_size: 15719851 dataset_size: 2062094 - config_name: hn-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1830698 num_examples: 2000 - name: test num_bytes: 182932 num_examples: 200 download_size: 15719851 dataset_size: 2013630 - config_name: hn-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1798057 num_examples: 2000 - name: test num_bytes: 180461 num_examples: 200 download_size: 15719851 dataset_size: 1978518 - config_name: hn-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1879776 num_examples: 2000 - name: test num_bytes: 187954 num_examples: 200 download_size: 15719851 dataset_size: 2067730 - config_name: hn-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1915482 num_examples: 1250 - name: test num_bytes: 191747 num_examples: 125 download_size: 15719851 dataset_size: 2107229 - config_name: hn-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2392212 num_examples: 2000 - name: test num_bytes: 240436 num_examples: 200 download_size: 15719851 dataset_size: 2632648 - config_name: hn-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1702512 num_examples: 2500 - name: test num_bytes: 170259 num_examples: 250 download_size: 15719851 dataset_size: 1872771 - config_name: hn-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 5229983 num_examples: 10000 - name: test num_bytes: 523032 num_examples: 1000 download_size: 15719851 dataset_size: 5753015 - config_name: hn-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1039729 num_examples: 1250 - name: test num_bytes: 104061 num_examples: 125 download_size: 15719851 dataset_size: 1143790 - config_name: hn-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1738458 num_examples: 1977 - name: test num_bytes: 176824 num_examples: 198 download_size: 15719851 dataset_size: 1915282 - config_name: hn-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4702044 num_examples: 10000 - name: test num_bytes: 470479 num_examples: 1000 download_size: 15719851 dataset_size: 5172523 - config_name: hn-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1147599 num_examples: 934 - name: test num_bytes: 115088 num_examples: 94 download_size: 15719851 dataset_size: 1262687 - config_name: shuffled-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165386 num_examples: 200 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 330903 - config_name: shuffled-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 302888 num_examples: 200 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 609519 - config_name: shuffled-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 887756 num_examples: 200 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 1770943 - config_name: shuffled-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 205510 num_examples: 1000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 410944 - config_name: shuffled-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 337349 num_examples: 200 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 687806 - config_name: shuffled-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 173053 num_examples: 200 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 345302 - config_name: shuffled-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 224778 num_examples: 200 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 440290 - config_name: shuffled-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 212517 num_examples: 200 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 428761 - config_name: shuffled-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 168350 num_examples: 200 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 336598 - config_name: shuffled-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170257 num_examples: 200 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 340929 - config_name: shuffled-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 178083 num_examples: 200 - name: test num_bytes: 178313 num_examples: 200 download_size: 15719851 dataset_size: 356396 - config_name: shuffled-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 185600 num_examples: 200 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 371129 - config_name: shuffled-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 190556 num_examples: 200 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 381040 - config_name: shuffled-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 234355 num_examples: 200 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 467559 - config_name: shuffled-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 163728 num_examples: 250 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 327537 - config_name: shuffled-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 456374 num_examples: 1000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 912622 - config_name: shuffled-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 103636 num_examples: 125 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 207254 - config_name: shuffled-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 162875 num_examples: 198 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 324141 - config_name: shuffled-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 404536 num_examples: 1000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 809025 - config_name: shuffled-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 115812 num_examples: 94 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 231675 - config_name: shuffled-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1654288 num_examples: 2000 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 1819805 - config_name: shuffled-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3062580 num_examples: 2000 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 3369211 - config_name: shuffled-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8921215 num_examples: 2000 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 9804402 - config_name: shuffled-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2055105 num_examples: 10000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 2260539 - config_name: shuffled-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3592157 num_examples: 2000 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 3942614 - config_name: shuffled-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1726716 num_examples: 2000 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 1898965 - config_name: shuffled-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2228087 num_examples: 2000 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 2443599 - config_name: shuffled-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2141880 num_examples: 2000 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 2358124 - config_name: shuffled-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1681213 num_examples: 2000 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 1849461 - config_name: shuffled-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1707675 num_examples: 2000 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 1878347 - config_name: shuffled-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1781176 num_examples: 2000 - name: test num_bytes: 178313 num_examples: 200 download_size: 15719851 dataset_size: 1959489 - config_name: shuffled-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1854745 num_examples: 2000 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 2040274 - config_name: shuffled-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1903149 num_examples: 2000 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 2093633 - config_name: shuffled-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2321511 num_examples: 2000 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 2554715 - config_name: shuffled-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1637398 num_examples: 2500 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 1801207 - config_name: shuffled-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4562844 num_examples: 10000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 5019092 - config_name: shuffled-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1034333 num_examples: 1250 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 1137951 - config_name: shuffled-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1641650 num_examples: 1978 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 1802916 - config_name: shuffled-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4045086 num_examples: 10000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 4449575 - config_name: shuffled-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1157351 num_examples: 933 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 1273214 --- # Dataset Card for bAbi QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:[The bAbI project](https://research.fb.com/downloads/babi/) - Repository: - Paper: [arXiv Paper](https://arxiv.org/pdf/1502.05698.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. The aim is to classify these tasks into skill sets,so that researchers can identify (and then rectify) the failings of their systems. ### Supported Tasks and Leaderboards The dataset supports a set of 20 proxy story-based question answering tasks for various "types" in English and Hindi. The tasks are: \|task_no\|task_name\| \|----\|------------\| \|qa1 \|single-supporting-fact\| \|qa2 \|two-supporting-facts\| \|qa3 \|three-supporting-facts\| \|qa4 \|two-arg-relations\| \|qa5 \|three-arg-relations\| \|qa6 \|yes-no-questions\| \|qa7 \|counting\| \|qa8 \|lists-sets\| \|qa9 \|simple-negation\| \|qa10\| indefinite-knowledge\| \|qa11\| basic-coreference\| \|qa12\| conjunction\| \|qa13\| compound-coreference\| \|qa14\| time-reasoning\| \|qa15\| basic-deduction\| \|qa16\| basic-induction\| \|qa17\| positional-reasoning\| \|qa18\| size-reasoning\| \|qa19\| path-finding\| \|qa20\| agents-motivations\| The "types" are are: - `en` - the tasks in English, readable by humans. - `hn` - the tasks in Hindi, readable by humans. - `shuffled` - the same tasks with shuffled letters so they are not readable by humans, and for existing parsers and taggers cannot be used in a straight-forward fashion to leverage extra resources-- in this case the learner is more forced to rely on the given training data. This mimics a learner being first presented a language and having to learn from scratch. - `en-10k`, `shuffled-10k` and `hn-10k` - the same tasks in the three formats, but with 10,000 training examples, rather than 1000 training examples. - `en-valid` and `en-valid-10k` - are the same as `en` and `en10k` except the train sets have been conveniently split into train and valid portions (90% and 10% split). To get a particular dataset, use `load_dataset('babi_qa',type=f'{type}',task_no=f'{task_no}')` where `type` is one of the types, and `task_no` is one of the task numbers. For example, `load_dataset('babi_qa', type='en', task_no='qa1')`. ### Languages ## Dataset Structure ### Data Instances An instance from the `en-qa1` config's `train` split: ``` {'story': {'answer': ['', '', 'bathroom', '', '', 'hallway', '', '', 'hallway', '', '', 'office', '', '', 'bathroom'], 'id': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'], 'supporting_ids': [[], [], ['1'], [], [], ['4'], [], [], ['4'], [], [], ['11'], [], [], ['8']], 'text': ['Mary moved to the bathroom.', 'John went to the hallway.', 'Where is Mary?', 'Daniel went back to the hallway.', 'Sandra moved to the garden.', 'Where is Daniel?', 'John moved to the office.', 'Sandra journeyed to the bathroom.', 'Where is Daniel?', 'Mary moved to the hallway.', 'Daniel travelled to the office.', 'Where is Daniel?', 'John went back to the garden.', 'John moved to the bedroom.', 'Where is Sandra?'], 'type': [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1]}} ``` ### Data Fields - `story`: a dictionary feature containing: - `id`: a `string` feature, which denotes the line number in the example. - `type`: a classification label, with possible values including `context`, `question`, denoting whether the text is context or a question. - `text`: a `string` feature the text present, whether it is a question or context. - `supporting_ids`: a `list` of `string` features containing the line numbers of the lines in the example which support the answer. - `answer`: a `string` feature containing the answer to the question, or an empty string if the `type`s is not `question`. ### Data Splits The splits and corresponding sizes are: \| \| train \| test \| validation \| \|-------------------\|---------\|--------\|--------------\| \| en-qa1 \| 200 \| 200 \| - \| \| en-qa2 \| 200 \| 200 \| - \| \| en-qa3 \| 200 \| 200 \| - \| \| en-qa4 \| 1000 \| 1000 \| - \| \| en-qa5 \| 200 \| 200 \| - \| \| en-qa6 \| 200 \| 200 \| - \| \| en-qa7 \| 200 \| 200 \| - \| \| en-qa8 \| 200 \| 200 \| - \| \| en-qa9 \| 200 \| 200 \| - \| \| en-qa10 \| 200 \| 200 \| - \| \| en-qa11 \| 200 \| 200 \| - \| \| en-qa12 \| 200 \| 200 \| - \| \| en-qa13 \| 200 \| 200 \| - \| \| en-qa14 \| 200 \| 200 \| - \| \| en-qa15 \| 250 \| 250 \| - \| \| en-qa16 \| 1000 \| 1000 \| - \| \| en-qa17 \| 125 \| 125 \| - \| \| en-qa18 \| 198 \| 199 \| - \| \| en-qa19 \| 1000 \| 1000 \| - \| \| en-qa20 \| 94 \| 93 \| - \| \| en-10k-qa1 \| 2000 \| 200 \| - \| \| en-10k-qa2 \| 2000 \| 200 \| - \| \| en-10k-qa3 \| 2000 \| 200 \| - \| \| en-10k-qa4 \| 10000 \| 1000 \| - \| \| en-10k-qa5 \| 2000 \| 200 \| - \| \| en-10k-qa6 \| 2000 \| 200 \| - \| \| en-10k-qa7 \| 2000 \| 200 \| - \| \| en-10k-qa8 \| 2000 \| 200 \| - \| \| en-10k-qa9 \| 2000 \| 200 \| - \| \| en-10k-qa10 \| 2000 \| 200 \| - \| \| en-10k-qa11 \| 2000 \| 200 \| - \| \| en-10k-qa12 \| 2000 \| 200 \| - \| \| en-10k-qa13 \| 2000 \| 200 \| - \| \| en-10k-qa14 \| 2000 \| 200 \| - \| \| en-10k-qa15 \| 2500 \| 250 \| - \| \| en-10k-qa16 \| 10000 \| 1000 \| - \| \| en-10k-qa17 \| 1250 \| 125 \| - \| \| en-10k-qa18 \| 1978 \| 199 \| - \| \| en-10k-qa19 \| 10000 \| 1000 \| - \| \| en-10k-qa20 \| 933 \| 93 \| - \| \| en-valid-qa1 \| 180 \| 200 \| 20 \| \| en-valid-qa2 \| 180 \| 200 \| 20 \| \| en-valid-qa3 \| 180 \| 200 \| 20 \| \| en-valid-qa4 \| 900 \| 1000 \| 100 \| \| en-valid-qa5 \| 180 \| 200 \| 20 \| \| en-valid-qa6 \| 180 \| 200 \| 20 \| \| en-valid-qa7 \| 180 \| 200 \| 20 \| \| en-valid-qa8 \| 180 \| 200 \| 20 \| \| en-valid-qa9 \| 180 \| 200 \| 20 \| \| en-valid-qa10 \| 180 \| 200 \| 20 \| \| en-valid-qa11 \| 180 \| 200 \| 20 \| \| en-valid-qa12 \| 180 \| 200 \| 20 \| \| en-valid-qa13 \| 180 \| 200 \| 20 \| \| en-valid-qa14 \| 180 \| 200 \| 20 \| \| en-valid-qa15 \| 225 \| 250 \| 25 \| \| en-valid-qa16 \| 900 \| 1000 \| 100 \| \| en-valid-qa17 \| 113 \| 125 \| 12 \| \| en-valid-qa18 \| 179 \| 199 \| 19 \| \| en-valid-qa19 \| 900 \| 1000 \| 100 \| \| en-valid-qa20 \| 85 \| 93 \| 9 \| \| en-valid-10k-qa1 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa2 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa3 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa4 \| 9000 \| 1000 \| 1000 \| \| en-valid-10k-qa5 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa6 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa7 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa8 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa9 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa10 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa11 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa12 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa13 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa14 \| 1800 \| 200 \| 200 \| \| en-valid-10k-qa15 \| 2250 \| 250 \| 250 \| \| en-valid-10k-qa16 \| 9000 \| 1000 \| 1000 \| \| en-valid-10k-qa17 \| 1125 \| 125 \| 125 \| \| en-valid-10k-qa18 \| 1781 \| 199 \| 197 \| \| en-valid-10k-qa19 \| 9000 \| 1000 \| 1000 \| \| en-valid-10k-qa20 \| 840 \| 93 \| 93 \| \| hn-qa1 \| 200 \| 200 \| - \| \| hn-qa2 \| 200 \| 200 \| - \| \| hn-qa3 \| 167 \| 167 \| - \| \| hn-qa4 \| 1000 \| 1000 \| - \| \| hn-qa5 \| 200 \| 200 \| - \| \| hn-qa6 \| 200 \| 200 \| - \| \| hn-qa7 \| 200 \| 200 \| - \| \| hn-qa8 \| 200 \| 200 \| - \| \| hn-qa9 \| 200 \| 200 \| - \| \| hn-qa10 \| 200 \| 200 \| - \| \| hn-qa11 \| 200 \| 200 \| - \| \| hn-qa12 \| 200 \| 200 \| - \| \| hn-qa13 \| 125 \| 125 \| - \| \| hn-qa14 \| 200 \| 200 \| - \| \| hn-qa15 \| 250 \| 250 \| - \| \| hn-qa16 \| 1000 \| 1000 \| - \| \| hn-qa17 \| 125 \| 125 \| - \| \| hn-qa18 \| 198 \| 198 \| - \| \| hn-qa19 \| 1000 \| 1000 \| - \| \| hn-qa20 \| 93 \| 94 \| - \| \| hn-10k-qa1 \| 2000 \| 200 \| - \| \| hn-10k-qa2 \| 2000 \| 200 \| - \| \| hn-10k-qa3 \| 1667 \| 167 \| - \| \| hn-10k-qa4 \| 10000 \| 1000 \| - \| \| hn-10k-qa5 \| 2000 \| 200 \| - \| \| hn-10k-qa6 \| 2000 \| 200 \| - \| \| hn-10k-qa7 \| 2000 \| 200 \| - \| \| hn-10k-qa8 \| 2000 \| 200 \| - \| \| hn-10k-qa9 \| 2000 \| 200 \| - \| \| hn-10k-qa10 \| 2000 \| 200 \| - \| \| hn-10k-qa11 \| 2000 \| 200 \| - \| \| hn-10k-qa12 \| 2000 \| 200 \| - \| \| hn-10k-qa13 \| 1250 \| 125 \| - \| \| hn-10k-qa14 \| 2000 \| 200 \| - \| \| hn-10k-qa15 \| 2500 \| 250 \| - \| \| hn-10k-qa16 \| 10000 \| 1000 \| - \| \| hn-10k-qa17 \| 1250 \| 125 \| - \| \| hn-10k-qa18 \| 1977 \| 198 \| - \| \| hn-10k-qa19 \| 10000 \| 1000 \| - \| \| hn-10k-qa20 \| 934 \| 94 \| - \| \| shuffled-qa1 \| 200 \| 200 \| - \| \| shuffled-qa2 \| 200 \| 200 \| - \| \| shuffled-qa3 \| 200 \| 200 \| - \| \| shuffled-qa4 \| 1000 \| 1000 \| - \| \| shuffled-qa5 \| 200 \| 200 \| - \| \| shuffled-qa6 \| 200 \| 200 \| - \| \| shuffled-qa7 \| 200 \| 200 \| - \| \| shuffled-qa8 \| 200 \| 200 \| - \| \| shuffled-qa9 \| 200 \| 200 \| - \| \| shuffled-qa10 \| 200 \| 200 \| - \| \| shuffled-qa11 \| 200 \| 200 \| - \| \| shuffled-qa12 \| 200 \| 200 \| - \| \| shuffled-qa13 \| 200 \| 200 \| - \| \| shuffled-qa14 \| 200 \| 200 \| - \| \| shuffled-qa15 \| 250 \| 250 \| - \| \| shuffled-qa16 \| 1000 \| 1000 \| - \| \| shuffled-qa17 \| 125 \| 125 \| - \| \| shuffled-qa18 \| 198 \| 199 \| - \| \| shuffled-qa19 \| 1000 \| 1000 \| - \| \| shuffled-qa20 \| 94 \| 93 \| - \| \| shuffled-10k-qa1 \| 2000 \| 200 \| - \| \| shuffled-10k-qa2 \| 2000 \| 200 \| - \| \| shuffled-10k-qa3 \| 2000 \| 200 \| - \| \| shuffled-10k-qa4 \| 10000 \| 1000 \| - \| \| shuffled-10k-qa5 \| 2000 \| 200 \| - \| \| shuffled-10k-qa6 \| 2000 \| 200 \| - \| \| shuffled-10k-qa7 \| 2000 \| 200 \| - \| \| shuffled-10k-qa8 \| 2000 \| 200 \| - \| \| shuffled-10k-qa9 \| 2000 \| 200 \| - \| \| shuffled-10k-qa10 \| 2000 \| 200 \| - \| \| shuffled-10k-qa11 \| 2000 \| 200 \| - \| \| shuffled-10k-qa12 \| 2000 \| 200 \| - \| \| shuffled-10k-qa13 \| 2000 \| 200 \| - \| \| shuffled-10k-qa14 \| 2000 \| 200 \| - \| \| shuffled-10k-qa15 \| 2500 \| 250 \| - \| \| shuffled-10k-qa16 \| 10000 \| 1000 \| - \| \| shuffled-10k-qa17 \| 1250 \| 125 \| - \| \| shuffled-10k-qa18 \| 1978 \| 199 \| - \| \| shuffled-10k-qa19 \| 10000 \| 1000 \| - \| \| shuffled-10k-qa20 \| 933 \| 93 \| - \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Code to generate tasks is available on [github](https://github.com/facebook/bAbI-tasks) #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston, at Facebook Research. ### Licensing Information ``` Creative Commons Attribution 3.0 License ``` ### Citation Information ``` @misc{dodge2016evaluating, title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems}, author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston}, year={2016}, eprint={1511.06931}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.
banking77	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - multi-class-classification pretty_name: BANKING77 dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': activate_my_card '1': age_limit '2': apple_pay_or_google_pay '3': atm_support '4': automatic_top_up '5': balance_not_updated_after_bank_transfer '6': balance_not_updated_after_cheque_or_cash_deposit '7': beneficiary_not_allowed '8': cancel_transfer '9': card_about_to_expire '10': card_acceptance '11': card_arrival '12': card_delivery_estimate '13': card_linking '14': card_not_working '15': card_payment_fee_charged '16': card_payment_not_recognised '17': card_payment_wrong_exchange_rate '18': card_swallowed '19': cash_withdrawal_charge '20': cash_withdrawal_not_recognised '21': change_pin '22': compromised_card '23': contactless_not_working '24': country_support '25': declined_card_payment '26': declined_cash_withdrawal '27': declined_transfer '28': direct_debit_payment_not_recognised '29': disposable_card_limits '30': edit_personal_details '31': exchange_charge '32': exchange_rate '33': exchange_via_app '34': extra_charge_on_statement '35': failed_transfer '36': fiat_currency_support '37': get_disposable_virtual_card '38': get_physical_card '39': getting_spare_card '40': getting_virtual_card '41': lost_or_stolen_card '42': lost_or_stolen_phone '43': order_physical_card '44': passcode_forgotten '45': pending_card_payment '46': pending_cash_withdrawal '47': pending_top_up '48': pending_transfer '49': pin_blocked '50': receiving_money '51': Refund_not_showing_up '52': request_refund '53': reverted_card_payment? '54': supported_cards_and_currencies '55': terminate_account '56': top_up_by_bank_transfer_charge '57': top_up_by_card_charge '58': top_up_by_cash_or_cheque '59': top_up_failed '60': top_up_limits '61': top_up_reverted '62': topping_up_by_card '63': transaction_charged_twice '64': transfer_fee_charged '65': transfer_into_account '66': transfer_not_received_by_recipient '67': transfer_timing '68': unable_to_verify_identity '69': verify_my_identity '70': verify_source_of_funds '71': verify_top_up '72': virtual_card_not_working '73': visa_or_mastercard '74': why_verify_identity '75': wrong_amount_of_cash_received '76': wrong_exchange_rate_for_cash_withdrawal splits: - name: train num_bytes: 715036 num_examples: 10003 - name: test num_bytes: 204014 num_examples: 3080 download_size: 1079034 dataset_size: 919050 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for BANKING77 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/PolyAI-LDN/task-specific-datasets) - Repository: [Github](https://github.com/PolyAI-LDN/task-specific-datasets) - Paper: [ArXiv](https://arxiv.org/abs/2003.04807) - Leaderboard: - Point of Contact: ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "banking77" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/PolyAI/banking77">PolyAI/banking77</a>" instead.</p> </div> Dataset composed of online banking queries annotated with their corresponding intents. BANKING77 dataset provides a very fine-grained set of intents in a banking domain. It comprises 13,083 customer service queries labeled with 77 intents. It focuses on fine-grained single-domain intent detection. ### Supported Tasks and Leaderboards Intent classification, intent detection ### Languages English ## Dataset Structure ### Data Instances An example of 'train' looks as follows: ``` { 'label': 11, # integer label corresponding to "card_arrival" intent 'text': 'I am still waiting on my card?' } ``` ### Data Fields - `text`: a string feature. - `label`: One of classification labels (0-76) corresponding to unique intents. Intent names are mapped to `label` in the following way: \| label \| intent (category) \| \|---:\|:-------------------------------------------------\| \| 0 \| activate_my_card \| \| 1 \| age_limit \| \| 2 \| apple_pay_or_google_pay \| \| 3 \| atm_support \| \| 4 \| automatic_top_up \| \| 5 \| balance_not_updated_after_bank_transfer \| \| 6 \| balance_not_updated_after_cheque_or_cash_deposit \| \| 7 \| beneficiary_not_allowed \| \| 8 \| cancel_transfer \| \| 9 \| card_about_to_expire \| \| 10 \| card_acceptance \| \| 11 \| card_arrival \| \| 12 \| card_delivery_estimate \| \| 13 \| card_linking \| \| 14 \| card_not_working \| \| 15 \| card_payment_fee_charged \| \| 16 \| card_payment_not_recognised \| \| 17 \| card_payment_wrong_exchange_rate \| \| 18 \| card_swallowed \| \| 19 \| cash_withdrawal_charge \| \| 20 \| cash_withdrawal_not_recognised \| \| 21 \| change_pin \| \| 22 \| compromised_card \| \| 23 \| contactless_not_working \| \| 24 \| country_support \| \| 25 \| declined_card_payment \| \| 26 \| declined_cash_withdrawal \| \| 27 \| declined_transfer \| \| 28 \| direct_debit_payment_not_recognised \| \| 29 \| disposable_card_limits \| \| 30 \| edit_personal_details \| \| 31 \| exchange_charge \| \| 32 \| exchange_rate \| \| 33 \| exchange_via_app \| \| 34 \| extra_charge_on_statement \| \| 35 \| failed_transfer \| \| 36 \| fiat_currency_support \| \| 37 \| get_disposable_virtual_card \| \| 38 \| get_physical_card \| \| 39 \| getting_spare_card \| \| 40 \| getting_virtual_card \| \| 41 \| lost_or_stolen_card \| \| 42 \| lost_or_stolen_phone \| \| 43 \| order_physical_card \| \| 44 \| passcode_forgotten \| \| 45 \| pending_card_payment \| \| 46 \| pending_cash_withdrawal \| \| 47 \| pending_top_up \| \| 48 \| pending_transfer \| \| 49 \| pin_blocked \| \| 50 \| receiving_money \| \| 51 \| Refund_not_showing_up \| \| 52 \| request_refund \| \| 53 \| reverted_card_payment? \| \| 54 \| supported_cards_and_currencies \| \| 55 \| terminate_account \| \| 56 \| top_up_by_bank_transfer_charge \| \| 57 \| top_up_by_card_charge \| \| 58 \| top_up_by_cash_or_cheque \| \| 59 \| top_up_failed \| \| 60 \| top_up_limits \| \| 61 \| top_up_reverted \| \| 62 \| topping_up_by_card \| \| 63 \| transaction_charged_twice \| \| 64 \| transfer_fee_charged \| \| 65 \| transfer_into_account \| \| 66 \| transfer_not_received_by_recipient \| \| 67 \| transfer_timing \| \| 68 \| unable_to_verify_identity \| \| 69 \| verify_my_identity \| \| 70 \| verify_source_of_funds \| \| 71 \| verify_top_up \| \| 72 \| virtual_card_not_working \| \| 73 \| visa_or_mastercard \| \| 74 \| why_verify_identity \| \| 75 \| wrong_amount_of_cash_received \| \| 76 \| wrong_exchange_rate_for_cash_withdrawal \| ### Data Splits \| Dataset statistics \| Train \| Test \| \| --- \| --- \| --- \| \| Number of examples \| 10 003 \| 3 080 \| \| Average character length \| 59.5 \| 54.2 \| \| Number of intents \| 77 \| 77 \| \| Number of domains \| 1 \| 1 \| ## Dataset Creation ### Curation Rationale Previous intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale multi-domain datasets ([HWU64](https://github.com/xliuhw/NLU-Evaluation-Data) and [CLINC150](https://github.com/clinc/oos-eval)), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered "in the wild". This dataset tries to fill the gap and provides a very fine-grained set of intents in a single-domain i.e. banking. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The dataset does not contain any additional annotations. #### Who are the annotators? [N/A] ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset it to help develop better intent detection systems. Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [PolyAI](https://github.com/PolyAI-LDN) ### Licensing Information Creative Commons Attribution 4.0 International ### Citation Information ``` @inproceedings{Casanueva2020, author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic}, title = {Efficient Intent Detection with Dual Sentence Encoders}, year = {2020}, month = {mar}, note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets}, url = {https://arxiv.org/abs/2003.04807}, booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020} } ``` ### Contributions Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.
bbaw_egyptian	--- annotations_creators: - expert-generated language_creators: - found language: - de - egy - en license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - extended\|wikipedia task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: BbawEgyptian dataset_info: features: - name: transcription dtype: string - name: translation dtype: string - name: hieroglyphs dtype: string splits: - name: train num_bytes: 18546162 num_examples: 100736 download_size: 35348686 dataset_size: 18546162 --- # Dataset Card for "bbaw_egyptian" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://edoc.bbaw.de/frontdoor/index/index/docId/2919](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) - Repository: [Github](https://phiwi.github.io/all.json) - Paper: [Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyph](https://zenodo.org/record/3524924) - Point of Contact: [Philipp Wiesenbach](https://www.cl.uni-heidelberg.de/~wiesenbach/index.html) - Size of downloaded dataset files: 35.65 MB ### Dataset Summary This dataset comprises parallel sentences of hieroglyphic encodings, transcription and translation as used in the paper [Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyph](https://zenodo.org/record/3524924). The data triples are extracted from the [digital corpus of Egyptian texts](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) compiled by the project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache". ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The dataset consists of parallel triples of - `hieroglyphs`: [Encoding of the hieroglyphs with the [Gardiner's sign list](https://en.wikipedia.org/wiki/Gardiner%27s_sign_list) - `transcription`: Transliteration of the above mentioned hieroglyphs with a [transliteration scheme](https://en.wikipedia.org/wiki/Transliteration_of_Ancient_Egyptian) - `translation`: Translation in mostly German language (with some English mixed in) ## Dataset Structure The dataset is not divided into 'train', 'dev' and 'test' splits as it was not built for competitive purposes and we encourage all scientists to use individual partitioning schemes to suit their needs (due to the low resource setting it might be advisable to use cross validation anyway). The only available split 'all' therefore comprises the full 100,708 translation triples, 35,503 of which possess hieroglyphic encodings (the remaining 65,205 triples have empty `hieroglyph` entries). ### Data Instances An example of a data triple looks the following way: ``` { "transcription": "n rḏi̯(.w) gꜣ =j r dbḥ.t m pr-ḥḏ", "translation": "I was not let to suffer lack in the treasury with respect to what was needed;", "hieroglyphs": "D35 D21 -D37 G1&W11 -V32B A1 D21 D46 -D58 V28 -F18 X1 -A2 G17 [? O2 ?]" } ``` Important: Only about a third of the instance actually cover hieroglyphic encodings (the rest is the empty string `""`) as the leftover encodings have not yet been incorporated into the BBAW's project database. ### Data Fields #### plain_text - `transcription`: a `string` feature. - `translation`: a `string` feature. - `hieroglyphs`: a `string` feature. ### Data Splits \| name \|all\| \|----------\|----:\| \|plain_text\|100708\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data source comes from the project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache" which is compiling an extensively annotated digital corpus of Egyptian texts. Their [publication](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) comprises an excerpt of the internal database's contents. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The corpus has not been preprocessed as we encourage every scientist to prepare the corpus to their desired needs. This means, that all textcritic symbols are still included in the transliteration and translation. This concerns the following annotations: - `()`: defective - `[]`: lost - `{}`: surplus - `〈〉`: omitted - `⸢⸣`: damaged - `⸮?`: unclear - `{{}}`: erasure - `(())`: above - `[[]]`: overstrike - `〈〈〉〉`: haplography Their exists a similar sign list for the annotation of the hieroglyphic encoding. If you wish access to this list, please get in contact with the author. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information Source corpus: ``` @misc{OPUS4-2919, title = {Teilauszug der Datenbank des Vorhabens "Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache" vom Januar 2018}, institution = {Akademienvorhaben Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache. Text- und Wissenskultur im alten {\"A}gypten}, type = {other}, year = {2018}, } ``` Translation paper: ``` @article{wiesenbach19, title = {Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs}, author = {Wiesenbach, Philipp and Riezler, Stefan}, journal = {Proceedings of the International Workshop on Spoken Language Translation}, journal-abbrev = {IWSLT}, year = {2019}, url = {https://www.cl.uni-heidelberg.de/statnlpgroup/publications/IWSLT2019_v2.pdf} } ``` ### Contributions Thanks to [@phiwi](https://github.com/phiwi) for adding this dataset.
bbc_hindi_nli	--- annotations_creators: - machine-generated language_creators: - found language: - hi license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|bbc__hindi_news_classification task_categories: - text-classification task_ids: - natural-language-inference pretty_name: BBC Hindi NLI Dataset dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': not-entailment '1': entailment - name: topic dtype: class_label: names: '0': india '1': news '2': international '3': entertainment '4': sport '5': science config_name: bbc hindi nli splits: - name: train num_bytes: 2990080 num_examples: 15552 - name: validation num_bytes: 496808 num_examples: 2580 - name: test num_bytes: 494432 num_examples: 2592 download_size: 3815652 dataset_size: 3981320 --- # Dataset Card for BBC Hindi NLI Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [GitHub](https://github.com/midas-research/hindi-nli-data) - Paper: [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71) - Point of Contact: [GitHub](https://github.com/midas-research/hindi-nli-data) ### Dataset Summary - Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs. - Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic. - Context and Hypothesis is written in Hindi while Entailment_Label is in English. - Entailment_label is of 2 types - entailed and not-entailed. - Dataset can be used to train models for Natural Language Inference tasks in Hindi Language. [More Information Needed] ### Supported Tasks and Leaderboards - Natural Language Inference for Hindi ### Languages Dataset is in Hindi ## Dataset Structure - Data is structured in TSV format. - Train and Test files are in seperate files ### Dataset Instances An example of 'train' looks as follows. ``` {'hypothesis': 'यह खबर की सूचना है\|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'} ``` ### Data Fields - Each row contatins 4 columns - Premise, Hypothesis, Label and Topic. ### Data Splits - Train : 15553 - Valid : 2581 - Test : 2593 ## Dataset Creation - We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems - In this recasting process, we build template hypotheses for each class in the label taxonomy - Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples. - For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71" ### Source Data Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1) #### Initial Data Collection and Normalization - BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia - We processed this dataset to combine two sets of relevant but low prevalence classes. - Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international. - Likewise, we also merged samples from news, business, social, learning english, and institutional as news. - Lastly, we also removed the class multimedia because there were very few samples. #### Who are the source language producers? Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71" ### Annotations #### Annotation process Annotation process has been described in Dataset Creation Section. #### Who are the annotators? Annotation is done automatically. ### Personal and Sensitive Information No Personal and Sensitive Information is mentioned in the Datasets. ## Considerations for Using the Data Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71 ### Discussion of Biases Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71 ### Other Known Limitations No other known limitations ## Additional Information Pls refer to this link: https://github.com/midas-research/hindi-nli-data ### Dataset Curators It is written in the repo : https://github.com/avinsit123/hindi-nli-data that - This corpus can be used freely for research purposes. - The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper. - If interested in commercial use of the corpus, send email to [email protected]. - If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus. - Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications. - Rather than redistributing the corpus, please direct interested parties to this page - Please feel free to send us an email: - with feedback regarding the corpus. - with information on how you have used the corpus. - if interested in having us analyze your data for natural language inference. - if interested in a collaborative research project. ### Licensing Information Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi). Pls contact authors for any information on the dataset. ### Citation Information ``` @inproceedings{uppal-etal-2020-two, title = "Two-Step Classification using Recasted Data for Low Resource Settings", author = "Uppal, Shagun and Gupta, Vivek and Swaminathan, Avinash and Zhang, Haimin and Mahata, Debanjan and Gosangi, Rakesh and Shah, Rajiv Ratn and Stent, Amanda", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.aacl-main.71", pages = "706--719", abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.", } ``` ### Contributions Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.
bc2gm_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Bc2GmCorpus dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-GENE '2': I-GENE config_name: bc2gm_corpus splits: - name: train num_bytes: 6095172 num_examples: 12501 - name: validation num_bytes: 1215951 num_examples: 2501 - name: test num_bytes: 2454621 num_examples: 5001 download_size: 4636753 dataset_size: 9765744 --- # Dataset Card for bc2gm_corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/spyysalo/bc2gm-corpus/) - Repository: [Github](https://github.com/spyysalo/bc2gm-corpus/) - Paper: [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/) - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@mahajandiwakar](https://github.com/mahajandiwakar) for adding this dataset.
beans	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification pretty_name: Beans dataset_info: features: - name: image_file_path dtype: string - name: image dtype: image - name: labels dtype: class_label: names: '0': angular_leaf_spot '1': bean_rust '2': healthy splits: - name: train num_bytes: 382110 num_examples: 1034 - name: validation num_bytes: 49711 num_examples: 133 - name: test num_bytes: 46584 num_examples: 128 download_size: 180024906 dataset_size: 478405 --- # Dataset Card for Beans ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Beans Homepage](https://github.com/AI-Lab-Makerere/ibean/) - Repository: [AI-Lab-Makerere/ibean](https://github.com/AI-Lab-Makerere/ibean/) - Paper: N/A - Leaderboard: N/A - Point of Contact: N/A ### Dataset Summary Beans leaf dataset with images of diseased and health leaves. ### Supported Tasks and Leaderboards - `image-classification`: Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any. ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/0aaa78294d4bf5114f58547e48d91b7826649919505379a167decb629aa92b0a/train/bean_rust/bean_rust_train.109.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x16BAA72A4A8>, 'labels': 1 } ``` ### Data Fields The data instances have the following fields: - `image_file_path`: a `string` filepath to an image. - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ```json { "angular_leaf_spot": 0, "bean_rust": 1, "healthy": 2, } ``` ### Data Splits \| \|train\|validation\|test\| \|-------------\|----:\|---------:\|---:\| \|# of examples\|1034 \|133 \|128 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @ONLINE {beansdata, author="Makerere AI Lab", title="Bean disease dataset", month="January", year="2020", url="https://github.com/AI-Lab-Makerere/ibean/" } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.
best2009	--- annotations_creators: - expert-generated language_creators: - found language: - th license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: [] pretty_name: best2009 tags: - word-tokenization dataset_info: features: - name: fname dtype: string - name: char sequence: string - name: char_type sequence: class_label: names: '0': b_e '1': c '2': d '3': n '4': o '5': p '6': q '7': s '8': s_e '9': t '10': v '11': w - name: is_beginning sequence: class_label: names: '0': neg '1': pos config_name: best2009 splits: - name: train num_bytes: 483129998 num_examples: 148995 - name: test num_bytes: 10498726 num_examples: 2252 download_size: 13891260 dataset_size: 493628724 --- # Dataset Card for `best2009` ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://aiforthai.in.th/ - Repository: https://aiforthai.in.th/corpus.php - Paper: - Leaderboard: - Point of Contact: https://aiforthai.in.th/ ### Dataset Summary `best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly. ### Supported Tasks and Leaderboards word tokenization ### Languages Thai ## Dataset Structure ### Data Instances ``` {'char': ['?', 'ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', '\n'], 'char_type': [4, 1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]} {'char': ['ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ห', 'ม', 'า', 'ย', 'ถ', 'ึ', 'ง', ' ', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ข', 'อ', 'ง', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ซ', 'ึ', '่', 'ง', 'เ', 'ร', 'ี', 'ย', 'น', 'ร', 'ู', '้', 'ม', 'า', 'จ', 'า', 'ก', 'พ', '่', 'อ', 'แ', 'ม', '่', ' ', 'ป', 'ู', '่', 'ย', '่', 'า', 'ต', 'า', 'ย', 'า', 'ย', ' ', 'ญ', 'า', 'ต', 'ิ', 'พ', 'ี', '่', 'น', '้', 'อ', 'ง', ' ', 'ห', 'ร', 'ื', 'อ', 'ผ', 'ู', '้', 'ม', 'ี', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ใ', 'น', 'ห', 'ม', 'ู', '่', 'บ', '้', 'า', 'น', 'ใ', 'น', 'ท', '้', 'อ', 'ง', 'ถ', 'ิ', '่', 'น', 'ต', '่', 'า', 'ง', 'ๆ', '\n'], 'char_type': [1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 5, 3, 1, 10, 1, 1, 10, 1, 5, 1, 1, 10, 1, 1, 10, 9, 1, 1, 1, 1, 10, 1, 1, 9, 10, 1, 5, 1, 10, 9, 1, 11, 1, 10, 1, 1, 1, 10, 9, 1, 10, 1, 10, 1, 1, 9, 1, 11, 1, 9, 5, 1, 10, 9, 1, 9, 10, 1, 10, 1, 10, 1, 5, 1, 10, 1, 10, 1, 10, 9, 1, 9, 1, 1, 5, 3, 1, 10, 1, 3, 10, 9, 1, 10, 1, 1, 10, 1, 1, 10, 9, 11, 1, 3, 1, 10, 9, 1, 9, 10, 1, 11, 1, 1, 9, 1, 1, 1, 10, 9, 1, 1, 9, 10, 1, 7, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]} ``` ### Data Fields - `fname`: file name; also marks if article is articles, news, encyclopedia or novels - `char`: characters - `char_type`: character types as adopted from []() by [deepcut](https://github.com/rkcosmos/deepcut) - `is_beginning`: is beginning of word ### Data Splits \| \| train \| test \| \|-------------------------\|------------\|---------\| \| # lines \| 148,995 \| 2,252 \| \| avg words per line \| 39.05 \| NA \| \| total words \| 5,818,521 \| NA \| \| avg characters per line \| 140.39 \| 202.79 \| \| total characters \| 20,918,132 \| 456,684 \| \| # lines articles \| 16,990 \| NA \| \| # lines encyclopedia \| 50,631 \| NA \| \| # lines novels \| 50,140 \| NA \| \| # lines news \| 31,234 \| NA \| ## Dataset Creation ### Curation Rationale The dataset was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10) by [NECTEC](https://www.nectec.or.th/). ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Respective authors of the articles, news, encyclopedia and novels ### Annotations #### Annotation process Detailed annotation guidelines can be found in `BEST_Guideline_Release1.pdf` as part of the uncompressed files. Word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf) #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information All data are curated from public sources. No personal and sensitive information is expected to be included. ## Considerations for Using the Data ### Social Impact of Dataset - word tokenization dataset from articles, news, encyclopedia and novels ### Discussion of Biases - texts are relatively formal ones from articles, news, encyclopedia and novels. - word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf). ### Other Known Limitations - some tags unrelated to word tokenization (`<NE>` and `<AB>`) are cleaned out. - no word boundary provdied for the test set ## Additional Information ### Dataset Curators [NECTEC](https://www.nectec.or.th/) ### Licensing Information CC-BY-NC-SA 3.0 ### Citation Information Dataset: ``` @inproceedings{kosawat2009best, title={BEST 2009: Thai word segmentation software contest}, author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others}, booktitle={2009 Eighth International Symposium on Natural Language Processing}, pages={83--88}, year={2009}, organization={IEEE} } @inproceedings{boriboon2009best, title={Best corpus development and analysis}, author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit}, booktitle={2009 International Conference on Asian Language Processing}, pages={322--327}, year={2009}, organization={IEEE} } ``` Character type features: ``` @inproceedings{haruechaiyasak2009tlex, title={TLex: Thai lexeme analyser based on the conditional random fields}, author={Haruechaiyasak, Choochart and Kongyoung, Sarawoot}, booktitle={Proceedings of 8th International Symposium on Natural Language Processing}, year={2009} } ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
bianet	--- annotations_creators: - found language_creators: - found language: - en - ku - tr license: - unknown multilinguality: - translation size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: bianet pretty_name: Bianet configs: - en-to-ku - en-to-tr - en_to_ku - en_to_tr - ku-to-tr - ku_to_tr dataset_info: - config_name: en_to_ku features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ku splits: - name: train num_bytes: 1800806 num_examples: 6402 download_size: 622420 dataset_size: 1800806 - config_name: en_to_tr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - tr splits: - name: train num_bytes: 10231043 num_examples: 34770 download_size: 3544116 dataset_size: 10231043 - config_name: ku_to_tr features: - name: id dtype: string - name: translation dtype: translation: languages: - ku - tr splits: - name: train num_bytes: 2086550 num_examples: 7325 download_size: 725227 dataset_size: 2086550 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Bianet](http://opus.nlpl.eu/Bianet.php) - Repository: - Paper: [Ataman, D. (2018) Bianet: A Parallel News Corpus in Turkish, Kurdish and English. In Proceedings of the LREC 2018 Workshop MLP-Moment. pp. 14-17. pdf](http://lrec-conf.org/workshops/lrec2018/W19/pdf/6_W19.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary A parallel news corpus in Turkish, Kurdish and English; Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper. 3 languages, 3 bitexts total number of files: 6 total number of tokens: 2.25M total number of sentence fragments: 0.14M ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC-BY-SA-4.0 ### Citation Information @InProceedings{ATAMAN18.6, author = {Duygu Ataman}, title = {Bianet: A Parallel News Corpus in Turkish, Kurdish and English}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Jinhua Du and Mihael Arcan and Qun Liu and Hitoshi Isahara}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-15-3}, language = {english} } ### Contributions Thanks to [@param087](https://github.com/param087) for adding this dataset.
bible_para	--- annotations_creators: - found language_creators: - found language: - acu - af - agr - ake - am - amu - ar - bg - bsn - cak - ceb - ch - chq - chr - cjp - cni - cop - crp - cs - da - de - dik - dje - djk - dop - ee - el - en - eo - es - et - eu - fi - fr - gbi - gd - gu - gv - he - hi - hr - hu - hy - id - is - it - ja - jak - jiv - kab - kbh - kek - kn - ko - la - lt - lv - mam - mi - ml - mr - my - ne - nhg - nl - 'no' - ojb - pck - pes - pl - plt - pot - ppk - pt - quc - quw - ro - rom - ru - shi - sk - sl - sn - so - sq - sr - ss - sv - syr - te - th - tl - tmh - tr - uk - usp - vi - wal - wo - xh - zh - zu license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: BiblePara dataset_info: - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 17262178 num_examples: 62195 download_size: 5440713 dataset_size: 17262178 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 17536445 num_examples: 62195 download_size: 5470044 dataset_size: 17536445 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 17105724 num_examples: 62191 download_size: 5418998 dataset_size: 17105724 - config_name: en-fi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 17486055 num_examples: 62026 download_size: 5506407 dataset_size: 17486055 - config_name: en-no features: - name: id dtype: string - name: translation dtype: translation: languages: - en - 'no' splits: - name: train num_bytes: 16681323 num_examples: 62107 download_size: 5293164 dataset_size: 16681323 - config_name: en-hi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - hi splits: - name: train num_bytes: 27849361 num_examples: 62073 download_size: 6224765 dataset_size: 27849361 --- # Dataset Card for BiblePara ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://opus.nlpl.eu/bible-uedin.php - Repository: None - Paper: https://link.springer.com/article/10.1007/s10579-014-9287-y - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php E.g. `dataset = load_dataset("bible_para", lang1="fi", lang2="hi")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
big_patent	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: bigpatent pretty_name: Big Patent configs: - a - all - b - c - d - e - f - g - h - y tags: - patent-summarization dataset_info: - config_name: all features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 38367048389 num_examples: 1207222 - name: validation num_bytes: 2115827002 num_examples: 67068 - name: test num_bytes: 2129505280 num_examples: 67072 download_size: 10142923776 dataset_size: 42612380671 - config_name: a features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 5683460620 num_examples: 174134 - name: validation num_bytes: 313324505 num_examples: 9674 - name: test num_bytes: 316633277 num_examples: 9675 download_size: 10142923776 dataset_size: 6313418402 - config_name: b features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4236070976 num_examples: 161520 - name: validation num_bytes: 234425138 num_examples: 8973 - name: test num_bytes: 231538734 num_examples: 8974 download_size: 10142923776 dataset_size: 4702034848 - config_name: c features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4506249306 num_examples: 101042 - name: validation num_bytes: 244684775 num_examples: 5613 - name: test num_bytes: 252566793 num_examples: 5614 download_size: 10142923776 dataset_size: 5003500874 - config_name: d features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 264717412 num_examples: 10164 - name: validation num_bytes: 14560482 num_examples: 565 - name: test num_bytes: 14403430 num_examples: 565 download_size: 10142923776 dataset_size: 293681324 - config_name: e features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 881101433 num_examples: 34443 - name: validation num_bytes: 48646158 num_examples: 1914 - name: test num_bytes: 48586429 num_examples: 1914 download_size: 10142923776 dataset_size: 978334020 - config_name: f features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2146383473 num_examples: 85568 - name: validation num_bytes: 119632631 num_examples: 4754 - name: test num_bytes: 119596303 num_examples: 4754 download_size: 10142923776 dataset_size: 2385612407 - config_name: g features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8877854206 num_examples: 258935 - name: validation num_bytes: 492581177 num_examples: 14385 - name: test num_bytes: 496324853 num_examples: 14386 download_size: 10142923776 dataset_size: 9866760236 - config_name: h features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8075621958 num_examples: 257019 - name: validation num_bytes: 447602356 num_examples: 14279 - name: test num_bytes: 445460513 num_examples: 14279 download_size: 10142923776 dataset_size: 8968684827 - config_name: y features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 3695589005 num_examples: 124397 - name: validation num_bytes: 200369780 num_examples: 6911 - name: test num_bytes: 204394948 num_examples: 6911 download_size: 10142923776 dataset_size: 4100353733 --- # Dataset Card for Big Patent ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Big Patent](https://evasharma.github.io/bigpatent/) - Repository: - Paper: [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741) - Leaderboard: - Point of Contact: [Lu Wang](mailto:[email protected]) ### Dataset Summary BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories: - a: Human Necessities - b: Performing Operations; Transporting - c: Chemistry; Metallurgy - d: Textiles; Paper - e: Fixed Constructions - f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting - g: Physics - h: Electricity - y: General tagging of new or cross-sectional technology Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes: ```python from datasets import load_dataset ds = load_dataset("big_patent") # default is 'all' CPC codes ds = load_dataset("big_patent", "all") # the same as above ds = load_dataset("big_patent", "a") # only 'a' CPC codes ds = load_dataset("big_patent", codes=["a", "b"]) ``` To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ds = load_dataset("big_patent", codes="a", version="1.0.0") ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0") ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section. ``` { 'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...', 'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...' } ``` ### Data Fields - `description`: detailed description of patent. - `abstract`: Patent abastract. ### Data Splits \| \| train \| validation \| test \| \|:----\|------------------:\|-------------:\|-------:\| \| all \| 1207222 \| 67068 \| 67072 \| \| a \| 174134 \| 9674 \| 9675 \| \| b \| 161520 \| 8973 \| 8974 \| \| c \| 101042 \| 5613 \| 5614 \| \| d \| 10164 \| 565 \| 565 \| \| e \| 34443 \| 1914 \| 1914 \| \| f \| 85568 \| 4754 \| 4754 \| \| g \| 258935 \| 14385 \| 14386 \| \| h \| 257019 \| 14279 \| 14279 \| \| y \| 124397 \| 6911 \| 6911 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @article{DBLP:journals/corr/abs-1906-03741, author = {Eva Sharma and Chen Li and Lu Wang}, title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent Summarization}, journal = {CoRR}, volume = {abs/1906.03741}, year = {2019}, url = {http://arxiv.org/abs/1906.03741}, eprinttype = {arXiv}, eprint = {1906.03741}, timestamp = {Wed, 26 Jun 2019 07:14:58 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
billsum	--- annotations_creators: - found language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: billsum pretty_name: BillSum train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: text: text summary: target metrics: - type: rouge name: Rouge tags: - bills-summarization dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: title dtype: string splits: - name: train num_bytes: 219596090 num_examples: 18949 - name: test num_bytes: 37866257 num_examples: 3269 - name: ca_test num_bytes: 14945291 num_examples: 1237 download_size: 67260676 dataset_size: 272407638 --- # Dataset Card for "billsum" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/FiscalNote/BillSum](https://github.com/FiscalNote/BillSum) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 67.26 MB - Size of the generated dataset: 272.42 MB - Total amount of disk used: 339.68 MB ### Dataset Summary BillSum, summarization of US Congressional and California state bills. There are several features: - text: bill text. - summary: summary of the bills. - title: title of the bills. features for us bills. ca bills does not have. - text_len: number of chars in text. - sum_len: number of chars in summary. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 67.26 MB - Size of the generated dataset: 272.42 MB - Total amount of disk used: 339.68 MB An example of 'train' looks as follows. ``` { "summary": "some summary", "text": "some text.", "title": "An act to amend Section xxx." } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. ### Data Splits \| name \|train\|ca_test\|test\| \|-------\|----:\|------:\|---:\| \|default\|18949\| 1237\|3269\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data consists of three parts: US training bills, US test bills and California test bills. The US bills were collected from the [Govinfo](https://github.com/unitedstates/congress) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license. The California, bills from the 2015-2016 session are available from the legislature’s [website](https://leginfo.legislature.ca.gov/). #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{kornilova2019billsum, title={BillSum: A Corpus for Automatic Summarization of US Legislation}, author={Anastassia Kornilova and Vlad Eidelman}, year={2019}, eprint={1910.00523}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun) for adding this dataset.
bing_coronavirus_query_set	--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: null pretty_name: BingCoronavirusQuerySet dataset_info: features: - name: id dtype: int32 - name: Date dtype: string - name: Query dtype: string - name: IsImplicitIntent dtype: string - name: Country dtype: string - name: PopularityScore dtype: int32 config_name: country_2020-09-01_2020-09-30 splits: - name: train num_bytes: 22052706 num_examples: 317856 download_size: 16351450 dataset_size: 22052706 --- # Dataset Card for BingCoronavirusQuerySet ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: None - Repository: https://github.com/microsoft/BingCoronavirusQuerySet - Paper: Nonewww - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary Please note that you can specify the start and end date of the data. You can get start and end dates from here: https://github.com/microsoft/BingCoronavirusQuerySet/tree/master/data/2020 example: ``` load_dataset("bing_coronavirus_query_set", queries_by="state", start_date="2020-09-01", end_date="2020-09-30") ``` You can also load the data by country by using `queries_by="country"`. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
biomrc	--- language: - en paperswithcode_id: biomrc pretty_name: BIOMRC dataset_info: - config_name: plain_text features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1325877001 num_examples: 700000 - name: validation num_bytes: 96414040 num_examples: 50000 - name: test num_bytes: 118708586 num_examples: 62707 download_size: 343061539 dataset_size: 1540999627 - config_name: biomrc_small_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 206553549 num_examples: 87500 - name: validation num_bytes: 14957163 num_examples: 6250 - name: test num_bytes: 14807799 num_examples: 6250 download_size: 68879274 dataset_size: 236318511 - config_name: biomrc_small_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165662937 num_examples: 87500 - name: validation num_bytes: 12047304 num_examples: 6250 - name: test num_bytes: 11911172 num_examples: 6250 download_size: 57706889 dataset_size: 189621413 - config_name: biomrc_tiny_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 70914 num_examples: 30 download_size: 22519 dataset_size: 70914 - config_name: biomrc_tiny_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 59925 num_examples: 30 download_size: 19685 dataset_size: 59925 --- # Dataset Card for "biomrc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://nlp.cs.aueb.gr/](http://nlp.cs.aueb.gr/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.29 GB - Size of the generated dataset: 5.81 GB - Total amount of disk used: 7.09 GB ### Dataset Summary We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### biomrc_large_A - Size of downloaded dataset files: 408.08 MB - Size of the generated dataset: 1.92 GB - Total amount of disk used: 2.33 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...", "answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"], "title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n" } ``` #### biomrc_large_B - Size of downloaded dataset files: 343.06 MB - Size of the generated dataset: 1.54 GB - Total amount of disk used: 1.88 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...", "answer": "@entity2", "entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"], "title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n" } ``` #### biomrc_small_A - Size of downloaded dataset files: 68.88 MB - Size of the generated dataset: 236.32 MB - Total amount of disk used: 305.20 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...", "answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n", "entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...", "title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n" } ``` #### biomrc_small_B - Size of downloaded dataset files: 57.70 MB - Size of the generated dataset: 189.62 MB - Total amount of disk used: 247.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...", "answer": "@entity10", "entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"], "title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n" } ``` #### biomrc_tiny_A - Size of downloaded dataset files: 0.02 MB - Size of the generated dataset: 0.07 MB - Total amount of disk used: 0.09 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...", "answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"], "title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n" } ``` ### Data Fields The data fields are the same among all splits. #### biomrc_large_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_large_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_tiny_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. ### Data Splits #### biomrc_large_A \| \|train \|validation\|test \| \|--------------\|-----:\|---------:\|----:\| \|biomrc_large_A\|700000\| 50000\|62707\| #### biomrc_large_B \| \|train \|validation\|test \| \|--------------\|-----:\|---------:\|----:\| \|biomrc_large_B\|700000\| 50000\|62707\| #### biomrc_small_A \| \|train\|validation\|test\| \|--------------\|----:\|---------:\|---:\| \|biomrc_small_A\|87500\| 6250\|6250\| #### biomrc_small_B \| \|train\|validation\|test\| \|--------------\|----:\|---------:\|---:\| \|biomrc_small_B\|87500\| 6250\|6250\| #### biomrc_tiny_A \| \|test\| \|-------------\|---:\| \|biomrc_tiny_A\| 30\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{pappas-etal-2020-biomrc, title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension", author = "Pappas, Dimitris and Stavropoulos, Petros and Androutsopoulos, Ion and McDonald, Ryan", booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.bionlp-1.15", pages = "140--149", abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.", } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@PetrosStav](https://github.com/PetrosStav), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
biosses	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - gpl-3.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - semantic-similarity-scoring paperswithcode_id: biosses pretty_name: BIOSSES dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float32 splits: - name: train num_bytes: 32783 num_examples: 100 download_size: 36324 dataset_size: 32783 --- # Dataset Card for BIOSSES ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html - Repository: https://github.com/gizemsogancioglu/biosses - Paper: [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954) - Point of Contact: [Gizem Soğancıoğlu]([email protected]) and [Arzucan Özgür]([email protected]) ### Dataset Summary BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article. The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows: - very strong: 0.80–1.00 - strong: 0.60–0.79 - moderate: 0.40–0.59 - weak: 0.20–0.39 - very weak: 0.00–0.19 ### Supported Tasks and Leaderboards Biomedical Semantic Similarity Scoring. ### Languages English. ## Dataset Structure ### Data Instances For each instance, there are two sentences (i.e. sentence 1 and 2), and its corresponding similarity score (the mean of the scores assigned by the five human annotators). ``` {'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation' 'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase' 'score': 2.2} ``` ### Data Fields - `sentence 1`: string - `sentence 2`: string - `score`: float ranging from 0 (no relation) to 4 (equivalent) ### Data Splits No data splits provided. ## Dataset Creation ### Curation Rationale ### Source Data The [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/). #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees. The table below shows the Pearson correlation of the scores of each annotator with respect to the average scores of the remaining four annotators. It is observed that there is strong association among the scores of the annotators. The lowest correlations are 0.902, which can be considered as an upper bound for an algorithmic measure evaluated on this dataset. \| \|Correlation r \| \|----------:\|--------------:\| \|Annotator A\| 0.952\| \|Annotator B\| 0.958\| \|Annotator C\| 0.917\| \|Annotator D\| 0.902\| \|Annotator E\| 0.941\| #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators - Gizem Soğancıoğlu, [email protected] - Hakime Öztürk, [email protected] - Arzucan Özgür, [email protected] Bogazici University, Istanbul, Turkey ### Licensing Information BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html). ### Citation Information @article{souganciouglu2017biosses, title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain}, author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan}, journal={Bioinformatics}, volume={33}, number={14}, pages={i49--i58}, year={2017}, publisher={Oxford University Press} } ### Contributions Thanks to [@bwang482](https://github.com/bwang482) for adding this dataset.
blbooks	--- annotations_creators: - no-annotation language_creators: - machine-generated language: - de - en - es - fr - it - nl license: - cc0-1.0 multilinguality: - multilingual pretty_name: British Library Books size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask - other task_ids: - language-modeling - masked-language-modeling tags: - digital-humanities-research dataset_info: - config_name: all features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30394267732 num_examples: 14011953 download_size: 10486035662 dataset_size: 30394267732 - config_name: 1800s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30020434670 num_examples: 13781747 download_size: 10348577602 dataset_size: 30020434670 - config_name: 1700s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 266382657 num_examples: 178224 download_size: 95137895 dataset_size: 266382657 - config_name: '1510_1699' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 107667469 num_examples: 51982 download_size: 42320165 dataset_size: 107667469 - config_name: '1500_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30452067039 num_examples: 14011953 download_size: 10486035662 dataset_size: 30452067039 - config_name: '1800_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30077284377 num_examples: 13781747 download_size: 10348577602 dataset_size: 30077284377 - config_name: '1700_1799' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 267117831 num_examples: 178224 download_size: 95137895 dataset_size: 267117831 --- # Dataset Card for British Library Books ## Table of Contents - [Dataset Card for British Library Books](#dataset-card-for-British-Library-Books) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Language model training](#language-model-training) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Language change](#language-change) - [Optical Character Recognition](#optical-character-recognition) - [OCR word confidence](#ocr-word-confidence) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Date normalization](#date-normalization) - [Metadata included](#metadata-included) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Colonialism](#colonialism) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.bl.uk/collection-guides/digitised-printed-books - Repository: https://doi.org/10.21250/db14 - Paper: - Leaderboard: - Point of Contact: [email protected] ### Dataset Summary This dataset consists of books digitised by the British Library in partnership with Microsoft. The dataset includes ~25 million pages of out of copyright texts. The majority of the texts were published in the 18th and 19th Century, but the collection also consists of a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas, including geography, philosophy, history, poetry and literature and are published in various languages. While the books are predominately from the 18th and 19th Centuries, there are fewer books from earlier periods. The number of pages in the corpus by decade: \| \| page count \| \| ---- \| ---------- \| \| 1510 \| 94 \| \| 1520 \| 32 \| \| 1540 \| 184 \| \| 1550 \| 16 \| \| 1580 \| 276 \| \| 1590 \| 540 \| \| 1600 \| 1117 \| \| 1610 \| 1132 \| \| 1620 \| 1856 \| \| 1630 \| 9274 \| \| 1640 \| 4232 \| \| 1650 \| 2944 \| \| 1660 \| 5858 \| \| 1670 \| 11415 \| \| 1680 \| 8348 \| \| 1690 \| 13756 \| \| 1700 \| 10160 \| \| 1710 \| 9556 \| \| 1720 \| 10314 \| \| 1730 \| 13282 \| \| 1740 \| 10778 \| \| 1750 \| 12001 \| \| 1760 \| 21415 \| \| 1770 \| 28490 \| \| 1780 \| 32676 \| \| 1790 \| 50014 \| \| 1800 \| 307806 \| \| 1810 \| 478008 \| \| 1820 \| 589419 \| \| 1830 \| 681212 \| \| 1840 \| 1113473 \| \| 1850 \| 1726108 \| \| 1860 \| 1725407 \| \| 1870 \| 2069089 \| \| 1880 \| 2585159 \| \| 1890 \| 3365031 \| [More Information Needed] ### Supported Tasks and Leaderboards This collection has been previously used across various digital history and humanities projects since being published. The dataset consists of text and a range of metadata associated with this text. This metadata includes: - date of publication - place of publication - country of publication - language - OCR quality - physical description of the original physical item #### Language model training As a relatively large dataset, `blbooks` provides a source dataset for training language models. The presence of this metadata also offers interesting opportunities to use this dataset as a source for training language models based on: - specific time-periods - specific languages - certain OCR quality thresholds The above is not an exhaustive list but offer some suggestions of how the dataset can be used to explore topics such as the impact of OCR quality on language models, the ‘transferability’ of language models across time or the impact of training multilingual language models on historical languages. #### Supervised tasks Whilst this dataset does not have annotations for a specific NLP task, such as Named Entity Recognition, it does include a wide variety of metadata. This metadata has the potential to be used for training and/or evaluating a variety of supervised tasks predicting this metadata. ### Languages This dataset consists of books published in several languages. The breakdown of the languages included (at the page level) is: \| Language \| Pages \| \| --------------------- \| -------- \| \| English \| 10039463 \| \| French \| 1442929 \| \| German \| 1172793 \| \| Spanish \| 286778 \| \| Italian \| 214255 \| \| Dutch \| 204759 \| \| Russian \| 193347 \| \| Danish \| 93366 \| \| Hungarian \| 88094 \| \| Swedish \| 76225 \| \| Polish \| 58901 \| \| Greek, Modern (1453-) \| 26104 \| \| Latin \| 25611 \| \| Portuguese \| 25410 \| \| Czech \| 20160 \| \| Bulgarian \| 7891 \| \| Finnish \| 5677 \| \| Irish \| 2743 \| \| Serbian \| 1975 \| \| Romanian \| 1544 \| \| Norwegian Nynorsk \| 1398 \| \| Croatian \| 1306 \| \| Norwegian \| 1227 \| \| Icelandic \| 902 \| \| Slovak \| 840 \| \| Lithuanian \| 714 \| \| Welsh \| 580 \| \| Slovenian \| 545 \| \| Indonesian \| 418 \| \| Cornish \| 223 \| This breakdown was derived from the first language in the associated metadata field. Some books include multiple languages. Some of the languages codes for this data were also derived using computational methods. Therefore, the language fields in the dataset should be treated with some caution (discussed in more detail below). #### Language change The publication dates of books in the data cover a broad period of time (1500-1900). For languages in the dataset with broad temporal coverage, significant [language change](https://en.wikipedia.org/wiki/Language_change) might be found. The ability to study this change by taking reasonably large samples of languages covering different time periods is one of the opportunities offered by this dataset. The fact that the text in this dataset was produced via Optical Character Recognition (OCR) causes some challenges for this type of research (see below). #### Optical Character Recognition The digitised books in this collection were transformed into machine-readable text using Optical Character Recognition (OCR) software. The text produced via OCR software will usually include some errors. These errors include; mistakes at the character level; for example, an `i` is mistaken for an `l`, at the word level or across significant passages of text. The books in this dataset can pose some additional challenges for OCR software. OCR errors can stem from: - the quality of the original printing: printing technology was a developing technology during the time period covered by this corpus; some of the original book text will include misprints, blurred or faded ink that is hard to read - damage to the page: some of the books will have become damaged over time, this can obscure all or parts of the text on a page - poor quality scans: scanning books can be challenging; for example, if the book has tight bindings, it can be hard to capture text that has fallen into the [gutter](https://www.abaa.org/glossary/entry/gutter) of the book. - the language used in the books may differ from the languages OCR software is predominantly trained to recognise. ##### OCR word confidence Many OCR engines produce some form of confidence score alongside the predicted text. These confidence scores are usually at the character or word level. The word confidence score was given for each word in the original ALTO XML versions of the text in this dataset in this dataset. The OCR confidence scores should be treated with some scepticism. For historical text or in a lower resource language, for example, a low confidence score may be more likely for words not included in a modern dictionary but may be accurate transcriptions of the original text. With that said, the confidence scores do give some sense of the OCR quality. An example of text with a high (over 90% mean word confidence score): ``` 8 direction to the Conduit, round which is a wide open space, and a good broad pavement called the Parade. It commands a pleasant peep of the slopes and terrace throughout its entire length. The street continuing from the Conduit, in the same general direction, was known anciently as Lodborne Lane, and is now named South Street. From the Conduit two other streets, at right angles to these, are Long Street, leading Eastwards, and Half-Moon Street (formerly Lodborne), leading to Westbury, Trendle Street, and the Horsecastles Road. ``` An example of text with a score below 40%: ``` Hannover. Schrift und Druck von Fr. CultniTmn,', "LeMNs'utluirui.", 'ü 8u«llim» M^äalßwi 01de!lop 1<M.', 'p^dnalmw vom Xr^u/e, lpiti>»*Kmm lie« !»^2!M kleine lii!<! (,«> ttünee!<»e^ v»n tndzt Lievclum, 1872, ``` The quality of OCR - as measured by mean OCR confidence for a page - across the dataset correlates with other features. A groupby of publication decade and mean word confidence: \| decade \| mean_wc_ocr \| \| ------ \| ----------- \| \| 1510 \| 0.499151 \| \| 1520 \| 0.544818 \| \| 1540 \| 0.511589 \| \| 1550 \| 0.4505 \| \| 1580 \| 0.321858 \| \| 1590 \| 0.461282 \| \| 1600 \| 0.467318 \| \| 1610 \| 0.495895 \| \| 1620 \| 0.501257 \| \| 1630 \| 0.49766 \| \| 1640 \| 0.512095 \| \| 1650 \| 0.528534 \| \| 1660 \| 0.521014 \| \| 1670 \| 0.592575 \| \| 1680 \| 0.583901 \| \| 1690 \| 0.567202 \| \| 1700 \| 0.575175 \| \| 1710 \| 0.61436 \| \| 1720 \| 0.627725 \| \| 1730 \| 0.658534 \| \| 1740 \| 0.64214 \| \| 1750 \| 0.657357 \| \| 1760 \| 0.6389 \| \| 1770 \| 0.651883 \| \| 1780 \| 0.632326 \| \| 1790 \| 0.664279 \| \| 1800 \| 0.682338 \| \| 1810 \| 0.708915 \| \| 1820 \| 0.730015 \| \| 1830 \| 0.730973 \| \| 1840 \| 0.713886 \| \| 1850 \| 0.697106 \| \| 1860 \| 0.696701 \| \| 1870 \| 0.717233 \| \| 1880 \| 0.733331 \| \| 1890 \| 0.762364 \| As might be expected, the earlier periods have lower mean word confidence scores. Again, all of this should be treated with some scepticism, especially as the size of the data grows over time. As with time, the mean word confidence of the OCR software varies across languages: \| Language_1 \| mean_wc_ocr \| \| --------------------- \| ----------- \| \| Croatian \| 0.755565 \| \| Welsh \| 0.7528 \| \| Norwegian Nynorsk \| 0.751648 \| \| Slovenian \| 0.746007 \| \| French \| 0.740772 \| \| Finnish \| 0.738032 \| \| Czech \| 0.737849 \| \| Hungarian \| 0.736076 \| \| Dutch \| 0.734977 \| \| Cornish \| 0.733682 \| \| Danish \| 0.733106 \| \| English \| 0.733037 \| \| Irish \| 0.732658 \| \| Portuguese \| 0.727746 \| \| Spanish \| 0.725111 \| \| Icelandic \| 0.724427 \| \| Italian \| 0.715839 \| \| Swedish \| 0.715633 \| \| Polish \| 0.715133 \| \| Lithuanian \| 0.700003 \| \| Bulgarian \| 0.694657 \| \| Romanian \| 0.692957 \| \| Latin \| 0.689022 \| \| Russian \| 0.685847 \| \| Serbian \| 0.674329 \| \| Slovak \| 0.66739 \| \| Greek, Modern (1453-) \| 0.632195 \| \| German \| 0.631457 \| \| Indonesian \| 0.6155 \| \| Norwegian \| 0.597987 \| Again, these numbers should be treated sceptically since some languages appear very infrequently. For example, the above table suggests the mean word confidence for Welsh is relatively high. However, there isn’t much Welsh in the dataset. Therefore, it is unlikely that this data will be particularly useful for training (historic) Welsh language models. [More Information Needed] ## Dataset Structure The dataset has a number of configurations relating to the different dates of publication in the underlying data: - `1500_1899`: this configuration covers all years - `1800_1899`: this configuration covers the years between 1800 and 1899 - `1700_1799`: this configuration covers the years between 1700 and 1799 - `1510_1699`: this configuration covers the years between 1510 and 1699 ### Configuration option All of the configurations have an optional keyword argument `skip_empty_pages` which is set to `True` by default. The underlying dataset includes some pages where there is no text. This could either be because the underlying book page didn't have any text or the OCR software failed to detect this text. For many uses of this dataset it doesn't make sense to include empty pages so these are skipped by default. However, for some uses you may prefer to retain a representation of the data that includes these empty pages. Passing `skip_empty_pages=False` when loading the dataset will enable this option. ### Data Instances An example data instance: ```python {'Country of publication 1': 'England', 'Language_1': 'English', 'Language_2': None, 'Language_3': None, 'Language_4': None, 'Physical description': None, 'Publisher': None, 'all Countries of publication': 'England', 'all names': 'Settle, Elkanah [person]', 'date': 1689, 'empty_pg': True, 'mean_wc_ocr': 0.0, 'multi_language': False, 'name': 'Settle, Elkanah', 'pg': 1, 'place': 'London', 'raw_date': '1689', 'record_id': '001876770', 'std_wc_ocr': 0.0, 'text': None, ‘title’: ‘The Female Prelate: being the history and the life and death of Pope Joan. A tragedy [in five acts and in verse] . Written by a Person of Quality [i.e. Elkanah Settle]’} ``` Each instance in the dataset represents a single page from an original digitised book. ### Data Fields Included in this dataset are: \| Field \| Data Type \| Description \| \| ---------------------------- \| --------- \| ------------------------------------------------------------------------------------------------------------- \| \| record_id \| string \| British Library ID for the item \| \| date \| int \| parsed/normalised year for the item. i.e. 1850 \| \| raw_date \| string \| the original raw date for an item i.e. 1850- \| \| title \| string \| title of the book \| \| place \| string \| Place of publication, i.e. London \| \| empty_pg \| bool \| whether page contains text \| \| text \| string \| OCR generated text for a page \| \| pg \| int \| page in original book the instance refers to \| \| mean_wc_ocr \| float \| mean word confidence values for the page \| \| std_wc_ocr \| float \| standard deviation of the word confidence values for the page \| \| name \| string \| name associated with the item (usually author) \| \| all names \| string \| all names associated with a publication \| \| Publisher \| string \| publisher of the book \| \| Country of publication 1 \| string \| first country associated with publication \| \| all Countries of publication \| string \| all countries associated with a publication \| \| Physical description \| string \| physical description of the item (size). This requires some normalisation before use and isn’t always present \| \| Language_1 \| string \| first language associated with the book, this is usually present \| \| Language_2 \| string \| \| \| Language_3 \| string \| \| \| Language_4 \| string \| \| \| multi_language \| bool \| \| Some of these fields are not populated a large proportion of the time. You can get some sense of this from this [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) [report](https://davanstrien.github.io/BL-datasets-pandas-profile-reports/pandas_profile_report_MS_digitised_books_2021-01-09.html) The majority of these fields relate to metadata about the books. Most of these fields were created by staff working for the British Library. The notable exception is the “Languages” fields that have sometimes been determined using computational methods. This work is reported in more detail in [Automated Language Identification of Bibliographic Resources](https://doi.org/10.1080/01639374.2019.1700201). It is important to note that metadata is neither perfect nor static. The metadata associated with this book was generated based on export from the British Library catalogue in 2021. [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation Note this section is a work in progress. ### Curation Rationale The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization), i.e. projects intending to quickly digitise large volumes of materials shape the selection of materials to include in several ways. Some considerations which are often involved in the decision of whether to include items for digitisation include (but are not limited to): - copyright status - preservation needs - the size of an item, very large and very small items are often hard to digitise quickly These criteria can have knock-on effects on the makeup of a collection. For example, systematically excluding large books may result in some types of book content not being digitised. Large volumes are likely to be correlated to content to at least some extent, so excluding them from digitisation will mean that material is underrepresented. Similarly, copyright status is often (but not only) determined by publication date. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date. All of the above is largely to make clear that this collection was not curated to create a representative sample of the British Library’s holdings. Some material will be over-represented, and others under-represented. Similarly, the collection should not be considered a representative sample of what was published across the period covered by the dataset (nor that the relative proportions of the data for each time period represent a proportional sample of publications from that period). Finally, and this probably does not need stating, the language included in the text should not be considered representative of either written or spoken language(s) from that time period. [More Information Needed] ### Source Data The source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. “Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It’s existed in English law since 1662.” [source](https://www.bl.uk/legal-deposit/about-legal-deposit). The source data for this version of the data is derived from the original ALTO XML files and a recent metadata export #TODO add links [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created using the original ALTO XML files and, where a match was found, updating the metadata associated with that item with more recent metadata using an export from the British Library catalogue. The process of creating this new dataset is documented here #TODO add link. There are a few decisions made in the above processing steps worth highlighting in particular: ##### Date normalization The metadata around date of publication for an item is not always exact. It often is represented as a date range e.g. `1850-1860`. The `date` field above takes steps to normalise this date to a single integer value. In most cases, this is taking the mean of the values associated with the item. The `raw_date` field includes the unprocessed date string. ##### Metadata included The metadata associated with each item includes most of the fields available via the ALTO XML. However, the data doesn’t include some metadata fields from the metadata export file. The reason fields were excluded because they are frequently not populated. A cut off of 50% was chosen, i.e. values from the metadata which are missing above 50% of the time were not included. This is slightly arbitrary, but since the aim of this version of the data was to support computational research using the collection it was felt that these fields with frequent missing values would be less valuable. #### Who are the source language producers? [More Information Needed] ### Annotations This dataset does not include annotations as usually understood in the context of NLP. The data does include metadata associated with the books. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data There a range of considerations around using the data. These include the representativeness of the dataset, the OCR quality and the language used. Depending on your use case, these may be more or less important. For example, the impact of OCR quality on downstream tasks will depend on the target task. It may also be possible to mitigate this negative impact from OCR through tokenizer choice, Language Model training objectives, oversampling high-quality OCR, etc. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historical text. As a result, the text will reflect this time period's social beliefs and attitudes. The books include both fiction and non-fiction books. Examples of book titles that appear in the data (these are randomly sampled from all titles): - ‘Rhymes and Dreams, Legends of Pendle Forest, and other poems’, - “Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General’s Department, Horse Guards, War Office, etc”, - ‘The fan. A poem’, - ‘Grif; a story of Australian Life’, - ‘Calypso; a masque: in three acts, etc’, - ‘Tales Uncle told [With illustrative woodcuts.]’, - 'Questings', - 'Home Life on an Ostrich Farm. With ... illustrations’, - ‘Bulgarya i Bulgarowie’, - 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc', - ‘The Corsair, a tale’, ‘Poems ... With notes [With a portrait.]’, - ‘Report of the Librarian for the year 1898 (1899, 1901, 1909)’, - “The World of Thought. A novel. By the author of ‘Before I began to speak.’”, - 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc'] While using titles alone is insufficient to integrate bias in this collection, it gives some insight into the topics covered by books. Further, the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list. #### Colonialism Even in the above random sample of titles examples of colonial attitudes, we can see examples of titles. We can try and interrogate this further by searching for the name of places that were part of the British Empire when many of these books were published. Searching for the string `India` in the titles and randomly sampling 10 titles returns: - “Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the ‘Calcutta Weekly Englishman.’”, - ‘A Winter in India and Malaysia among the Methodist Missions’, - “The Tourist’s Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition”, - ‘Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson’, - "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]”, - ‘The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies’, - “From Tonkin to India : by the sources of the Irawadi, January’ 95-January ’96”, - ‘Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844’, - ‘The Andaman Islands; their colonisation, etc. A correspondence addressed to the India Office’, - ‘Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle’] Searching form the string `Africa` in the titles and randomly sampling 10 titles returns: - ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada', - ‘To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]’, - ‘Diamonds and Gold in South Africa ... With maps, etc’, - ‘Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition’, - ‘A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts’, - ‘Side Lights on South Africa ... With a map, etc’, - ‘My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc’, - ‘Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations’, - ‘[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder’, - ‘Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc’] [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license. ### Citation Information ```bibtext @misc{bBritishLibraryBooks2021, author = {British Library Labs}, title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)}, year = {2021}, publisher = {British Library}, howpublished={https://doi.org/10.23636/r7w6-zy15} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
blbooksgenre	--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - de - en - fr - nl license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - text-classification - text-generation - fill-mask task_ids: - topic-classification - multi-label-classification - language-modeling - masked-language-modeling pretty_name: British Library Books Genre configs: - annotated_raw - raw - title_genre_classifiction dataset_info: - config_name: title_genre_classifiction features: - name: BL record ID dtype: string - name: title dtype: string - name: label dtype: class_label: names: '0': Fiction '1': Non-fiction splits: - name: train num_bytes: 187600 num_examples: 1736 download_size: 20111420 dataset_size: 187600 - config_name: annotated_raw features: - name: BL record ID dtype: string - name: Name dtype: string - name: Dates associated with name dtype: string - name: Type of name dtype: string - name: Role dtype: string - name: All names sequence: string - name: Title dtype: string - name: Variant titles dtype: string - name: Series title dtype: string - name: Number within series dtype: string - name: Country of publication sequence: string - name: Place of publication sequence: string - name: Publisher dtype: string - name: Date of publication dtype: string - name: Edition dtype: string - name: Physical description dtype: string - name: Dewey classification dtype: string - name: BL shelfmark dtype: string - name: Topics dtype: string - name: Genre dtype: string - name: Languages sequence: string - name: Notes dtype: string - name: BL record ID for physical resource dtype: string - name: classification_id dtype: string - name: user_id dtype: string - name: subject_ids dtype: string - name: annotator_date_pub dtype: string - name: annotator_normalised_date_pub dtype: string - name: annotator_edition_statement dtype: string - name: annotator_FAST_genre_terms dtype: string - name: annotator_FAST_subject_terms dtype: string - name: annotator_comments dtype: string - name: annotator_main_language dtype: string - name: annotator_other_languages_summaries dtype: string - name: annotator_summaries_language dtype: string - name: annotator_translation dtype: string - name: annotator_original_language dtype: string - name: annotator_publisher dtype: string - name: annotator_place_pub dtype: string - name: annotator_country dtype: string - name: annotator_title dtype: string - name: Link to digitised book dtype: string - name: annotated dtype: bool - name: Type of resource dtype: class_label: names: '0': Monograph '1': Serial - name: created_at dtype: timestamp[s] - name: annotator_genre dtype: class_label: names: '0': Fiction '1': Can't tell '2': Non-fiction '3': The book contains both Fiction and Non-Fiction splits: - name: train num_bytes: 3583138 num_examples: 4398 download_size: 20111420 dataset_size: 3583138 - config_name: raw features: - name: BL record ID dtype: string - name: Name dtype: string - name: Dates associated with name dtype: string - name: Type of name dtype: string - name: Role dtype: string - name: All names sequence: string - name: Title dtype: string - name: Variant titles dtype: string - name: Series title dtype: string - name: Number within series dtype: string - name: Country of publication sequence: string - name: Place of publication sequence: string - name: Publisher dtype: string - name: Date of publication dtype: string - name: Edition dtype: string - name: Physical description dtype: string - name: Dewey classification dtype: string - name: BL shelfmark dtype: string - name: Topics dtype: string - name: Genre dtype: string - name: Languages sequence: string - name: Notes dtype: string - name: BL record ID for physical resource dtype: string - name: classification_id dtype: string - name: user_id dtype: string - name: subject_ids dtype: string - name: annotator_date_pub dtype: string - name: annotator_normalised_date_pub dtype: string - name: annotator_edition_statement dtype: string - name: annotator_FAST_genre_terms dtype: string - name: annotator_FAST_subject_terms dtype: string - name: annotator_comments dtype: string - name: annotator_main_language dtype: string - name: annotator_other_languages_summaries dtype: string - name: annotator_summaries_language dtype: string - name: annotator_translation dtype: string - name: annotator_original_language dtype: string - name: annotator_publisher dtype: string - name: annotator_place_pub dtype: string - name: annotator_country dtype: string - name: annotator_title dtype: string - name: Link to digitised book dtype: string - name: annotated dtype: bool - name: Type of resource dtype: class_label: names: '0': Monograph '1': Serial '2': Monographic component part - name: created_at dtype: string - name: annotator_genre dtype: string splits: - name: train num_bytes: 27518816 num_examples: 55343 download_size: 20111420 dataset_size: 27518816 --- # Dataset Card for blbooksgenre ## Table of Contents - [Dataset Card for blbooksgenre](#dataset-card-for-blbooksgenre) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Colonialism](#colonialism) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:: [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312) - Repository: [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312) - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset consists of metadata relating to books [digitised by the British Library in partnership with Microsoft](https://www.bl.uk/collection-guides/google-books-digitised-printed-heritage). Some of this metadata was exported from the British Library catalogue whilst others was generated as part of a crowdsourcing project. The text of this book and other metadata can be found on the [date.bl](https://data.bl.uk/bl_labs_datasets/#3) website. The majority of the books in this collection were published in the 18th and 19th Century but the collection also includes a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas including geography, philosophy, history, poetry and literature and are published in a variety of languages. For the subsection of the data which contains additional crowsourced annotations the date of publication breakdown is as follows: \| \| Date of publication \| \| ---- \| ------------------- \| \| 1630 \| 8 \| \| 1690 \| 4 \| \| 1760 \| 10 \| \| 1770 \| 5 \| \| 1780 \| 5 \| \| 1790 \| 18 \| \| 1800 \| 45 \| \| 1810 \| 96 \| \| 1820 \| 152 \| \| 1830 \| 182 \| \| 1840 \| 259 \| \| 1850 \| 400 \| \| 1860 \| 377 \| \| 1870 \| 548 \| \| 1880 \| 776 \| \| 1890 \| 1484 \| \| 1900 \| 17 \| \| 1910 \| 1 \| \| 1970 \| 1 \| [More Information Needed] ### Supported Tasks and Leaderboards The digitised books collection which this dataset describes has been used in a variety of digital history and humanities projects since being published. This dataset is suitable for a variety of unsupervised tasks and for a 'genre classification task'. #### Supervised tasks The main possible use case for this dataset is to develop and evaluate 'genre classification' models. The dataset includes human generated labels for whether a book is 'fiction' or 'non-fiction'. This has been used to train models for genre classifcation which predict whether a book is 'fiction' or 'non-fiction' based on its title. ### Languages [More Information Needed] ## Dataset Structure The dataset currently has three configurations intended to support a range of tasks for which this dataset could be used for: - `title_genre_classifiction` : this creates a de-duplicated version of the dataset with the `BL record`, `title` and `label`. - `annotated_raw`: This version of the dataset includes all fields from the original dataset which are annotated. This includes duplication from different annotators" - `raw`: This version of the dataset includes all the data from the original data including data without annotations. ### Data Instances An example data instance from the `title_genre_classifiction` config: ```python {'BL record ID': '014603046', 'title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'label': 0} ``` An example data instance from the `annotated_raw` config: ```python {'BL record ID': '014603046', 'Name': 'Yates, William Joseph H.', 'Dates associated with name': '', 'Type of name': 'person', 'Role': '', 'All names': ['Yates, William Joseph H. [person] ', ' Y, W. J. H. [person]'], 'Title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'Variant titles': '', 'Series title': '', 'Number within series': '', 'Country of publication': ['England'], 'Place of publication': ['London'], 'Publisher': '', 'Date of publication': '1879', 'Edition': '', 'Physical description': 'pages not numbered, 21 cm', 'Dewey classification': '', 'BL shelfmark': 'Digital Store 11601.f.36. (1.)', 'Topics': '', 'Genre': '', 'Languages': ['English'], 'Notes': 'In verse', 'BL record ID for physical resource': '004079262', 'classification_id': '267476823.0', 'user_id': '15.0', 'subject_ids': '44369003.0', 'annotator_date_pub': '1879', 'annotator_normalised_date_pub': '1879', 'annotator_edition_statement': 'NONE', 'annotator_FAST_genre_terms': '655 7 ‡aPoetry‡2fast‡0(OCoLC)fst01423828', 'annotator_FAST_subject_terms': '60007 ‡aAlice,‡cGrand Duchess, consort of Ludwig IV, Grand Duke of Hesse-Darmstadt,‡d1843-1878‡2fast‡0(OCoLC)fst00093827', 'annotator_comments': '', 'annotator_main_language': '', 'annotator_other_languages_summaries': 'No', 'annotator_summaries_language': '', 'annotator_translation': 'No', 'annotator_original_language': '', 'annotator_publisher': 'NONE', 'annotator_place_pub': 'London', 'annotator_country': 'enk', 'annotator_title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'Link to digitised book': 'http://access.bl.uk/item/viewer/ark:/81055/vdc_00000002842E', 'annotated': True, 'Type of resource': 0, 'created_at': datetime.datetime(2020, 8, 11, 14, 30, 33), 'annotator_genre': 0} ``` ### Data Fields The data fields differ slightly between configs. All possible fields for the `annotated_raw` config are listed below. For the `raw` version of the dataset datatypes are usually string to avoid errors when processing missing values. - `BL record ID`: an internal ID used by the British Library, this can be useful for linking this data to other BL collections. - `Name`: name associated with the item (usually author) - `Dates associated with name`: dates associated with above e.g. DOB - `Type of name`: whether `Name` is a person or an organization etc. - `Role`: i.e. whether `Name` is `author`, `publisher` etc. - `All names`: a fuller list of names associated with the item. - `Title`: The title of the work - `Variant titles` - `Series title` - `Number within series` - `Country of publication`: encoded as a list of countries listed in the metadata - `Place of publication`: encoded as a list of places listed in the metadata - `Publisher` - `Date of publication`: this is encoded as a string since this field can include data ranges i.e.`1850-1855`. - `Edition` - `Physical description`: encoded as a string since the format of this field varies - `Dewey classification` - `BL shelfmark`: a British Library shelf mark - `Topics`: topics included in the catalogue record - `Genre` the genre information included in the original catalogue record note that this is often missing - `Languages`; encoded as a list of languages - `Notes`: notes from the catalogue record - `BL record ID for physical resource` The following fields are all generated via the crowdsourcing task (discussed in more detail below) - `classification_id`: ID for the classification in the annotation task - `user_id` ID for the annotator - `subject_ids`: internal annotation task ID - `annotator_date_pub`: an updated publication data - `annotator_normalised_date_pub`: normalized version of the above - `annotator_edition_statement` updated edition - `annotator_FAST_genre_terms`: [FAST classification genre terms](https://www.oclc.org/research/areas/data-science/fast.html) - `annotator_FAST_subject_terms`: [FAST subject terms](https://www.oclc.org/research/areas/data-science/fast.html) - `annotator_comments`: free form comments - `annotator_main_language` - `annotator_other_languages_summaries` - `'annotator_summaries_language` - `annotator_translation` - `annotator_original_language` - `annotator_publisher` - `annotator_place_pub` - `annotator_country` - `annotator_title` - `Link to digitised book` - `annotated`: `bool` flag to indicate if row has annotations or not - `created_at`: when the annotation was created - `annotator_genre`: the updated annotation for the `genre` of the book. Finally the `label` field of the `title_genre_classifiction` configuration is a class label with values 0 (Fiction) or 1 (Non-fiction). [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation Note this section is a work in progress. ### Curation Rationale The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization) i.e. projects where there is a goal to quickly digitise large volumes of materials shape the selection of materials to include in a number of ways. Some consideratoins which are often involved in the decision of whether to include items for digitization include (but are not limited to): - copyright status - preservation needs- the size of an item, very large and very small items are often hard to digitize quickly These criteria can have knock-on effects on the makeup of a collection. For example systematically excluding large books may result in some types of book content not being digitized. Large volumes are likely to be correlated to content to at least some extent so excluding them from digitization will mean that material is under represented. Similarly copyright status is often (but not only) determined by publication data. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date. All of the above is largely to make clear that this collection was not curated with the aim of creating a representative sample of the British Library's holdings. Some material will be over-represented and other under-represented. Similarly, the collection should not be considered a representative sample of what was published across the time period covered by the dataset (nor that that the relative proportions of the data for each time period represent a proportional sample of publications from that period). [More Information Needed] ### Source Data The original source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. "Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It's existed in English law since 1662."[source](https://www.bl.uk/legal-deposit/about-legal-deposit). [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created partially from data exported from British Library catalogue records and partially via data generated from a crowdsourcing task involving British Library staff. #### Who are the source language producers? [More Information Needed] ### Annotations The data does includes metadata associated with the books these are produced by British Library staff. The additional annotations were carried out during 2020 as part of an internal crowdsourcing task. #### Annotation process New annotations were produced via a crowdsourcing tasks. Annotators have the option to pick titles from a particular language subset from the broader digitized 19th century books collection. As a result the annotations are not random and overrepresent some languages. [More Information Needed] #### Who are the annotators? Staff working at the British Library. Most of these staff work with metadata as part of their jobs and so could be considered expert annotators. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data There a range of considerations around using the data. These include the representativeness of the dataset, the bias towards particular languages etc. It is also important to note that library metadata is not static. The metadata held in library catalogues is updated and changed over time for a variety of reasons. The way in which different institutions catalogue items also varies. As a result it is important to evaluate the performance of any models trained on this data before applying to a new collection. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historic text. As a result the text will reflect to social beliefs and attitudes of this time period. The titles of the book give some sense of their content. Examples of book titles which appear in the data (these are randomly sampled from all titles): - 'Rhymes and Dreams, Legends of Pendle Forest, and other poems', - "Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General's Department, Horse Guards, War Office, etc", - 'The fan. A poem', - 'Grif; a story of Australian Life', - 'Calypso; a masque: in three acts, etc', - 'Tales Uncle told [With illustrative woodcuts.]', - 'Questings', - 'Home Life on an Ostrich Farm. With ... illustrations', - 'Bulgarya i Bulgarowie', - 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc', - 'The Corsair, a tale', 'Poems ... With notes [With a portrait.]', - 'Report of the Librarian for the year 1898 (1899, 1901, 1909)', - "The World of Thought. A novel. By the author of 'Before I began to speak.'", - 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc'] Whilst using titles alone, is obviously insufficient to integrate bias in this collection it gives some insight into the topics covered by books in the corpus. Further looking into the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list. #### Colonialism We can see even in the above random sample of titles examples of colonial attitudes. We can try and interrogate this further by searching for the name of countries which were part of the British Empire at the time many of these books were published. Searching for the string `India` in the titles and randomly sampling 10 titles returns: - "Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the 'Calcutta Weekly Englishman.'", - 'A Winter in India and Malaysia among the Methodist Missions', - "The Tourist's Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition", - 'Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson', - "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]", - 'The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies', - "From Tonkin to India : by the sources of the Irawadi, January '95-January '96", - 'Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844', - 'The Andaman Islands; their colonization, etc. A correspondence addressed to the India Office', - 'Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle'] Searching form the string `Africa` in the titles and randomly sampling 10 titles returns: - ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada', - 'To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]', - 'Diamonds and Gold in South Africa ... With maps, etc', - 'Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition', - 'A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts', - 'Side Lights on South Africa ... With a map, etc', - 'My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc', - 'Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations', - '[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder', - 'Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc'] Whilst this dataset doesn't include the underlying text it is important to consider the potential attitudes represented in the title of the books, or the full text if you are using this dataset in conjunction with the full text. [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license. ### Citation Information ```bibtex @misc{british library_genre, title={ 19th Century Books - metadata with additional crowdsourced annotations}, url={https://doi.org/10.23636/BKHQ-0312}, author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi}, year={2021}} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
blended_skill_talk	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual pretty_name: BlendedSkillTalk size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation paperswithcode_id: blended-skill-talk dataset_info: features: - name: personas sequence: string - name: additional_context dtype: string - name: previous_utterance sequence: string - name: context dtype: string - name: free_messages sequence: string - name: guided_messages sequence: string - name: suggestions sequence: - name: convai2 dtype: string - name: empathetic_dialogues dtype: string - name: wizard_of_wikipedia dtype: string - name: guided_chosen_suggestions sequence: string - name: label_candidates sequence: sequence: string splits: - name: train num_bytes: 10831361 num_examples: 4819 - name: validation num_bytes: 43961658 num_examples: 1009 - name: test num_bytes: 44450102 num_examples: 980 download_size: 38101408 dataset_size: 99243121 --- # Dataset Card for "blended_skill_talk" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://parl.ai/projects/bst/](https://parl.ai/projects/bst/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 38.11 MB - Size of the generated dataset: 15.08 MB - Total amount of disk used: 53.17 MB ### Dataset Summary A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 38.11 MB - Size of the generated dataset: 15.08 MB - Total amount of disk used: 53.17 MB An example of 'train' looks as follows. ``` { 'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'], 'additional_context': 'Backstreet Boys', 'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "], 'context': 'wizard_of_wikipedia', 'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "], 'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'], 'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']}, 'guided_chosen_suggestions': ['convai2', '', ''], 'label_candidates': []} ``` ### Data Fields The data fields are the same among all splits. #### default - `personas`: a `list` of `string` features. - `additional_context`: a `string` feature. - `previous_utterance`: a `list` of `string` features. - `context`: a `string` feature. - `free_messages`: a `list` of `string` features. - `guided_messgaes`: a `list` of `string` features. - `suggestions`: a dictionary feature containing: - `convai2`: a `string` feature. - `empathetic_dialogues`: a `string` feature. - `wizard_of_wikipedia`: a `string` feature. - `guided_chosen_suggestions`: a `list` of `string` features. - `label_candidates`: a `list` of `lists` of `string` features. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\| 4819\| 1009\| 980\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{smith2020evaluating, title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills}, author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau}, year={2020}, eprint={2004.08449}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
blimp	--- annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: cc-by-4.0 multilinguality: - monolingual pretty_name: BLiMP size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - acceptability-classification paperswithcode_id: blimp dataset_info: - config_name: adjunct_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 167289 num_examples: 1000 download_size: 359284 dataset_size: 167289 - config_name: anaphor_gender_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 132313 num_examples: 1000 download_size: 436749 dataset_size: 132313 - config_name: anaphor_number_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 141274 num_examples: 1000 download_size: 450861 dataset_size: 141274 - config_name: animate_subject_passive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 145818 num_examples: 1000 download_size: 462292 dataset_size: 145818 - config_name: animate_subject_trans features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 129193 num_examples: 1000 download_size: 433098 dataset_size: 129193 - config_name: causative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 124167 num_examples: 1000 download_size: 317162 dataset_size: 124167 - config_name: complex_NP_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 200367 num_examples: 1000 download_size: 392362 dataset_size: 200367 - config_name: coordinate_structure_constraint_complex_left_branch features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 212307 num_examples: 1000 download_size: 571696 dataset_size: 212307 - config_name: coordinate_structure_constraint_object_extraction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173050 num_examples: 1000 download_size: 366045 dataset_size: 173050 - config_name: determiner_noun_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 157515 num_examples: 1000 download_size: 468642 dataset_size: 157515 - config_name: determiner_noun_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 157599 num_examples: 1000 download_size: 488856 dataset_size: 157599 - config_name: determiner_noun_agreement_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165868 num_examples: 1000 download_size: 474932 dataset_size: 165868 - config_name: determiner_noun_agreement_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 162469 num_examples: 1000 download_size: 490854 dataset_size: 162469 - config_name: determiner_noun_agreement_with_adj_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 181061 num_examples: 1000 download_size: 526806 dataset_size: 181061 - config_name: determiner_noun_agreement_with_adj_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 185924 num_examples: 1000 download_size: 499664 dataset_size: 185924 - config_name: determiner_noun_agreement_with_adj_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 185791 num_examples: 1000 download_size: 528528 dataset_size: 185791 - config_name: determiner_noun_agreement_with_adjective_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 186521 num_examples: 1000 download_size: 504676 dataset_size: 186521 - config_name: distractor_agreement_relational_noun features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 192868 num_examples: 1000 download_size: 525650 dataset_size: 192868 - config_name: distractor_agreement_relative_clause features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 218151 num_examples: 1000 download_size: 564770 dataset_size: 218151 - config_name: drop_argument features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 111201 num_examples: 1000 download_size: 304196 dataset_size: 111201 - config_name: ellipsis_n_bar_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 218985 num_examples: 1000 download_size: 411980 dataset_size: 218985 - config_name: ellipsis_n_bar_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 234556 num_examples: 1000 download_size: 427551 dataset_size: 234556 - config_name: existential_there_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 225136 num_examples: 1000 download_size: 550672 dataset_size: 225136 - config_name: existential_there_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 164326 num_examples: 1000 download_size: 357321 dataset_size: 164326 - config_name: existential_there_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 166221 num_examples: 1000 download_size: 359216 dataset_size: 166221 - config_name: existential_there_subject_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 201458 num_examples: 1000 download_size: 394453 dataset_size: 201458 - config_name: expletive_it_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 240010 num_examples: 1000 download_size: 587648 dataset_size: 240010 - config_name: inchoative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 105714 num_examples: 1000 download_size: 298709 dataset_size: 105714 - config_name: intransitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 112492 num_examples: 1000 download_size: 305487 dataset_size: 112492 - config_name: irregular_past_participle_adjectives features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 146056 num_examples: 1000 download_size: 444520 dataset_size: 146056 - config_name: irregular_past_participle_verbs features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 127087 num_examples: 1000 download_size: 420119 dataset_size: 127087 - config_name: irregular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 166979 num_examples: 1000 download_size: 460705 dataset_size: 166979 - config_name: irregular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 155238 num_examples: 1000 download_size: 453376 dataset_size: 155238 - config_name: left_branch_island_echo_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 149235 num_examples: 1000 download_size: 482617 dataset_size: 149235 - config_name: left_branch_island_simple_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 151455 num_examples: 1000 download_size: 343450 dataset_size: 151455 - config_name: matrix_question_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 154657 num_examples: 1000 download_size: 457806 dataset_size: 154657 - config_name: npi_present_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 139860 num_examples: 1000 download_size: 438013 dataset_size: 139860 - config_name: npi_present_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 129031 num_examples: 1000 download_size: 422136 dataset_size: 129031 - config_name: only_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 149911 num_examples: 1000 download_size: 459170 dataset_size: 149911 - config_name: only_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 210297 num_examples: 1000 download_size: 583720 dataset_size: 210297 - config_name: passive_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 147277 num_examples: 1000 download_size: 340272 dataset_size: 147277 - config_name: passive_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 115355 num_examples: 1000 download_size: 308350 dataset_size: 115355 - config_name: principle_A_c_command features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 189885 num_examples: 1000 download_size: 527689 dataset_size: 189885 - config_name: principle_A_case_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171793 num_examples: 1000 download_size: 477239 dataset_size: 171793 - config_name: principle_A_case_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171807 num_examples: 1000 download_size: 492973 dataset_size: 171807 - config_name: principle_A_domain_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 172565 num_examples: 1000 download_size: 499865 dataset_size: 172565 - config_name: principle_A_domain_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 166728 num_examples: 1000 download_size: 493189 dataset_size: 166728 - config_name: principle_A_domain_3 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160393 num_examples: 1000 download_size: 513886 dataset_size: 160393 - config_name: principle_A_reconstruction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153499 num_examples: 1000 download_size: 345494 dataset_size: 153499 - config_name: regular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160214 num_examples: 1000 download_size: 451850 dataset_size: 160214 - config_name: regular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 155004 num_examples: 1000 download_size: 456477 dataset_size: 155004 - config_name: sentential_negation_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173259 num_examples: 1000 download_size: 490996 dataset_size: 173259 - config_name: sentential_negation_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 233493 num_examples: 1000 download_size: 614930 dataset_size: 233493 - config_name: sentential_subject_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173827 num_examples: 1000 download_size: 365822 dataset_size: 173827 - config_name: superlative_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160685 num_examples: 1000 download_size: 381189 dataset_size: 160685 - config_name: superlative_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160735 num_examples: 1000 download_size: 516120 dataset_size: 160735 - config_name: tough_vs_raising_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 150031 num_examples: 1000 download_size: 343026 dataset_size: 150031 - config_name: tough_vs_raising_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171079 num_examples: 1000 download_size: 364074 dataset_size: 171079 - config_name: transitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 134499 num_examples: 1000 download_size: 460291 dataset_size: 134499 - config_name: wh_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 143735 num_examples: 1000 download_size: 448630 dataset_size: 143735 - config_name: wh_questions_object_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 194440 num_examples: 1000 download_size: 387435 dataset_size: 194440 - config_name: wh_questions_subject_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 196988 num_examples: 1000 download_size: 389983 dataset_size: 196988 - config_name: wh_questions_subject_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 269665 num_examples: 1000 download_size: 462660 dataset_size: 269665 - config_name: wh_vs_that_no_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 190267 num_examples: 1000 download_size: 383262 dataset_size: 190267 - config_name: wh_vs_that_no_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 248434 num_examples: 1000 download_size: 441429 dataset_size: 248434 - config_name: wh_vs_that_with_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 174781 num_examples: 1000 download_size: 367776 dataset_size: 174781 - config_name: wh_vs_that_with_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 232990 num_examples: 1000 download_size: 425985 dataset_size: 232990 --- # Dataset Card for "blimp" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/alexwarstadt/blimp/tree/master/](https://github.com/alexwarstadt/blimp/tree/master/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 29.58 MB - Size of the generated dataset: 11.45 MB - Total amount of disk used: 41.03 MB ### Dataset Summary BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### adjunct_island - Size of downloaded dataset files: 0.36 MB - Size of the generated dataset: 0.17 MB - Total amount of disk used: 0.52 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_gender_agreement - Size of downloaded dataset files: 0.44 MB - Size of the generated dataset: 0.14 MB - Total amount of disk used: 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_number_agreement - Size of downloaded dataset files: 0.45 MB - Size of the generated dataset: 0.14 MB - Total amount of disk used: 0.59 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_passive - Size of downloaded dataset files: 0.46 MB - Size of the generated dataset: 0.15 MB - Total amount of disk used: 0.61 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_trans - Size of downloaded dataset files: 0.43 MB - Size of the generated dataset: 0.13 MB - Total amount of disk used: 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` ### Data Fields The data fields are the same among all splits. #### adjunct_island - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_gender_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_number_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_passive - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_trans - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. ### Data Splits \| name \|train\| \|------------------------\|----:\| \|adjunct_island \| 1000\| \|anaphor_gender_agreement\| 1000\| \|anaphor_number_agreement\| 1000\| \|animate_subject_passive \| 1000\| \|animate_subject_trans \| 1000\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{warstadt2019blimp, title={BLiMP: A Benchmark of Linguistic Minimal Pairs for English}, author={Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei, and Wang, Sheng-Fu and Bowman, Samuel R}, journal={arXiv preprint arXiv:1912.00582}, year={2019} } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
blog_authorship_corpus	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual paperswithcode_id: blog-authorship-corpus pretty_name: Blog Authorship Corpus size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification dataset_info: features: - name: text dtype: string - name: date dtype: string - name: gender dtype: string - name: age dtype: int32 - name: horoscope dtype: string - name: job dtype: string config_name: blog_authorship_corpus splits: - name: train num_bytes: 753833081 num_examples: 689793 - name: validation num_bytes: 41236028 num_examples: 37919 download_size: 632898892 dataset_size: 795069109 --- # Dataset Card for Blog Authorship Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 312.95 MB - Size of the generated dataset: 647.76 MB - Total amount of disk used: 960.71 MB ### Dataset Summary The Blog Authorship Corpus consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. The corpus incorporates a total of 681,288 posts and over 140 million words - or approximately 35 posts and 7250 words per person. Each blog is presented as a separate file, the name of which indicates a blogger id# and the blogger’s self-provided gender, age, industry and astrological sign. (All are labeled for gender and age but for many, industry and/or sign is marked as unknown.) All bloggers included in the corpus fall into one of three age groups: - 8240 "10s" blogs (ages 13-17), - 8086 "20s" blogs (ages 23-27), - 2994 "30s" blogs (ages 33-47). For each age group there are an equal number of male and female bloggers. Each blog in the corpus includes at least 200 occurrences of common English words. All formatting has been stripped with two exceptions. Individual posts within a single blogger are separated by the date of the following post and links within a post are denoted by the label urllink. The corpus may be freely used for non-commercial research purposes. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The language of the dataset is English (`en`). ## Dataset Structure ### Data Instances #### blog-authorship-corpus - Size of downloaded dataset files: 312.95 MB - Size of the generated dataset: 647.76 MB - Total amount of disk used: 960.71 MB An example of 'validation' looks as follows. ``` { "age": 23, "date": "27,July,2003", "gender": "female", "horoscope": "Scorpion", "job": "Student", "text": "This is a second test file." } ``` ### Data Fields The data fields are the same among all splits. #### blog-authorship-corpus - `text`: a `string` feature. - `date`: a `string` feature. - `gender`: a `string` feature. - `age`: a `int32` feature. - `horoscope`: a `string` feature. - `job`: a `string` feature. ### Data Splits \| name \|train \|validation\| \|----------------------\|-----:\|---------:\| \|blog-authorship-corpus\|532812\| 31277\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The corpus may be freely used for non-commercial research purposes. ### Citation Information ``` @inproceedings{schler2006effects, title={Effects of age and gender on blogging.}, author={Schler, Jonathan and Koppel, Moshe and Argamon, Shlomo and Pennebaker, James W}, booktitle={AAAI spring symposium: Computational approaches to analyzing weblogs}, volume={6}, pages={199--205}, year={2006} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
bn_hate_speech	--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - bn license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: bengali-hate-speech pretty_name: Bengali Hate Speech Dataset tags: - hate-speech-topic-classification dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Personal '1': Political '2': Religious '3': Geopolitical '4': Gender abusive splits: - name: train num_bytes: 972635 num_examples: 3418 download_size: 974312 dataset_size: 972635 --- # Dataset Card for Bengali Hate Speech Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Bengali Hate Speech Dataset](https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset) - Repository: [Bengali Hate Speech Dataset](https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset) - Paper: [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://arxiv.org/abs/2004.07807) - Point of Contact: [Md. Rezaul Karim]([email protected]) ### Dataset Summary The Bengali Hate Speech Dataset is a Bengali-language dataset of news articles collected from various Bengali media sources and categorized based on the type of hate in the text. The dataset was created to provide greater support for under-resourced languages like Bengali on NLP tasks, and serves as a benchmark for multiple types of classification tasks. ### Supported Tasks and Leaderboards * `topic classification`: The dataset can be used to train a Multichannel Convolutional-LSTM for classifying different types of hate speech. The model performance can be measured by its F1 score. ### Languages The text in the dataset is in Bengali and the associated BCP-47 code is `bn`. ## Dataset Structure ### Data Instances A data instance takes the form of a news article and its associated label. 🚨 Beware that the following example contains extremely offensive content! An example looks like this: ``` {"text": "রেন্ডিয়াকে পৃথীবির মানচিএ থেকে মুচে ফেলতে হবে", "label": "Geopolitical"} ``` ### Data Fields * `text`: the text of the Bengali news article * `label`: one of `Geopolitical`, `Personal`, `Political`, `Religious`, or `Gender abusive` indicating the type of hate speech ### Data Splits The dataset has 3418 examples. ## Dataset Creation ### Curation Rationale Under-resourced languages like Bengali lack supporting resources that languages like English have. This dataset was collected from multiple Bengali news sources to provide several classification benchmarks for hate speech detection, document classification and sentiment analysis. ### Source Data #### Initial Data Collection and Normalization Bengali articles were collected from a Bengali Wikipedia dump, Bengali news articles, news dumps of TV channels, books, blogs, sports portal and social media. Emphasis was placed on Facebook pages and newspaper sources because they have about 50 million followers and is a common source of opinion and hate speech. The full dataset consists of 250 million articles and is currently being prepared. This is a subset of the full dataset. #### Who are the source language producers? The source language producers are Bengali authors and users who interact with these various forms of Bengali media. ### Annotations #### Annotation process The data was annotated by manually identifying freqently occurring terms in texts containing hate speech and references to specific entities. The authors also prepared normalized frequency vectors of 175 abusive terms that are commonly used to express hate in Bengali. A hate label is assigned if at least one of these terms exists in the text. Annotator's were provided with unbiased text only contents to make the decision. Non-hate statements were removed from the list and the category of hate was further divided into political, personal, gender abusive, geopolitical and religious. To reduce possible bias, each label was assigned based on a majority voting on the annotator's opinions and Cohen's Kappa was computed to measure inter-annotator agreement. #### Who are the annotators? Three native Bengali speakers and two linguists annotated the dataset which was then reviewed and validated by three experts (one South Asian linguist and two native speakers). ### Personal and Sensitive Information The dataset contains very sensitive and highly offensive comments in a religious, political and gendered context. Some of the comments are directed towards contemporary public figures like politicians, religious leaders, celebrities and athletes. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of the dataset is to improve hate speech detection in Bengali. The growth of social media has enabled people to express hate freely online and there has been a lot of focus on detecting hate speech for highly resourced languages like English. The use of hate speech is pervasive, like any other major language, which can have serious and deadly consequences. Failure to react to hate speech renders targeted minorities more vulnerable to attack and it can also create indifference towards their treatment from majority populations. ### Discussion of Biases The dataset was collected using a bootstrapping approach. An initial search was made for specific types of texts, articles and tweets containing common harassment directed at targeting characteristics. As a result, this dataset contains extremely offensive content that is disturbing. In addition, Facebook pages and newspaper sources were emphasized because they are well-known for having hate and harassment issues. ### Other Known Limitations The dataset contains racist, sexist, homophobic and offensive comments. It is collected and annotated for research related purposes only. ## Additional Information ### Dataset Curators The dataset was curated by Md. Rezaul Karim, Sumon Kanti Dey, Bharathi Raja Chakravarthi, John McCrae and Michael Cochez. ### Licensing Information This dataset is licensed under the MIT License. ### Citation Information ``` @inproceedings{karim2020BengaliNLP, title={Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network}, author={Karim, Md. Rezaul and Chakravarti, Bharathi Raja and P. McCrae, John and Cochez, Michael}, booktitle={7th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA,2020)}, publisher={IEEE}, year={2020} } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.
bnl_newspapers	--- annotations_creators: - no-annotation language_creators: - found language: - ar - da - de - fi - fr - lb - nl - pt license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling pretty_name: BnL Historical Newspapers dataset_info: features: - name: id dtype: string - name: source dtype: string - name: url dtype: string - name: title dtype: string - name: ispartof dtype: string - name: text dtype: string - name: pub_date dtype: timestamp[s] - name: publisher dtype: string - name: language dtype: string - name: article_type dtype: class_label: names: '0': ADVERTISEMENT_SECTION '1': BIBLIOGRAPHY '2': CHAPTER '3': INDEX '4': CONTRIBUTION '5': TABLE_OF_CONTENTS '6': WEATHER '7': SHIPPING '8': SECTION '9': ARTICLE '10': TITLE_SECTION '11': DEATH_NOTICE '12': SUPPLEMENT '13': TABLE '14': ADVERTISEMENT '15': CHART_DIAGRAM '16': ILLUSTRATION '17': ISSUE - name: extent dtype: int32 config_name: processed splits: - name: train num_bytes: 1611620156 num_examples: 537558 download_size: 1224029060 dataset_size: 1611620156 --- # Dataset Card for BnL Historical Newspapers ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://data.bnl.lu/data/historical-newspapers/ - Repository: - Paper: - Leaderboard: - Point of Contact: [email protected] ### Dataset Summary The BnL has digitised over 800.000 pages of Luxembourg newspapers. This dataset currently has one configuration covering a subset of these newspapers, which sit under the "Processed Datasets" collection. The BNL: > processed all newspapers and monographs that are in the public domain and extracted the full text and associated meta data of every single article, section, advertisement… The result is a large number of small, easy to use XML files formatted using Dublin Core. [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure The dataset currently contains a single configuration. ### Data Instances An example instance from the datasets: ``` python {'id': 'https://persist.lu/ark:/70795/wx8r4c/articles/DTL47', 'article_type': 8, 'extent': 49, 'ispartof': 'Luxemburger Wort', 'pub_date': datetime.datetime(1853, 3, 23, 0, 0), 'publisher': 'Verl. der St-Paulus-Druckerei', 'source': 'newspaper/luxwort/1853-03-23', 'text': 'Asien. Eine neue Nedcrland-Post ist angekommen mil Nachrichten aus Calcutta bis zum 5. Febr.; Vom» vay, 12. Febr. ; Nangun und HongKong, 13. Jan. Die durch die letzte Post gebrachle Nachricht, der König von Ava sei durch seinen Bruder enlhronl worden, wird bestätigt. (K. Z.) Verantwortl. Herausgeber, F. Schümann.', 'title': 'Asien.', 'url': 'http://www.eluxemburgensia.lu/webclient/DeliveryManager?pid=209701#panel:pp\|issue:209701\|article:DTL47', 'language': 'de' } ``` ### Data Fields - 'id': This is a unique and persistent identifier using ARK. - 'article_type': The type of the exported data, possible values ('ADVERTISEMENT_SECTION', 'BIBLIOGRAPHY', 'CHAPTER', 'INDEX', 'CONTRIBUTION', 'TABLE_OF_CONTENTS', 'WEATHER', 'SHIPPING', 'SECTION', 'ARTICLE', 'TITLE_SECTION', 'DEATH_NOTICE', 'SUPPLEMENT', 'TABLE', 'ADVERTISEMENT', 'CHART_DIAGRAM', 'ILLUSTRATION', 'ISSUE') - 'extent': The number of words in the text field - 'ispartof: The complete title of the source document e.g. “Luxemburger Wort”. - 'pub_date': The publishing date of the document e.g “1848-12-15” - 'publisher':The publisher of the document e.g. “Verl. der St-Paulus-Druckerei”. - 'source': Describes the source of the document. For example <dc:source>newspaper/luxwort/1848-12-15</dc:source> means that this article comes from the newspaper “luxwort” (ID for Luxemburger Wort) issued on 15.12.1848. - 'text': The full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines. - 'title': The main title of the article, section, advertisement, etc. - 'url': The link to the BnLViewer on eluxemburgensia.lu to view the resource online. - 'language': The language of the text, possible values ('ar', 'da', 'de', 'fi', 'fr', 'lb', 'nl', 'pt') ### Data Splits This dataset contains a single split `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{bnl_newspapers, title={Historical Newspapers}, url={https://data.bnl.lu/data/historical-newspapers/}, author={ Bibliothèque nationale du Luxembourg}, ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
bookcorpus	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: BookCorpus size_categories: - 10M<n<100M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: bookcorpus dataset_info: features: - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 4853859824 num_examples: 74004228 download_size: 1179510242 dataset_size: 4853859824 --- # Dataset Card for BookCorpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://yknzhu.wixsite.com/mbweb](https://yknzhu.wixsite.com/mbweb) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.18 GB - Size of the generated dataset: 4.85 GB - Total amount of disk used: 6.03 GB ### Dataset Summary Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 1.18 GB - Size of the generated dataset: 4.85 GB - Total amount of disk used: 6.03 GB An example of 'train' looks as follows. ``` { "text": "But I traded all my life for some lovin' and some gold" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits \| name \| train \| \|----------\|-------:\| \|plain_text\|74004228\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The books have been crawled from https://www.smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information. A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241). ### Citation Information ``` @InProceedings{Zhu_2015_ICCV, title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books}, author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@richarddwang](https://github.com/richarddwang), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
bookcorpusopen	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: BookCorpusOpen size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: bookcorpus dataset_info: features: - name: title dtype: string - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 6643435392 num_examples: 17868 download_size: 2404269430 dataset_size: 6643435392 --- # Dataset Card for BookCorpusOpen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/soskek/bookcorpus/issues/27](https://github.com/soskek/bookcorpus/issues/27) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 2.40 GB - Size of the generated dataset: 6.64 GB - Total amount of disk used: 9.05 GB ### Dataset Summary Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This version of bookcorpus has 17868 dataset items (books). Each item contains two fields: title and text. The title is the name of the book (just the file name) while text contains unprocessed book text. The bookcorpus has been prepared by Shawn Presser and is generously hosted by The-Eye. The-Eye is a non-profit, community driven platform dedicated to the archiving and long-term preservation of any and all data including but by no means limited to... websites, books, games, software, video, audio, other digital-obscura and ideas. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 2.40 GB - Size of the generated dataset: 6.64 GB - Total amount of disk used: 9.05 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\"\\n\\nzONE\\n\\n## The end and the beginning\\n\\nby\\n\\nPhilip F. Blood\\n\\nSMASHWORDS EDITION\\n\\nVersion 3.55\\n\\nPUBLISHED BY:\\n\\nPhi...", "title": "zone-the-end-and-the-beginning.epub.txt" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `title`: a `string` feature. - `text`: a `string` feature. ### Data Splits \| name \|train\| \|----------\|----:\| \|plain_text\|17868\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The books have been crawled from smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information. A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241) ### Citation Information ``` @InProceedings{Zhu_2015_ICCV, title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books}, author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} } ``` ### Contributions Thanks to [@vblagoje](https://github.com/vblagoje) for adding this dataset.
boolq	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: boolq pretty_name: BoolQ dataset_info: features: - name: question dtype: string - name: answer dtype: bool - name: passage dtype: string splits: - name: train num_bytes: 5829592 num_examples: 9427 - name: validation num_bytes: 1998190 num_examples: 3270 download_size: 8764539 dataset_size: 7827782 --- # Dataset Card for Boolq ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/google-research-datasets/boolean-questions](https://github.com/google-research-datasets/boolean-questions) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 8.77 MB - Size of the generated dataset: 7.83 MB - Total amount of disk used: 16.59 MB ### Dataset Summary BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 8.77 MB - Size of the generated dataset: 7.83 MB - Total amount of disk used: 16.59 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answer": false, "passage": "\"All biomass goes through at least some of these steps: it needs to be grown, collected, dried, fermented, distilled, and burned...", "question": "does ethanol take more energy make that produces" } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `answer`: a `bool` feature. - `passage`: a `string` feature. ### Data Splits \| name \|train\|validation\| \|-------\|----:\|---------:\| \|default\| 9427\| 3270\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information BoolQ is released under the [Creative Commons Share-Alike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @inproceedings{clark2019boolq, title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions}, author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina}, booktitle = {NAACL}, year = {2019}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
bprec	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - pl license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-retrieval task_ids: - entity-linking-retrieval pretty_name: bprec dataset_info: - config_name: default features: - name: id dtype: int32 - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: tele num_bytes: 2739015 num_examples: 2391 - name: electro num_bytes: 125999 num_examples: 382 - name: cosmetics num_bytes: 1565263 num_examples: 2384 - name: banking num_bytes: 446944 num_examples: 561 download_size: 8006167 dataset_size: 4877221 - config_name: all features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 4937658 num_examples: 5718 download_size: 8006167 dataset_size: 4937658 - config_name: tele features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 2758147 num_examples: 2391 download_size: 4569708 dataset_size: 2758147 - config_name: electro features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 130205 num_examples: 382 download_size: 269917 dataset_size: 130205 - config_name: cosmetics features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 1596259 num_examples: 2384 download_size: 2417388 dataset_size: 1596259 - config_name: banking features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 453119 num_examples: 561 download_size: 749154 dataset_size: 453119 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [bprec homepage](https://clarin-pl.eu/dspace/handle/11321/736) - Repository: [bprec repository](https://gitlab.clarin-pl.eu/team-semantics/semrel-extraction) - Paper: [bprec paper](https://www.aclweb.org/anthology/2020.lrec-1.233.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary Brand-Product Relation Extraction Corpora in Polish ### Supported Tasks and Leaderboards NER, Entity linking ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - id: int identifier of a text - text: string text, for example a consumer comment on the social media - ner: extracted entities and their relationship - source and target: a pair of entities identified in the text - from: int value representing starting character of the entity - text: string value with the entity text - to: int value representing end character of the entity - type: one of pre-identified entity types: - PRODUCT_NAME - PRODUCT_NAME_IMP - PRODUCT_NO_BRAND - BRAND_NAME - BRAND_NAME_IMP - VERSION - PRODUCT_ADJ - BRAND_ADJ - LOCATION - LOCATION_IMP ### Data Splits No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts: - tele - electro - cosmetics - banking ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{inproceedings, author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka}, year = {2020}, month = {05}, pages = {}, title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations} } ``` ### Contributions Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset.
break_data	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: - open-domain-abstractive-qa paperswithcode_id: break pretty_name: BREAK dataset_info: - config_name: QDMR-high-level features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string splits: - name: test num_bytes: 482339 num_examples: 3195 - name: train num_bytes: 5148086 num_examples: 17503 - name: validation num_bytes: 914780 num_examples: 3130 download_size: 15971078 dataset_size: 6545205 - config_name: QDMR-high-level-lexicon features: - name: source dtype: string - name: allowed_tokens dtype: string splits: - name: test num_bytes: 4240755 num_examples: 3195 - name: train num_bytes: 23234518 num_examples: 17503 - name: validation num_bytes: 4158679 num_examples: 3130 download_size: 15971078 dataset_size: 31633952 - config_name: QDMR features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string splits: - name: test num_bytes: 900632 num_examples: 8069 - name: train num_bytes: 12790466 num_examples: 44321 - name: validation num_bytes: 2237472 num_examples: 7760 download_size: 15971078 dataset_size: 15928570 - config_name: QDMR-lexicon features: - name: source dtype: string - name: allowed_tokens dtype: string splits: - name: test num_bytes: 10331822 num_examples: 8069 - name: train num_bytes: 56913064 num_examples: 44321 - name: validation num_bytes: 9936933 num_examples: 7760 download_size: 15971078 dataset_size: 77181819 - config_name: logical-forms features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string - name: program dtype: string splits: - name: test num_bytes: 927038 num_examples: 8006 - name: train num_bytes: 19821676 num_examples: 44098 - name: validation num_bytes: 3504893 num_examples: 7719 download_size: 15971078 dataset_size: 24253607 --- # Dataset Card for "break_data" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/allenai/Break](https://github.com/allenai/Break) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 79.86 MB - Size of the generated dataset: 155.55 MB - Total amount of disk used: 235.39 MB ### Dataset Summary Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations (QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases. This repository contains the Break dataset along with information on the exact data format. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### QDMR - Size of downloaded dataset files: 15.97 MB - Size of the generated dataset: 15.93 MB - Total amount of disk used: 31.90 MB An example of 'validation' looks as follows. ``` { "decomposition": "return flights ;return #1 from denver ;return #2 to philadelphia ;return #3 if available", "operators": "['select', 'filter', 'filter', 'filter']", "question_id": "ATIS_dev_0", "question_text": "what flights are available tomorrow from denver to philadelphia ", "split": "dev" } ``` #### QDMR-high-level - Size of downloaded dataset files: 15.97 MB - Size of the generated dataset: 6.54 MB - Total amount of disk used: 22.51 MB An example of 'train' looks as follows. ``` { "decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4", "operators": "['select', 'filter', 'filter', 'filter', 'project']", "question_id": "ATIS_dev_102", "question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ", "split": "dev" } ``` #### QDMR-high-level-lexicon - Size of downloaded dataset files: 15.97 MB - Size of the generated dataset: 31.64 MB - Total amount of disk used: 47.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'he', 'distinct', 'House', 'two', 'at least', 'or ', 'date', 'o...", "source": "What office, also held by a member of the Maine House of Representatives, did James K. Polk hold before he was president?" } ``` #### QDMR-lexicon - Size of downloaded dataset files: 15.97 MB - Size of the generated dataset: 77.19 MB - Total amount of disk used: 93.16 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'distinct', 'two', 'at least', 'or ', 'date', 'on ', '@@14@@', ...", "source": "what flights are available tomorrow from denver to philadelphia " } ``` #### logical-forms - Size of downloaded dataset files: 15.97 MB - Size of the generated dataset: 24.25 MB - Total amount of disk used: 40.22 MB An example of 'train' looks as follows. ``` { "decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4", "operators": "['select', 'filter', 'filter', 'filter', 'project']", "program": "some program", "question_id": "ATIS_dev_102", "question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ", "split": "dev" } ``` ### Data Fields The data fields are the same among all splits. #### QDMR - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. #### QDMR-high-level - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. #### QDMR-high-level-lexicon - `source`: a `string` feature. - `allowed_tokens`: a `string` feature. #### QDMR-lexicon - `source`: a `string` feature. - `allowed_tokens`: a `string` feature. #### logical-forms - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. - `program`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-----------------------\|----:\|---------:\|---:\| \|QDMR \|44321\| 7760\|8069\| \|QDMR-high-level \|17503\| 3130\|3195\| \|QDMR-high-level-lexicon\|17503\| 3130\|3195\| \|QDMR-lexicon \|44321\| 7760\|8069\| \|logical-forms \|44098\| 7719\|8006\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Wolfson2020Break, title={Break It Down: A Question Understanding Benchmark}, author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan}, journal={Transactions of the Association for Computational Linguistics}, year={2020}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
brwac	--- annotations_creators: - no-annotation language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: brwac pretty_name: BrWaC dataset_info: features: - name: doc_id dtype: string - name: title dtype: string - name: uri dtype: string - name: text sequence: - name: paragraphs sequence: string splits: - name: train num_bytes: 18828421452 num_examples: 3530796 download_size: 0 dataset_size: 18828421452 --- # Dataset Card for BrWaC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [BrWaC homepage](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC) - Repository: [BrWaC repository](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC) - Paper: [The brWaC Corpus: A New Open Resource for Brazilian Portuguese](https://www.aclweb.org/anthology/L18-1686/) - Point of Contact: [Jorge A. Wagner Filho](mailto:[email protected]) ### Dataset Summary The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework, which was made public for research purposes. The current corpus version, released in January 2017, is composed by 3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available solely for academic research purposes, and you agreed not to use it for any commercial applications. Manually download at https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Portuguese ## Dataset Structure ### Data Instances An example from the BrWaC dataset looks as follows: ``` { "doc_id": "netg-1afc73", "text": { "paragraphs": [ [ "Conteúdo recente" ], [ "ESPUMA MARROM CHAMADA \"NINGUÉM MERECE\"" ], [ "31 de Agosto de 2015, 7:07 , por paulo soavinski - \| No one following this article yet." ], [ "Visualizado 202 vezes" ], [ "JORNAL ELETRÔNICO DA ILHA DO MEL" ], [ "Uma espuma marrom escuro tem aparecido com frequência na Praia de Fora.", "Na faixa de areia ela aparece disseminada e não chama muito a atenção.", "No Buraco do Aipo, com muitas pedras, ela aparece concentrada.", "É fácil saber que esta espuma estranha está lá, quando venta.", "Pequenos algodões de espuma começam a flutuar no espaço, pertinho da Praia do Saquinho.", "Quem pode ajudar na coleta deste material, envio a laboratório renomado e pagamento de análises, favor entrar em contato com o site." ] ] }, "title": "ESPUMA MARROM CHAMADA ‟NINGUÃÂM MERECE‟ - paulo soavinski", "uri": "http://blogoosfero.cc/ilhadomel/pousadasilhadomel.com.br/espuma-marrom-chamada-ninguem-merece" } ``` ### Data Fields - `doc_id`: The document ID - `title`: The document title - `uri`: URI where the document was extracted from - `text`: A list of document paragraphs (with a list of sentences in it as a list of strings) ### Data Splits The data is only split into train set with size of 3530796 samples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{wagner2018brwac, title={The brwac corpus: A new open resource for brazilian portuguese}, author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
bsd_ja_en	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - ja license: - cc-by-nc-sa-4.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: business-scene-dialogue pretty_name: Business Scene Dialogue tags: - business-conversations-translation dataset_info: features: - name: id dtype: string - name: tag dtype: string - name: title dtype: string - name: original_language dtype: string - name: 'no' dtype: int32 - name: en_speaker dtype: string - name: ja_speaker dtype: string - name: en_sentence dtype: string - name: ja_sentence dtype: string splits: - name: train num_bytes: 4778409 num_examples: 20000 - name: test num_bytes: 493038 num_examples: 2120 - name: validation num_bytes: 477964 num_examples: 2051 download_size: 8135045 dataset_size: 5749411 --- # Dataset Card for Business Scene Dialogue ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/) - Repository: [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/) - Paper: [Rikters et al., 2019](https://www.aclweb.org/anthology/D19-5204) - Leaderboard: - Point of Contact: Matīss Rikters ### Dataset Summary This is the Business Scene Dialogue (BSD) dataset, a Japanese-English parallel corpus containing written conversations in various business scenarios. The dataset was constructed in 3 steps: 1) selecting business scenes, 2) writing monolingual conversation scenarios according to the selected scenes, and 3) translating the scenarios into the other language. Half of the monolingual scenarios were written in Japanese and the other half were written in English. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English, Japanese. ## Dataset Structure ### Data Instances Each instance contains a conversation identifier, a sentence number that indicates its position within the conversation, speaker name in English and Japanese, text in English and Japanese, original language, scene of the scenario (tag), and title of the scenario (title). ```python { "id": "190315_E004_13", "no": 14, "speaker": "Mr. Sam Lee", "ja_speaker": "サムリーさん", "en_sentence": "Would you guys consider a different scheme?", "ja_sentence": "別の事業案も考慮されますか？", "original_language": "en", "tag": "phone call", "title": "Phone: Review spec and scheme" } ``` ### Data Fields - id: dialogue identifier - no: sentence pair number within a dialogue - en_speaker: speaker name in English - ja_speaker: speaker name in Japanese - en_sentence: sentence in English - ja_sentence: sentence in Japanese - original_language: language in which monolingual scenario was written - tag: scenario - title: scenario title ### Data Splits - There are a total of 24171 sentences / 808 business scenarios. - Train: 20000 sentences / 670 scenarios - Dev: 2051 sentences / 69 scenarios - Test: 2120 sentences / 69 scenarios ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This dataset was released under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. ### Citation Information ``` @inproceedings{rikters-etal-2019-designing, title = "Designing the Business Conversation Corpus", author = "Rikters, Mat{\=\i}ss and Ri, Ryokan and Li, Tong and Nakazawa, Toshiaki", booktitle = "Proceedings of the 6th Workshop on Asian Translation", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5204", doi = "10.18653/v1/D19-5204", pages = "54--61" } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.
bswac	--- annotations_creators: - no-annotation language_creators: - found language: - bs license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: BsWac dataset_info: features: - name: sentence dtype: string config_name: bswac splits: - name: train num_bytes: 9156258478 num_examples: 354581267 download_size: 1988514951 dataset_size: 9156258478 --- # Dataset Card for BsWac ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://nlp.ffzg.hr/resources/corpora/bswac/ - Repository: https://www.clarin.si/repository/xmlui/handle/11356/1062 - Paper: http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic14-bs.pdf - Leaderboard: - Point of Contact: [Nikola Ljubešič](mailto:[email protected]) ### Dataset Summary The Bosnian web corpus bsWaC was built by crawling the .ba top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Bosnian vs. Croatian vs. Serbian). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Dataset is monolingual in Bosnian language. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @misc{11356/1062, title = {Bosnian web corpus {bsWaC} 1.1}, author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip}, url = {http://hdl.handle.net/11356/1062}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)}, year = {2016} } ``` ### Contributions Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset.
c3	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - zh license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: c3 pretty_name: C3 dataset_info: - config_name: mixed features: - name: documents sequence: string - name: document_id dtype: string - name: questions sequence: - name: question dtype: string - name: answer dtype: string - name: choice sequence: string splits: - name: train num_bytes: 2710513 num_examples: 3138 - name: test num_bytes: 891619 num_examples: 1045 - name: validation num_bytes: 910799 num_examples: 1046 download_size: 5481785 dataset_size: 4512931 - config_name: dialog features: - name: documents sequence: string - name: document_id dtype: string - name: questions sequence: - name: question dtype: string - name: answer dtype: string - name: choice sequence: string splits: - name: train num_bytes: 2039819 num_examples: 4885 - name: test num_bytes: 646995 num_examples: 1627 - name: validation num_bytes: 611146 num_examples: 1628 download_size: 4352392 dataset_size: 3297960 --- # Dataset Card for C3 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: []() - Repository: [link]() - Paper: []() - Leaderboard: []() - Point of Contact: []() ### Dataset Summary Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @article{sun2019investigating, title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension}, author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire}, journal={Transactions of the Association for Computational Linguistics}, year={2020}, url={https://arxiv.org/abs/1904.09679v3} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
c4	--- pretty_name: C4 annotations_creators: - no-annotation language_creators: - found language: - en license: - odc-by multilinguality: - multilingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: c4 dataset_info: - config_name: en features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 828589180707 num_examples: 364868892 - name: validation num_bytes: 825767266 num_examples: 364608 download_size: 326778635540 dataset_size: 1657178361414 - config_name: en.noblocklist features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 1029628201361 num_examples: 393391519 - name: validation num_bytes: 1025606012 num_examples: 393226 download_size: 406611392434 dataset_size: 2059256402722 - config_name: realnewslike features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 38165657946 num_examples: 13799838 - name: validation num_bytes: 37875873 num_examples: 13863 download_size: 15419740744 dataset_size: 76331315892 - config_name: en.noclean features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 6715509699938 num_examples: 1063805381 - name: validation num_bytes: 6706356913 num_examples: 1065029 download_size: 2430376268625 dataset_size: 6722216056851 --- # Dataset Card for C4 ## Table of Contents - [Dataset Card for C4](#dataset-card-for-c4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://huggingface.co/datasets/allenai/c4 - Paper: https://arxiv.org/abs/1910.10683 ### Dataset Summary A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4 It comes in four variants: - `en`: 305GB in JSON format - `en.noblocklist`: 380GB in JSON format - `en.noclean`: 2.3TB in JSON format - `realnewslike`: 15GB in JSON format The `en.noblocklist` variant is exactly the same as the `en` variant, except we turned off the so-called "badwords filter", which removes all documents that contain words from the lists at https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words. ### Supported Tasks and Leaderboards C4 is mainly intended to pretrain language models and word representations. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances An example form the `en` config is: ``` { 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/', 'text': 'Beginners BBQ Class Taking Place in Missoula!\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z' } ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp as a string ### Data Splits \| name \| train \|validation\| \|----------------\|--------:\|---------:\| \| en \|364868892\| 364608\| \| en.noblocklist \|393391519\| 393226\| \| en.noclean \| ?\| ?\| \| realnewslike \| 13799838\| 13863\| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization C4 dataset is a collection of about 750GB of English-language text sourced from the public Common Crawl web scrape. It includes heuristics to extract only natural language (as opposed to boilerplate and other gibberish) in addition to extensive deduplication. You can find the code that has been used to build this dataset in [c4.py](https://github.com/tensorflow/datasets/blob/5952d3d60d60e1727786fa7a9a23d24bb463d4d6/tensorflow_datasets/text/c4.py) by Tensorflow Datasets. The dataset was explicitly designed to be English only: any page that was not given a probability of at least 99% of being English by [langdetect](https://github.com/Mimino666/langdetect) was discarded. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
cail2018	--- annotations_creators: - found language_creators: - found language: - zh license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: chinese-ai-and-law-cail-2018 pretty_name: CAIL 2018 tags: - judgement-prediction dataset_info: features: - name: fact dtype: string - name: relevant_articles sequence: int32 - name: accusation sequence: string - name: punish_of_money dtype: float32 - name: criminals sequence: string - name: death_penalty dtype: bool - name: imprisonment dtype: float32 - name: life_imprisonment dtype: bool splits: - name: exercise_contest_train num_bytes: 220112732 num_examples: 154592 - name: exercise_contest_valid num_bytes: 21702157 num_examples: 17131 - name: exercise_contest_test num_bytes: 41057634 num_examples: 32508 - name: first_stage_train num_bytes: 1779657510 num_examples: 1710856 - name: first_stage_test num_bytes: 244335194 num_examples: 217016 - name: final_test num_bytes: 44194707 num_examples: 35922 download_size: 984551626 dataset_size: 2351059934 --- --- # Dataset Card for CAIL 2018 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/thunlp/CAIL/blob/master/README_en.md) - Repository: [Github](https://github.com/thunlp/CAIL) - Paper: [Arxiv](https://arxiv.org/abs/1807.02478) - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.
caner	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ar license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: CANER dataset_info: features: - name: token dtype: string - name: ner_tag dtype: class_label: names: '0': Allah '1': Book '2': Clan '3': Crime '4': Date '5': Day '6': Hell '7': Loc '8': Meas '9': Mon '10': Month '11': NatOb '12': Number '13': O '14': Org '15': Para '16': Pers '17': Prophet '18': Rlig '19': Sect '20': Time splits: - name: train num_bytes: 5095721 num_examples: 258240 download_size: 17063406 dataset_size: 5095721 --- # Dataset Card for CANER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: [Classical-Arabic-Named-Entity-Recognition-Corpus](https://github.com/RamziSalah) - Paper: [Researchgate](https://www.researchgate.net/publication/330075080_BUILDING_THE_CLASSICAL_ARABIC_NAMED_ENTITY_RECOGNITION_CORPUS_CANERCORPUS) - Leaderboard: - Point of Contact: ### Dataset Summary The Classical Arabic Named Entity Recognition corpus is a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities. ### Supported Tasks and Leaderboards - Named Entity Recognition ### Languages Classical Arabic ## Dataset Structure ### Data Instances An example from the dataset: ``` {'ner_tag': 1, 'token': 'الجامع'} ``` Where 1 stands for "Book" ### Data Fields - `id`: id of the sample - `token`: the tokens of the example text - `ner_tag`: the NER tags of each token The NER tags correspond to this list: ``` "Allah", "Book", "Clan", "Crime", "Date", "Day", "Hell", "Loc", "Meas", "Mon", "Month", "NatOb", "Number", "O", "Org", "Para", "Pers", "Prophet", "Rlig", "Sect", "Time" ``` ### Data Splits Training splits only ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Ramzi Salah and Lailatul Qadri Zakaria ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @article{article, author = {Salah, Ramzi and Zakaria, Lailatul}, year = {2018}, month = {12}, pages = {}, title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)}, volume = {96}, journal = {Journal of Theoretical and Applied Information Technology} } ### Contributions Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset.
capes	--- annotations_creators: - found language_creators: - found language: - en - pt license: - unknown multilinguality: - multilingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: capes pretty_name: CAPES tags: - dissertation-abstracts-translation - theses-translation dataset_info: features: - name: translation dtype: translation: languages: - en - pt config_name: en-pt splits: - name: train num_bytes: 472484364 num_examples: 1157610 download_size: 162229298 dataset_size: 472484364 --- # Dataset Card for CAPES ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:[Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES](https://sites.google.com/view/felipe-soares/datasets#h.p_kxOR6EhHm2a6) - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil. The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were collected and aligned using the Hunalign algorithm. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{soares2018parallel, title={A Parallel Corpus of Theses and Dissertations Abstracts}, author={Soares, Felipe and Yamashita, Gabrielli Harumi and Anzanello, Michel Jose}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={345--352}, year={2018}, organization={Springer} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
casino	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-generation - fill-mask task_ids: - dialogue-modeling pretty_name: Campsite Negotiation Dialogues paperswithcode_id: casino dataset_info: features: - name: chat_logs list: - name: text dtype: string - name: task_data struct: - name: data dtype: string - name: issue2youget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: issue2theyget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: id dtype: string - name: participant_info struct: - name: mturk_agent_1 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: mturk_agent_2 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: annotations list: list: string splits: - name: train num_bytes: 3211555 num_examples: 1030 download_size: 4300019 dataset_size: 3211555 --- # Dataset Card for Casino ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Github: Kushal Chawla CaSiNo](https://github.com/kushalchawla/CaSiNo) - Paper: [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://aclanthology.org/2021.naacl-main.254.pdf) - Point of Contact: [Kushal Chawla]([email protected]) ### Dataset Summary We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations. This helps to overcome the limitations of prior negotiation datasets such as Deal or No Deal and Craigslist Bargain. Each dialogue consists of rich meta-data including participant demographics, personality, and their subjective evaluation of the negotiation in terms of satisfaction and opponent likeness. ### Supported Tasks and Leaderboards Train end-to-end models for negotiation ### Languages English ## Dataset Structure ### Data Instances ``` { "chat_logs": [ { "text": "Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "task_data": {}, "id": "mturk_agent_1" }, ... ], "participant_info": { "mturk_agent_1": { "value2issue": ... "value2reason": ... "outcomes": ... "demographics": ... "personality": ... }, "mturk_agent_2": ... }, "annotations": [ ["Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "promote-coordination,elicit-pref"], ... ] } ``` ### Data Fields - `chat_logs`: The negotiation dialogue between two participants - `text`: The dialogue utterance - `task_data`: Meta-data associated with the utterance such as the deal submitted by a participant - `id`: The ID of the participant who typed this utterance - `participant_info`: Meta-data about the two participants in this conversation - `mturk_agent_1`: For the first participant (Note that 'first' is just for reference. There is no order between the participants and any participant can start the conversation) - `value2issue`: The priority order of this participant among Food, Water, Firewood - `value2reason`: The personal arguments given by the participants themselves, consistent with the above preference order. This preference order and these arguments were submitted before the negotiation began. - `outcomes`: The negotiation outcomes for this participant including objective and subjective assessment. - `demographics`: Demographic attributes of the participant in terms of age, gender, ethnicity, and education. - `personality`: Personality attributes for this participant, in terms of Big-5 and Social Value Orientation - `mturk_agent_2`: For the second participant; follows the same structure as above - `annotations`: Strategy annotations for each utterance in the dialogue, wherever available. The first element represents the utterance and the second represents a comma-separated list of all strategies present in that utterance. ### Data Splits No default data split has been provided. Hence, all 1030 data points are under the 'train' split. \| \| Train \| \| ----- \| ----- \| \| total dialogues \| 1030 \| \| annotated dialogues \| 396 \| ## Dataset Creation ### Curation Rationale The dataset was collected to address the limitations in prior negotiation datasets from the perspective of downstream applications in pedagogy and conversational AI. Please refer to the original paper published at NAACL 2021 for details about the rationale and data curation steps ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Source Data #### Initial Data Collection and Normalization The dialogues were crowdsourced on Amazon Mechanical Turk. The strategy annotations were performed by expert annotators (first three authors of the paper). Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). #### Who are the source language producers? The primary producers are Turkers on Amazon Mechanical Turk platform. Two turkers were randomly paired with each other to engage in a negotiation via a chat interface. Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Annotations #### Annotation process From the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for this dataset: >Three expert annotators independently annotated 396 dialogues containing 4615 utterances. The annotation guidelines were iterated over a subset of 5 dialogues, while the reliability scores were computed on a different subset of 10 dialogues. We use the nominal form of Krippendorff’s alpha (Krippendorff, 2018) to measure the inter-annotator agreement. We provide the annotation statistics in Table 2. Although we release all the annotations, we skip Coordination and Empathy for our analysis in this work, due to higher subjectivity resulting in relatively lower reliability scores. #### Who are the annotators? Three expert annotators (first three authors of the paper). ### Personal and Sensitive Information All personally identifiable information about the participants such as MTurk Ids or HIT Ids was removed before releasing the data. ## Considerations for Using the Data ### Social Impact of Dataset Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Discussion of Biases Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Other Known Limitations Please refer to Section 7 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ## Additional Information ### Dataset Curators Corresponding Author: Kushal Chawla (`[email protected]`)\ Affiliation: University of Southern California\ Please refer to the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for the complete author list. ### Licensing Information The project is licensed under CC-by-4.0 ### Citation Information ``` @inproceedings{chawla2021casino, title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems}, author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan}, booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, pages={3167--3185}, year={2021} } ``` ### Contributions Thanks to [Kushal Chawla](https://kushalchawla.github.io/) for adding this dataset.
catalonia_independence	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - ca - es license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: cic pretty_name: Catalonia Independence Corpus configs: - catalan - spanish tags: - stance-detection dataset_info: - config_name: catalan features: - name: id_str dtype: string - name: TWEET dtype: string - name: LABEL dtype: class_label: names: '0': AGAINST '1': FAVOR '2': NEUTRAL splits: - name: train num_bytes: 1406250 num_examples: 6028 - name: test num_bytes: 469204 num_examples: 2010 - name: validation num_bytes: 473393 num_examples: 2010 download_size: 995415 dataset_size: 2348847 - config_name: spanish features: - name: id_str dtype: string - name: TWEET dtype: string - name: LABEL dtype: class_label: names: '0': AGAINST '1': FAVOR '2': NEUTRAL splits: - name: train num_bytes: 1507388 num_examples: 6046 - name: test num_bytes: 501783 num_examples: 2016 - name: validation num_bytes: 505092 num_examples: 2015 download_size: 1070281 dataset_size: 2514263 --- # Dataset Card for Catalonia Independence Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/ixa-ehu/catalonia-independence-corpus - Repository: https://github.com/ixa-ehu/catalonia-independence-corpus - Paper: [Multilingual Stance Detection: The Catalonia Independence Corpus](https://www.aclweb.org/anthology/2020.lrec-1.171/) - Leaderboard: - Point of Contact: [Rodrigo Agerri](https://github.com/ragerri) (corpus creator) ### Dataset Summary This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia. Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Spanish and Catalan ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.
cats_vs_dogs	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: cats-vs-dogs pretty_name: Cats Vs. Dogs dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 4219400 num_examples: 23410 download_size: 824887076 dataset_size: 4219400 --- # Dataset Card for Cats Vs. Dogs ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765) - Repository: - Paper: [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf) - Leaderboard: [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats) - Point of Contact: ### Dataset Summary A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page: > The Asirra data set > > Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords. > > Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface: > > Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs). ### Languages English. ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>, 'labels': 0 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits \| \| train \| \|---------------\|------:\| \| # of examples \| 23410 \| ## Dataset Creation ### Curation Rationale This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA: From the competition page: > Image recognition attacks > > While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. ### Source Data #### Initial Data Collection and Normalization This dataset is a subset of the Asirra dataset. From the competition page: > Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. #### Who are the source language producers? The users of [Petfinder.com](https://www.petfinder.com/). ### Annotations #### Annotation process The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/). #### Who are the annotators? The users of [Petfinder.com](https://www.petfinder.com/). ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.” ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, year = {2007}, month = {October}, publisher = {Association for Computing Machinery, Inc.}, url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.
cawac	--- annotations_creators: - no-annotation language_creators: - found language: - ca license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cawac pretty_name: caWaC dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 3987238444 num_examples: 24745986 download_size: 1620361999 dataset_size: 3987238444 --- # Dataset Card for caWaC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://nlp.ffzg.hr/resources/corpora/cawac/ - Repository: http://nlp.ffzg.hr/data/corpora/cawac.uniq.sortr.gz - Paper: http://www.lrec-conf.org/proceedings/lrec2014/pdf/841_Paper.pdf - Leaderboard: - Point of Contact: [Nikola Ljubešič](mailto:[email protected]) ### Dataset Summary caWaC is a 780-million-token web corpus of Catalan built from the .cat top-level-domain in late 2013. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Dataset is monolingual in Catalan language. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @inproceedings{DBLP:conf/lrec/LjubesicT14, author = {Nikola Ljubesic and Antonio Toral}, editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asunci{\'{o}}n Moreno and Jan Odijk and Stelios Piperidis}, title = {caWaC - {A} web corpus of Catalan and its application to language modeling and machine translation}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation, {LREC} 2014, Reykjavik, Iceland, May 26-31, 2014}, pages = {1728--1732}, publisher = {European Language Resources Association {(ELRA)}}, year = {2014}, url = {http://www.lrec-conf.org/proceedings/lrec2014/summaries/841.html}, timestamp = {Mon, 19 Aug 2019 15:23:35 +0200}, biburl = {https://dblp.org/rec/conf/lrec/LjubesicT14.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
cbt	--- pretty_name: Children’s Book Test (CBT) annotations_creators: - machine-generated language_creators: - found language: - en license: - gfdl multilinguality: - monolingual size_categories: - 100K<n<1M - n<1K source_datasets: - original task_categories: - other - question-answering task_ids: - multiple-choice-qa paperswithcode_id: cbt configs: - CN - NE - P - V - raw dataset_info: - config_name: raw features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 25741580 num_examples: 98 - name: test num_bytes: 1528704 num_examples: 5 - name: validation num_bytes: 1182657 num_examples: 5 download_size: 120547669 dataset_size: 28452941 - config_name: V features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 252177649 num_examples: 105825 - name: test num_bytes: 5806625 num_examples: 2500 - name: validation num_bytes: 4556425 num_examples: 2000 download_size: 120547669 dataset_size: 262540699 - config_name: P features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 852852601 num_examples: 334030 - name: test num_bytes: 6078048 num_examples: 2500 - name: validation num_bytes: 4776981 num_examples: 2000 download_size: 120547669 dataset_size: 863707630 - config_name: NE features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 253551931 num_examples: 108719 - name: test num_bytes: 5707734 num_examples: 2500 - name: validation num_bytes: 4424316 num_examples: 2000 download_size: 120547669 dataset_size: 263683981 - config_name: CN features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 301730151 num_examples: 120769 - name: test num_bytes: 6138376 num_examples: 2500 - name: validation num_bytes: 4737257 num_examples: 2000 download_size: 120547669 dataset_size: 312605784 --- # Dataset Card for CBT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:[The bAbI project](https://research.fb.com/downloads/babi/) - Repository: - Paper: [arXiv Paper](https://arxiv.org/pdf/1511.02301.pdf) - Leaderboard: - Point of Contact: [Felix Hill](mailto:[email protected]) or [Antoine Bordes](mailto:[email protected]). ### Dataset Summary The Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available. This dataset contains four different configurations: - `V`: where the answers to the questions are verbs. - `P`: where the answers to the questions are pronouns. - `NE`: where the answers to the questions are named entities. - `CN`: where the answers to the questions are common nouns. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The data is present in English language as written by authors Lucy Maud Montgomery, Charles Dickens,Andrew Lang, etc. in story books for children. ## Dataset Structure ### Data Instances An instance from the `V` config: ``` {'answer': 'said', 'options': ['christening', 'existed', 'hear', 'knows', 'read', 'remarked', 'said', 'sitting', 'talking', 'wearing'], 'question': "`` They are very kind old ladies in their way , '' XXXXX the king ; `` and were nice to me when I was a boy . ''", 'sentences': ['This vexed the king even more than the queen , who was very clever and learned , and who had hated dolls when she was a child .', 'However , she , too in spite of all the books she read and all the pictures she painted , would have been glad enough to be the mother of a little prince .', 'The king was anxious to consult the fairies , but the queen would not hear of such a thing .', 'She did not believe in fairies : she said that they had never existed ; and that she maintained , though The History of the Royal Family was full of chapters about nothing else .', 'Well , at long and at last they had a little boy , who was generally regarded as the finest baby that had ever been seen .', 'Even her majesty herself remarked that , though she could never believe all the courtiers told her , yet he certainly was a fine child -- a very fine child .', 'Now , the time drew near for the christening party , and the king and queen were sitting at breakfast in their summer parlour talking over it .', 'It was a splendid room , hung with portraits of the royal ancestors .', 'There was Cinderella , the grandmother of the reigning monarch , with her little foot in her glass slipper thrust out before her .', 'There was the Marquis de Carabas , who , as everyone knows , was raised to the throne as prince consort after his marriage with the daughter of the king of the period .', 'On the arm of the throne was seated his celebrated cat , wearing boots .', 'There , too , was a portrait of a beautiful lady , sound asleep : this was Madame La Belle au Bois-dormant , also an ancestress of the royal family .', 'Many other pictures of celebrated persons were hanging on the walls .', "`` You have asked all the right people , my dear ? ''", 'said the king .', "`` Everyone who should be asked , '' answered the queen .", "`` People are so touchy on these occasions , '' said his majesty .", "`` You have not forgotten any of our aunts ? ''", "`` No ; the old cats ! ''", "replied the queen ; for the king 's aunts were old-fashioned , and did not approve of her , and she knew it ."]} ``` ### Data Fields For the `raw` config, the data fields are: - `title`: a `string` feature containing the title of the book present in the dataset. - `content`: a `string` feature containing the content of the book present in the dataset. For all other configs, the data fields are: - `sentences`: a `list` of `string` features containing 20 sentences from a book. - `question`: a `string` feature containing a question with blank marked as `XXXX` which is to be filled with one of the options. - `answer`: a `string` feature containing the answer. - `options`: a `list` of `string` features containing the options for the question. ### Data Splits The splits and corresponding sizes are: \| \|train \|test \|validation\| \|:--\|------:\|----:\|---------:\| \|raw\|98 \|5 \|5 \| \|V \|105825 \|2500 \|2000 \| \|P \|334030 \|2500 \|2000 \| \|CN \|120769 \|2500 \|2000 \| \|NE \|108719 \|2500 \|2000 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Children's Book Authors ### Annotations #### Annotation process From the [homepage](https://research.fb.com/downloads/babi/): >After allocating books to either training, validation or test sets, we formed example ‘questions’ from chapters in the book by enumerating 21 consecutive sentences. In each question, the first 20 sentences form the context, and a word is removed from the 21st sentence, which becomes the query. Models must identify the answer word among a selection of 10 candidate answers appearing in the context sentences and the query. For finer-grained analyses, we evaluated four classes of question by removing distinct types of word: Named Entities, (Common) Nouns, Verbs and Prepositions. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information ``` GNU Free Documentation License v1.3 ``` ### Citation Information ``` @misc{hill2016goldilocks, title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations}, author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston}, year={2016}, eprint={1511.02301}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.
cc100	--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu language_bcp47: - bn-Latn - hi-Latn - my-x-zawgyi - ta-Latn - te-Latn - ur-Latn - zh-Hans - zh-Hant license: - unknown multilinguality: - multilingual size_categories: - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc100 pretty_name: CC100 configs: - am - sr dataset_info: - config_name: am features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 935440775 num_examples: 3124561 download_size: 138821056 dataset_size: 935440775 - config_name: sr features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10299427460 num_examples: 35747957 download_size: 1578989320 dataset_size: 10299427460 - config_name: ka features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10228918845 num_examples: 31708119 download_size: 1100446372 dataset_size: 10228918845 --- # Dataset Card for CC100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://data.statmt.org/cc-100/ - Repository: None - Paper: https://www.aclweb.org/anthology/2020.acl-main.747.pdf, https://www.aclweb.org/anthology/2020.lrec-1.494.pdf - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by _rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. ### Supported Tasks and Leaderboards CC-100 is mainly inteded to pretrain language models and word represantations. ### Languages To load a language which isn't part of the config, all you need to do is specify the language code in the config. You can find the valid languages in Homepage section of Dataset Description: https://data.statmt.org/cc-100/ E.g. `dataset = load_dataset("cc100", lang="en")` ## Dataset Structure ### Data Instances An example from the `am` configuration: ``` {'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'} ``` Each data point is a paragraph of text. The paragraphs are presented in the original (unshuffled) order. Documents are separated by a data point consisting of a single newline character. ### Data Fields The data fields are: - id: id of the example - text: content as a string ### Data Splits Sizes of some configurations: \| name \|train\| \|----------\|----:\| \|am\|3124561\| \|sr\|35747957\| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Being constructed from Common Crawl, personal and sensitive information might be present. This must* be considered before training deep learning models with CC-100, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was prepared by [Statistical Machine Translation at the University of Edinburgh](https://www.statmt.org/ued/) using the [CC-Net](https://github.com/facebookresearch/cc_net) toolkit by Facebook Research. ### Licensing Information Statistical Machine Translation at the University of Edinburgh makes no claims of intellectual property on the work of preparation of the corpus. By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset. ### Citation Information ```bibtex @inproceedings{conneau-etal-2020-unsupervised, title = "Unsupervised Cross-lingual Representation Learning at Scale", author = "Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.747", doi = "10.18653/v1/2020.acl-main.747", pages = "8440--8451", abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.", } ``` ```bibtex @inproceedings{wenzek-etal-2020-ccnet, title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data", author = "Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, Edouard", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.494", pages = "4003--4012", abstract = "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
cc_news	--- pretty_name: CC-News annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc-news dataset_info: features: - name: title dtype: string - name: text dtype: string - name: domain dtype: string - name: date dtype: string - name: description dtype: string - name: url dtype: string - name: image_url dtype: string config_name: plain_text splits: - name: train num_bytes: 2016418133 num_examples: 708241 download_size: 845131146 dataset_size: 2016418133 --- # Dataset Card for CC-News ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [CC-News homepage](https://commoncrawl.org/2016/10/news-dataset-available/) - Point of Contact: [Vladimir Blagojevic](mailto:[email protected]) ### Dataset Summary CC-News dataset contains news articles from news sites all over the world. The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has been prepared using [news-please](https://github.com/fhamborg/news-please) - an integrated web crawler and information extractor for news. It contains 708241 English language news articles published between Jan 2017 and December 2019. It represents a small portion of the English language subset of the CC-News dataset. ### Supported Tasks and Leaderboards CC-News has been mostly used for language model training. ### Languages The text in the dataset is in the English language. ## Dataset Structure ### Data Instances Dataset instance contains an article itself and the relevant article fields. An example from the Cc-New train set looks as follows: ``` { 'date': '2017-08-14 00:00:00', 'description': '"The spirit of Green Day has always been about rising above oppression."', 'domain': '1041jackfm.cbslocal.com', 'image_url': 'https://cbs1041jackfm.files.wordpress.com/2017/08/billie-joe-armstrong-theo-wargo-getty-images.jpg?w=946', 'text': 'By Abby Hassler\nGreen Day’s Billie Joe Armstrong has always been outspoken about his political beliefs. Following the tragedy in Charlottesville, Virgina, over the weekend, Armstrong felt the need to speak out against the white supremacists who caused much of the violence.\nRelated: Billie Joe Armstrong Wins #TBT with Childhood Studio Photo\n“My heart feels heavy. I feel like what happened in Charlottesville goes beyond the point of anger,” Armstrong wrote on Facebook. “It makes me sad and desperate. shocked. I f—— hate racism more than anything.”\n“The spirit of Green Day has always been about rising above oppression. and sticking up for what you believe in and singing it at the top of your lungs,” Armstrong continued. “We grew up fearing nuclear holocaust because of the cold war. those days are feeling way too relevant these days. these issues are our ugly past.. and now it’s coming to haunt us. always resist these doomsday politicians. and in the words of our punk forefathers .. Nazi punks f— off.”', 'title': 'Green Day’s Billie Joe Armstrong Rails Against White Nationalists', 'url': 'http://1041jackfm.cbslocal.com/2017/08/14/billie-joe-armstrong-white-nationalists/' } ``` ### Data Fields - `date`: date of publication - `description`: description or a summary of the article - `domain`: source domain of the article (i.e. www.nytimes.com) - `image_url`: URL of the article's image - `text`: the actual article text in raw form - `title`: title of the article - `url`: article URL, the original URL where it was scraped. ### Data Splits CC-News dataset has only the training set, i.e. it has to be loaded with `train` split specified: `cc_news = load_dataset('cc_news', split="train")` ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization CC-News dataset has been proposed, created, and maintained by Sebastian Nagel. The data is publicly available on AWS S3 Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has been prepared using [news-please](https://github.com/fhamborg/news-please) - an integrated web crawler and information extractor for news. It contains 708241 English language news articles published between Jan 2017 and December 2019. Although news-please tags each news article with an appropriate language tag, these tags are somewhat unreliable. To strictly isolate English language articles an additional check has been performed using [Spacy langdetect pipeline](https://spacy.io/universe/project/spacy-langdetect). We selected articles with text fields scores of 80% probability or more of being English. There are no strict guarantees that each article has all the relevant fields. For example, 527595 articles have a valid description field. All articles have what appears to be a valid image URL, but they have not been verified. #### Who are the source language producers? The news websites throughout the World. ### Annotations #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information As one can imagine, data contains contemporary public figures or individuals who appeared in the news. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help language model researchers develop better language models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{Hamborg2017, author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela}, title = {news-please: A Generic News Crawler and Extractor}, year = {2017}, booktitle = {Proceedings of the 15th International Symposium of Information Science}, location = {Berlin}, doi = {10.5281/zenodo.4120316}, pages = {218--223}, month = {March} } ``` ### Contributions Thanks to [@vblagoje](https://github.com/vblagoje) for adding this dataset.
ccaligned_multilingual	--- annotations_creators: - no-annotation language_creators: - found language: - af - ak - am - ar - as - ay - az - be - bg - bm - bn - br - bs - ca - ceb - ckb - cs - cy - de - dv - el - eo - es - fa - ff - fi - fo - fr - fy - ga - gl - gn - gu - he - hi - hr - hu - id - ig - is - it - iu - ja - ka - kac - kg - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - nso - ny - om - or - pa - pl - ps - pt - rm - ro - ru - rw - sc - sd - se - shn - si - sk - sl - sn - so - sq - sr - ss - st - su - sv - sw - syc - szl - ta - te - tg - th - ti - tl - tn - tr - ts - tt - ug - uk - ur - uz - ve - vi - war - wo - xh - yi - yo - zgh - zh - zu - zza license: - unknown multilinguality: - translation size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M - 10M<n<100M source_datasets: - original task_categories: - other paperswithcode_id: ccaligned pretty_name: CCAligned dataset_info: - config_name: documents-zz_TR features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - zz_TR splits: - name: train num_bytes: 641412 num_examples: 41 download_size: 125488 dataset_size: 641412 - config_name: sentences-zz_TR features: - name: translation dtype: translation: languages: - en_XX - zz_TR - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 4056 num_examples: 34 download_size: 1428 dataset_size: 4056 - config_name: documents-tz_MA features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - tz_MA splits: - name: train num_bytes: 51782 num_examples: 4 download_size: 11996 dataset_size: 51782 - config_name: sentences-tz_MA features: - name: translation dtype: translation: languages: - en_XX - tz_MA - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 6256 num_examples: 33 download_size: 2420 dataset_size: 6256 - config_name: documents-ak_GH features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - ak_GH splits: - name: train num_bytes: 10738312 num_examples: 249 download_size: 399236 dataset_size: 10738312 - config_name: sentences-ak_GH features: - name: translation dtype: translation: languages: - en_XX - ak_GH - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 50110 num_examples: 478 download_size: 17636 dataset_size: 50110 --- # Dataset Card for ccaligned_multilingual ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://www.statmt.org/cc-aligned/ - Repository: [Needs More Information] - Paper: https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). This corpus was created from 68 Commoncrawl Snapshots. To load a language which isn't part of the config, all you need to do is specify the language code. You can find the valid languages in http://www.statmt.org/cc-aligned/ E.g. ``` dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="documents") ``` or ``` dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="sentences") ``` ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in (137) multiple languages aligned with english. ## Dataset Structure ### Data Instances An instance of `documents` type for language `ak_GH`: ``` {'Domain': 'islamhouse.com', 'Source_URL': 'https://islamhouse.com/en/audios/373088/', 'Target_URL': 'https://islamhouse.com/ak/audios/373088/', 'translation': {'ak_GH': "Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah - Arab kasa\|Islamhouse.com\|Follow us:\|facebook\|twitter\|taepe\|Titles All\|Fie wibesite\|kasa nyina\|Buukuu edi adanse ma prente\|Nhyehyɛmu\|Nyim/sua Islam\|Curriculums\|Nyina ndeɛma\|Nyina ndeɛma (295)\|Buukuu/ nwoma (2)\|sini / muuvi (31)\|ɔdio (262)\|Aɛn websideNew!\|Kɔ wura kramosom mu seisei\|Ebio\|figa/kaasɛ\|Farebae\|AKAkan\|Kratafa titriw\|kasa interface( anyimu) : Akan\|Kasa ma no mu-nsɛm : Arab kasa\|ɔdio\|Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah\|play\|pause\|stop\|mute\|unmute\|max volume\|Kasakyerɛ ni :\|Farebae:\|17 / 11 / 1432 , 15/10/2011\|Nhyehyɛmu:\|Jurisprudence/ Esum Nimdea\|Som\|Hajj na Umrah\|Jurisprudence/ Esum Nimdea\|Som\|Hajj na Umrah\|Mmira ma Hajj na Umrah\|nkyerɛmu\|kasamu /sɛntɛns ma te ase na Umrah wɔ ... mu no hann ma no Quran na Sunnah na te ase ma no nana na no kasamu /sɛntɛns ma bi ma no emerging yi adu obusuani\|Akenkane we ye di ko kasa bi su (36)\|Afar - Qafár afa\|Akan\|Amhari ne - አማርኛ\|Arab kasa - عربي\|Assamese - অসমীয়া\|Bengali - বাংলা\|Maldive - ދިވެހި\|Greek - Ελληνικά\|English ( brofo kasa) - English\|Persian - فارسی\|Fula - pulla\|French - Français\|Hausa - Hausa\|Kurdish - كوردی سۆرانی\|Uganda ne - Oluganda\|Mandinka - Mandinko\|Malayalam - മലയാളം\|Nepali - नेपाली\|Portuguese - Português\|Russian - Русский\|Sango - Sango\|Sinhalese - සිංහල\|Somali - Soomaali\|Albania ne - Shqip\|Swahili - Kiswahili\|Telugu - తెలుగు ప్రజలు\|Tajik - Тоҷикӣ\|Thai - ไทย\|Tagalog - Tagalog\|Turkish - Türkçe\|Uyghur - ئۇيغۇرچە\|Urdu - اردو\|Uzbeck ne - Ўзбек тили\|Vietnamese - Việt Nam\|Wolof - Wolof\|Chine ne - 中文\|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin\|Soma kɔ ne kɔ hom adamfo\|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin\|Nsɔwso fael (1)\|1\|الموجز في فقه العمرة\|MP3 14.7 MB\|Enoumah ebatahu\|Rituals/Esom ajomadie ewu Hajji mmire .. 1434 AH [01] no fapemso Enum\|Fiidbak/ Ye hiya wu jun kyiri\|Lenke de yɛe\|kɔntakt yɛn\|Aɛn webside\|Qura'an Kro kronkrom\|Balagh\|wɔ mfinimfin Dowload faele\|Yɛ atuu bra Islam mu afei\|Tsin de yɛe ewu\|Anaa bomu/combine hɛn melin liste\|© Islamhouse Website/ Islam dan webi site\|×\|×\|Yi mu kasa\|", 'en_XX': 'SUMMARY in the jurisprudence of Umrah - Arabic - Abdul Aziz Bin Marzooq Al-Turaifi\|Islamhouse.com\|Follow us:\|facebook\|twitter\|QuranEnc.com\|HadeethEnc.com\|Type\|Titles All\|Home Page\|All Languages\|Categories\|Know about Islam\|All items\|All items (4057)\|Books (701)\|Articles (548)\|Fatawa (370)\|Videos (1853)\|Audios (416)\|Posters (98)\|Greeting cards (22)\|Favorites (25)\|Applications (21)\|Desktop Applications (3)\|To convert to Islam now !\|More\|Figures\|Sources\|Curriculums\|Our Services\|QuranEnc.com\|HadeethEnc.com\|ENEnglish\|Main Page\|Interface Language : English\|Language of the content : Arabic\|Audios\|تعريب عنوان المادة\|SUMMARY in the jurisprudence of Umrah\|play\|pause\|stop\|mute\|unmute\|max volume\|Lecturer : Abdul Aziz Bin Marzooq Al-Turaifi\|Sources:\|AlRaya Islamic Recoding in Riyadh\|17 / 11 / 1432 , 15/10/2011\|Categories:\|Islamic Fiqh\|Fiqh of Worship\|Hajj and Umrah\|Islamic Fiqh\|Fiqh of Worship\|Hajj and Umrah\|Pilgrimage and Umrah\|Description\|SUMMARY in jurisprudence of Umrah: A statement of jurisprudence and Umrah in the light of the Quran and Sunnah and understanding of the Ancestors and the statement of some of the emerging issues related to them.\|This page translated into (36)\|Afar - Qafár afa\|Akane - Akan\|Amharic - አማርኛ\|Arabic - عربي\|Assamese - অসমীয়া\|Bengali - বাংলা\|Maldivi - ދިވެހި\|Greek - Ελληνικά\|English\|Persian - فارسی\|Fula - pulla\|French - Français\|Hausa - Hausa\|kurdish - كوردی سۆرانی\|Ugandan - Oluganda\|Mandinka - Mandinko\|Malayalam - മലയാളം\|Nepali - नेपाली\|Portuguese - Português\|Russian - Русский\|Sango - Yanga ti Sango\|Sinhalese - සිංහල\|Somali - Soomaali\|Albanian - Shqip\|Swahili - Kiswahili\|Telugu - తెలుగు\|Tajik - Тоҷикӣ\|Thai - ไทย\|Tagalog - Tagalog\|Turkish - Türkçe\|Uyghur - ئۇيغۇرچە\|Urdu - اردو\|Uzbek - Ўзбек тили\|Vietnamese - Việt Nam\|Wolof - Wolof\|Chinese - 中文\|Send a comment to Webmaster\|Send to a friend?\|Send a comment to Webmaster\|Attachments (1)\|1\|الموجز في فقه العمرة\|MP3 14.7 MB\|The relevant Material\|The rituals of the pilgrimage season .. 1434 AH [ 01] the fifth pillar\|The Quality of the Accepted Hajj (Piligrimage) and Its Limitations\|Easy Path to the Rules of the Rites of Hajj\|A Call to the Pilgrims of the Scared House of Allah\|More\|feedback\|Important links\|Contact us\|Privacy policy\|Islam Q&A\|Learning Arabic Language\|About Us\|Convert To Islam\|Noble Quran encyclopedia\|IslamHouse.com Reader\|Encyclopedia of Translated Prophetic Hadiths\|Our Services\|The Quran\|Balagh\|Center for downloading files\|To embrace Islam now...\|Follow us through\|Or join our mailing list.\|© Islamhouse Website\|×\|×\|Choose language\|'}} ``` An instance of `sentences` type for language `ak_GH`: ``` {'LASER_similarity': 1.4549942016601562, 'translation': {'ak_GH': 'Salah (nyamefere) ye Mmerebeia', 'en_XX': 'What he dislikes when fasting (10)'}} ``` ### Data Fields For `documents` type: - `Domain`: a `string` feature containing the domain. - `Source_URL`: a `string` feature containing the source URL. - `Target_URL`: a `string` feature containing the target URL. - `translation`: a `dictionary` feature with two keys : - `en_XX`: a `string` feature containing the content in English. - <language_code>: a `string` feature containing the content in the `language_code` specified. For `sentences` type: - `LASER_similarity`: a `float32` feature representing the LASER similarity score. - `translation`: a `dictionary` feature with two keys : - `en_XX`: a `string` feature containing the content in English. - <language_code>: a `string` feature containing the content in the `language_code` specified. ### Data Splits Split sizes of some small configurations: \| name \|train\| \|----------\|----:\| \|documents-zz_TR\|41\| \|sentences-zz_TR\|34\| \|documents-tz_MA\|4\| \|sentences-tz_MA\|33\| \|documents-ak_GH\|249\| \|sentences-ak_GH\|478\| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{elkishky_ccaligned_2020, author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}, month = {November}, title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs}, year = {2020} address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.480", doi = "10.18653/v1/2020.emnlp-main.480", pages = "5960--5969" } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.
cdsc	--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: polish-cdscorpus pretty_name: Polish CDSCorpus tags: - sentences entailment and relatedness dataset_info: - config_name: cdsc-e features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: entailment_judgment dtype: class_label: names: '0': NEUTRAL '1': CONTRADICTION '2': ENTAILMENT splits: - name: train num_bytes: 1381902 num_examples: 8000 - name: test num_bytes: 179400 num_examples: 1000 - name: validation num_bytes: 174662 num_examples: 1000 download_size: 376079 dataset_size: 1735964 - config_name: cdsc-r features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: relatedness_score dtype: float32 splits: - name: train num_bytes: 1349902 num_examples: 8000 - name: test num_bytes: 175400 num_examples: 1000 - name: validation num_bytes: 170662 num_examples: 1000 download_size: 381525 dataset_size: 1695964 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://zil.ipipan.waw.pl/Scwad/CDSCorpus - Repository: - Paper: @inproceedings{wroblewska2017polish, title={Polish evaluation dataset for compositional distributional semantics models}, author={Wr{\'o}blewska, Alina and Krasnowska-Kiera{\'s}, Katarzyna}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={784--792}, year={2017} } - Leaderboard: https://klejbenchmark.com/leaderboard/ - Point of Contact: [email protected] ### Dataset Summary Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - pair_ID: id of sentences pairs - sentence_A: first sentence - sentence_B: second sentence for cdsc-e domain: - entailment_judgment: either 'NEUTRAL', 'CONTRADICTION' or 'ENTAILMENT' for cdsc-r domain: - relatedness_score: float representing a reletedness ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-NC-SA 4.0 ### Citation Information [More Information Needed] ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.
cdt	--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - bsd-3-clause multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: cdt dataset_info: features: - name: sentence dtype: string - name: target dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1104322 num_examples: 10041 - name: test num_bytes: 109681 num_examples: 1000 download_size: 375476 dataset_size: 1214003 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://2019.poleval.pl/index.php/tasks/ - Repository: https://github.com/ptaszynski/cyberbullying-Polish - Paper: - Leaderboard: https://klejbenchmark.com/leaderboard/ - Point of Contact: ### Dataset Summary The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sentence: an anonymized tweet in polish - target: 1 if tweet is described as bullying, 0 otherwise. The test set doesn't have labels so -1 is used instead. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information BSD 3-Clause ### Citation Information [More Information Needed] ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.
cedr	--- annotations_creators: - crowdsourced language_creators: - found language: - ru license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - multi-label-classification pretty_name: The Corpus for Emotions Detecting in Russian-language text sentences (CEDR) tags: - emotion-classification dataset_info: - config_name: main features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string splits: - name: train num_bytes: 1418355 num_examples: 7528 - name: test num_bytes: 350275 num_examples: 1882 download_size: 693026 dataset_size: 1768630 - config_name: enriched features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string - name: sentences list: list: - name: forma dtype: string - name: lemma dtype: string splits: - name: train num_bytes: 4792366 num_examples: 7528 - name: test num_bytes: 1182343 num_examples: 1882 download_size: 1822522 dataset_size: 5974709 --- # Dataset Card for [cedr] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [GitHub](https://github.com/sag111/CEDR) - Repository: [GitHub](https://github.com/sag111/CEDR) - Paper: [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S1877050921013247) - Leaderboard: - Point of Contact: [@sag111](mailto:[email protected]) ### Dataset Summary The Corpus for Emotions Detecting in Russian-language text sentences of different social sources (CEDR) contains 9410 comments labeled for 5 emotion categories (joy, sadness, surprise, fear, and anger). Here are 2 dataset configurations: - "main" - contains "text", "labels", and "source" features; - "enriched" - includes all "main" features and "sentences". Dataset with predefined train/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a text sentence in Russian from several sources with one or more emotion annotations (or no emotion at all). An example for an instance from the dataset is shown below: ``` { 'text': 'Забавно как люди в возрасте удивляются входящим звонкам на мобильник)', 'labels': [0], 'source': 'twitter', 'sentences': [ [ {'forma': 'Забавно', 'lemma': 'Забавно'}, {'forma': 'как', 'lemma': 'как'}, {'forma': 'люди', 'lemma': 'человек'}, {'forma': 'в', 'lemma': 'в'}, {'forma': 'возрасте', 'lemma': 'возраст'}, {'forma': 'удивляются', 'lemma': 'удивляться'}, {'forma': 'входящим', 'lemma': 'входить'}, {'forma': 'звонкам', 'lemma': 'звонок'}, {'forma': 'на', 'lemma': 'на'}, {'forma': 'мобильник', 'lemma': 'мобильник'}, {'forma': ')', 'lemma': ')'} ] ] } ``` Emotion label codes: {0: "joy", 1: "sadness", 2: "surprise", 3: "fear", 4: "anger"} ### Data Fields The main configuration includes: - text: the text of the sentence; - labels: the emotion annotations; - source: the tag name of the corresponding source In addition to the above, the raw data includes: - sentences: text tokenized and lemmatized with [udpipe](https://ufal.mff.cuni.cz/udpipe) - 'forma': the original word form; - 'lemma': the lemma of this word ### Data Splits The dataset includes a set of train/test splits. with 7528, and 1882 examples respectively. ## Dataset Creation ### Curation Rationale The formed dataset of examples consists of sentences in Russian from several sources (blogs, microblogs, news), which allows creating methods to analyse various types of texts. The created methodology for building the dataset based on applying a crowdsourcing service can be used to expand the number of examples to improve the accuracy of supervised classifiers. ### Source Data #### Initial Data Collection and Normalization Data was collected from several sources: posts of the Live Journal social network, texts of the online news agency Lenta.ru, and Twitter microblog posts. Only those sentences were selected that contained marker words from the dictionary of [the emotive vocabulary of the Russian language](http://lexrus.ru/default.aspx?p=2876). The authors manually formed a list of marker words for each emotion by choosing words from different categories of the dictionary. In total, 3069 sentences were selected from LiveJournal posts, 2851 sentences from Lenta.Ru, and 3490 sentencesfrom Twitter. After selection, sentences were offered to annotators for labeling. #### Who are the source language producers? Russian-speaking LiveJournal and Tweeter users, and authors of news articles on the site lenta.ru. ### Annotations #### Annotation process Annotating sentences with labels of their emotions was performed with the help of [a crowdsourcing platform](https://yandex.ru/support/toloka/index.html?lang=en). The annotators’ task was: “What emotions did the author express in the sentence?”. The annotators were allowed to put an arbitrary number of the following emotion labels: "joy", "sadness", "anger", "fear", and "surprise". If the accuracy of an annotator on the control sentences (including the trial run) became less than 70%, or if the accuracy was less than 66% over the last six control samples, the annotator was dismissed. Sentences were split into tasks and assigned to annotators so that each sentence was annotated at least three times. A label of a specific emotion was assigned to a sentence if put by more than half of the annotators. #### Who are the annotators? Only those of the 30% of the best-performing active users (by the platform’s internal rating) who spoke Russian and were over 18 years old were allowed into the annotation process. Moreover, before a platform user could be employed as an annotator, they underwent a training task, after which they were to mark 25 trial samples with more than 80% agreement compared to the annotation that the authors had performed themselves. ### Personal and Sensitive Information The text of the sentences may contain profanity. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Researchers at AI technology lab at NRC "Kurchatov Institute". See the author [list](https://www.sciencedirect.com/science/article/pii/S1877050921013247). ### Licensing Information The GitHub repository which houses this dataset has an Apache License 2.0. ### Citation Information If you have found our results helpful in your work, feel free to cite our publication. This is an updated version of the dataset, the collection and preparation of which is described here: ``` @article{sboev2021data, title={Data-Driven Model for Emotion Detection in Russian Texts}, author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman}, journal={Procedia Computer Science}, volume={190}, pages={637--642}, year={2021}, publisher={Elsevier} } ``` ### Contributions Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.
cfq	--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Compositional Freebase Questions size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering - other task_ids: - open-domain-qa - closed-domain-qa paperswithcode_id: cfq tags: - compositionality dataset_info: - config_name: mcd1 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 37408806 num_examples: 95743 - name: test num_bytes: 5446503 num_examples: 11968 download_size: 267599061 dataset_size: 42855309 - config_name: mcd2 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39424657 num_examples: 95743 - name: test num_bytes: 5314019 num_examples: 11968 download_size: 267599061 dataset_size: 44738676 - config_name: mcd3 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 38316345 num_examples: 95743 - name: test num_bytes: 5244503 num_examples: 11968 download_size: 267599061 dataset_size: 43560848 - config_name: question_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39989433 num_examples: 98999 - name: test num_bytes: 5781561 num_examples: 10340 download_size: 267599061 dataset_size: 45770994 - config_name: question_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41217350 num_examples: 95654 - name: test num_bytes: 5179936 num_examples: 11909 download_size: 267599061 dataset_size: 46397286 - config_name: query_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40270175 num_examples: 100654 - name: test num_bytes: 5634924 num_examples: 9512 download_size: 267599061 dataset_size: 45905099 - config_name: query_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40811284 num_examples: 94600 - name: test num_bytes: 5268358 num_examples: 12589 download_size: 267599061 dataset_size: 46079642 - config_name: random_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41279218 num_examples: 95744 - name: test num_bytes: 5164923 num_examples: 11967 download_size: 267599061 dataset_size: 46444141 --- # Dataset Card for "cfq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/google-research/google-research/tree/master/cfq](https://github.com/google-research/google-research/tree/master/cfq) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: https://arxiv.org/abs/1912.09713 - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 2.14 GB - Size of the generated dataset: 362.07 MB - Total amount of disk used: 2.50 GB ### Dataset Summary The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can also be used for semantic parsing. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English (`en`). ## Dataset Structure ### Data Instances #### mcd1 - Size of downloaded dataset files: 267.60 MB - Size of the generated dataset: 42.90 MB - Total amount of disk used: 310.49 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count() WHERE {\n?x0 a ns:people.person .\n?x0 ns:influence.influence_node.influenced M1 .\n?x0 ns:influence.influence_node.influenced M2 .\n?x0 ns:people.person.spouse_s/ns:people.marriage.spouse\|ns:fictional_universe.fictional_character.married_to/ns:fictional_universe.marriage_of_fictional_characters.spouses ?x1 .\n?x1 a ns:film.cinematographer .\nFILTER ( ?x0 != ?x1 )\n}', 'question': 'Did a person marry a cinematographer , influence M1 , and influence M2' } ``` #### mcd2 - Size of downloaded dataset files:* 267.60 MB - Size of the generated dataset: 44.77 MB - Total amount of disk used: 312.38 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count() WHERE {\n?x0 ns:people.person.parents\|ns:fictional_universe.fictional_character.parents\|ns:organization.organization.parent/ns:organization.organization_relationship.parent ?x1 .\n?x1 a ns:people.person .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M4 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M4\n}', 'question': "Did M1 and M5 employ M2 , M3 , and M4 and employ a person 's child" } ``` #### mcd3 - Size of downloaded dataset files:* 267.60 MB - Size of the generated dataset: 43.60 MB - Total amount of disk used: 311.20 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_complexity_split - Size of downloaded dataset files: 267.60 MB - Size of the generated dataset: 45.95 MB - Total amount of disk used: 313.55 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_pattern_split - Size of downloaded dataset files: 267.60 MB - Size of the generated dataset: 46.12 MB - Total amount of disk used: 313.72 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` ### Data Fields The data fields are the same among all splits and configurations: - `question`: a `string` feature. - `query`: a `string` feature. ### Data Splits \| name \| train \| test \| \|---------------------------\|-------:\|------:\| \| mcd1 \| 95743 \| 11968 \| \| mcd2 \| 95743 \| 11968 \| \| mcd3 \| 95743 \| 11968 \| \| query_complexity_split \| 100654 \| 9512 \| \| query_pattern_split \| 94600 \| 12589 \| \| question_complexity_split \| 98999 \| 10340 \| \| question_pattern_split \| 95654 \| 11909 \| \| random_split \| 95744 \| 11967 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Keysers2020, title={Measuring Compositional Generalization: A Comprehensive Method on Realistic Data}, author={Daniel Keysers and Nathanael Sch"{a}rli and Nathan Scales and Hylke Buisman and Daniel Furrer and Sergii Kashubin and Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and Olivier Bousquet}, booktitle={ICLR}, year={2020}, url={https://arxiv.org/abs/1912.09713.pdf}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@brainshawn](https://github.com/brainshawn) for adding this dataset.
chr_en	--- annotations_creators: - expert-generated - found - no-annotation language_creators: - found language: - chr - en license: - other multilinguality: - monolingual - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - fill-mask - text-generation - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: chren configs: - monolingual - monolingual_raw - parallel - parallel_raw dataset_info: - config_name: monolingual_raw features: - name: text_sentence dtype: string - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 1210828 num_examples: 5210 download_size: 28899321 dataset_size: 1210828 - config_name: parallel_raw features: - name: line_number dtype: string - name: sentence_pair dtype: translation: languages: - en - chr - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 5012923 num_examples: 14151 download_size: 28899321 dataset_size: 5012923 - config_name: monolingual features: - name: sentence dtype: string splits: - name: chr num_bytes: 882848 num_examples: 5210 - name: en5000 num_bytes: 615295 num_examples: 5000 - name: en10000 num_bytes: 1211645 num_examples: 10000 - name: en20000 num_bytes: 2432378 num_examples: 20000 - name: en50000 num_bytes: 6065780 num_examples: 49999 - name: en100000 num_bytes: 12130564 num_examples: 100000 download_size: 28899321 dataset_size: 23338510 - config_name: parallel features: - name: sentence_pair dtype: translation: languages: - en - chr splits: - name: train num_bytes: 3089658 num_examples: 11639 - name: dev num_bytes: 260409 num_examples: 1000 - name: out_dev num_bytes: 78134 num_examples: 256 - name: test num_bytes: 264603 num_examples: 1000 - name: out_test num_bytes: 80967 num_examples: 256 download_size: 28899321 dataset_size: 3773771 --- # Dataset Card for ChrEn ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn) - Paper: [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791) - Point of Contact: [[email protected]]([email protected]) ### Dataset Summary ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. ### Supported Tasks and Leaderboards The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`). ### Languages The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC). ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill. ### Licensing Information The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions). ### Citation Information ``` @inproceedings{zhang2020chren, title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization}, author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit}, booktitle={EMNLP2020}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
cifar10	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-10 pretty_name: Cifar10 dataset_info: features: - name: img dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck config_name: plain_text splits: - name: train num_bytes: 113854600 num_examples: 50000 - name: test num_bytes: 22772838 num_examples: 10000 download_size: 170498071 dataset_size: 136627438 --- # Dataset Card for CIFAR-10 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.cs.toronto.edu/~kriz/cifar.html - Repository: - Paper: Learning Multiple Layers of Features from Tiny Images by Alex Krizhevsky - Leaderboard: - Point of Contact: ### Dataset Summary The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-10). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x201FA6EE748>, 'label': 0 } ``` ### Data Fields - img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - label: 0-9 with the following correspondence 0 airplane 1 automobile 2 bird 3 cat 4 deer 5 dog 6 frog 7 horse 8 ship 9 truck ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.
cifar100	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-100 pretty_name: Cifar100 dataset_info: features: - name: img dtype: image - name: fine_label dtype: class_label: names: '0': apple '1': aquarium_fish '2': baby '3': bear '4': beaver '5': bed '6': bee '7': beetle '8': bicycle '9': bottle '10': bowl '11': boy '12': bridge '13': bus '14': butterfly '15': camel '16': can '17': castle '18': caterpillar '19': cattle '20': chair '21': chimpanzee '22': clock '23': cloud '24': cockroach '25': couch '26': cra '27': crocodile '28': cup '29': dinosaur '30': dolphin '31': elephant '32': flatfish '33': forest '34': fox '35': girl '36': hamster '37': house '38': kangaroo '39': keyboard '40': lamp '41': lawn_mower '42': leopard '43': lion '44': lizard '45': lobster '46': man '47': maple_tree '48': motorcycle '49': mountain '50': mouse '51': mushroom '52': oak_tree '53': orange '54': orchid '55': otter '56': palm_tree '57': pear '58': pickup_truck '59': pine_tree '60': plain '61': plate '62': poppy '63': porcupine '64': possum '65': rabbit '66': raccoon '67': ray '68': road '69': rocket '70': rose '71': sea '72': seal '73': shark '74': shrew '75': skunk '76': skyscraper '77': snail '78': snake '79': spider '80': squirrel '81': streetcar '82': sunflower '83': sweet_pepper '84': table '85': tank '86': telephone '87': television '88': tiger '89': tractor '90': train '91': trout '92': tulip '93': turtle '94': wardrobe '95': whale '96': willow_tree '97': wolf '98': woman '99': worm - name: coarse_label dtype: class_label: names: '0': aquatic_mammals '1': fish '2': flowers '3': food_containers '4': fruit_and_vegetables '5': household_electrical_devices '6': household_furniture '7': insects '8': large_carnivores '9': large_man-made_outdoor_things '10': large_natural_outdoor_scenes '11': large_omnivores_and_herbivores '12': medium_mammals '13': non-insect_invertebrates '14': people '15': reptiles '16': small_mammals '17': trees '18': vehicles_1 '19': vehicles_2 config_name: cifar100 splits: - name: train num_bytes: 112751396 num_examples: 50000 - name: test num_bytes: 22605519 num_examples: 10000 download_size: 169001437 dataset_size: 135356915 --- # Dataset Card for CIFAR-100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html) - Repository: - Paper: [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19, 'coarse_label': 11 } ``` ### Data Fields - `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `fine_label`: an `int` classification label with the following mapping: `0`: apple `1`: aquarium_fish `2`: baby `3`: bear `4`: beaver `5`: bed `6`: bee `7`: beetle `8`: bicycle `9`: bottle `10`: bowl `11`: boy `12`: bridge `13`: bus `14`: butterfly `15`: camel `16`: can `17`: castle `18`: caterpillar `19`: cattle `20`: chair `21`: chimpanzee `22`: clock `23`: cloud `24`: cockroach `25`: couch `26`: cra `27`: crocodile `28`: cup `29`: dinosaur `30`: dolphin `31`: elephant `32`: flatfish `33`: forest `34`: fox `35`: girl `36`: hamster `37`: house `38`: kangaroo `39`: keyboard `40`: lamp `41`: lawn_mower `42`: leopard `43`: lion `44`: lizard `45`: lobster `46`: man `47`: maple_tree `48`: motorcycle `49`: mountain `50`: mouse `51`: mushroom `52`: oak_tree `53`: orange `54`: orchid `55`: otter `56`: palm_tree `57`: pear `58`: pickup_truck `59`: pine_tree `60`: plain `61`: plate `62`: poppy `63`: porcupine `64`: possum `65`: rabbit `66`: raccoon `67`: ray `68`: road `69`: rocket `70`: rose `71`: sea `72`: seal `73`: shark `74`: shrew `75`: skunk `76`: skyscraper `77`: snail `78`: snake `79`: spider `80`: squirrel `81`: streetcar `82`: sunflower `83`: sweet_pepper `84`: table `85`: tank `86`: telephone `87`: television `88`: tiger `89`: tractor `90`: train `91`: trout `92`: tulip `93`: turtle `94`: wardrobe `95`: whale `96`: willow_tree `97`: wolf `98`: woman `99`: worm - `coarse_label`: an `int` coarse classification label with following mapping: `0`: aquatic_mammals `1`: fish `2`: flowers `3`: food_containers `4`: fruit_and_vegetables `5`: household_electrical_devices `6`: household_furniture `7`: insects `8`: large_carnivores `9`: large_man-made_outdoor_things `10`: large_natural_outdoor_scenes `11`: large_omnivores_and_herbivores `12`: medium_mammals `13`: non-insect_invertebrates `14`: people `15`: reptiles `16`: small_mammals `17`: trees `18`: vehicles_1 `19`: vehicles_2 ### Data Splits \| name \|train\|test\| \|----------\|----:\|---------:\| \|cifar100\|50000\| 10000\| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.
circa	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: circa pretty_name: CIRCA tags: - question-answer-pair-classification dataset_info: features: - name: context dtype: string - name: question-X dtype: string - name: canquestion-X dtype: string - name: answer-Y dtype: string - name: judgements dtype: string - name: goldstandard1 dtype: class_label: names: '0': 'Yes' '1': 'No' '2': In the middle, neither yes nor no '3': Probably yes / sometimes yes '4': Probably no '5': Yes, subject to some conditions '6': Other '7': I am not sure how X will interpret Y’s answer - name: goldstandard2 dtype: class_label: names: '0': 'Yes' '1': 'No' '2': In the middle, neither yes nor no '3': Yes, subject to some conditions '4': Other splits: - name: train num_bytes: 8149489 num_examples: 34268 download_size: 7766077 dataset_size: 8149489 --- # Dataset Card for CIRCA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [CIRCA homepage](https://github.com/google-research-datasets/circa) - Repository: [CIRCA repository](https://github.com/google-research-datasets/circa) - Paper: ["I’d rather just go to bed”: Understanding Indirect Answers](https://arxiv.org/abs/2010.03450) - Point of Contact: [Circa team, Google]([email protected]) ### Dataset Summary The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions. The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend). The following are the situational contexts for the dialogs in the data. ``` 1. X wants to know about Y’s food preferences 2. X wants to know what activities Y likes to do during weekends. 3. X wants to know what sorts of books Y likes to read. 4. Y has just moved into a neighbourhood and meets his/her new neighbour X. 5. X and Y are colleagues who are leaving work on a Friday at the same time. 6. X wants to know about Y's music preferences. 7. Y has just travelled from a different city to meet X. 8. X and Y are childhood neighbours who unexpectedly run into each other at a cafe. 9. Y has just told X that he/she is thinking of buying a flat in New York. 10. Y has just told X that he/she is considering switching his/her job. ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances The columns indicate: ``` 1. id : unique id for the question-answer pair 2. context : the social situation for the dialogue. One of 10 situations (see next section). Each situation is a dialogue between a person who poses the question (X) and the person who answers (Y). 3. question-X : the question posed by X 4. canquestion-X : a (automatically) rewritten version of question into declarative form Eg. Do you like Italian? --> I like Italian. See the paper for details. 5. answer-Y : the answer given by Y to X 6. judgements : the interpretations for the QA pair from 5 annotators. The value is a list of 5 strings, separated by the token ‘#’ 7. goldstandard1 : a gold standard majority judgement from the annotators. The value is the most common interpretation and picked by at least 3 (out of 5 annotators). When a majority judgement was not reached by the above criteria, the value is ‘NA’ 8. goldstandard2 : Here the labels ‘Probably yes / sometimes yes’, ‘Probably no', and 'I am not sure how X will interpret Y’s answer' are mapped respectively to ‘Yes’, ‘No’, and 'In the middle, neither yes nor no’ before computing the majority. Still the label must be given at least 3 times to become the majority choice. This method represents a less strict way of analyzing the interpretations. ``` ### Data Fields ``` id : 1 context : X wants to know about Y's food preferences. question-X : Are you vegan? canquestion-X : I am vegan. answer-Y : I love burgers too much. judgements : no#no#no#no#no goldstandard1 : no (label(s) used for the classification task) goldstandard2 : no (label(s) used for the classification task) ``` ### Data Splits There are no explicit train/val/test splits in this dataset. ## Dataset Creation ### Curation Rationale They revisited a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret ‘I’m starving.’ in response to ‘Hungry?’, even without direct cue words such as ‘yes’ and ‘no’. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today’s systems are only as sensitive to these pragmatic moves as their language model allows. They create and release the first large-scale English language corpus ‘Circa’ with 34,268 (polar question, indirect answer) pairs to enable progress on this task. ### Source Data #### Initial Data Collection and Normalization The QA pairs and judgements were collected using crowd annotations in three phases. They recruited English native speakers. The full descriptions of the data collection and quality control are present in [EMNLP 2020 paper](https://arxiv.org/pdf/2010.03450.pdf). Below is a brief overview only. Phase 1: In the first phase, they collected questions only. They designed 10 imaginary social situations which give the annotator a context for the conversation. Examples are: ``` ‘asking a friend for food preferences’ ‘meeting your childhood neighbour’ ‘your friend wants to buy a flat in New York’ ``` Annotators were asked to suggest questions which could be asked in each situation, such that each question only requires a ‘yes’ or ‘no’ answer. 100 annotators produced 5 questions each for the 10 situations, resulting in 5000 questions. Phase 2: Here they focused on eliciting answers to the questions. They sampled 3500 questions from our previous set. For each question, They collected possible answers from 10 different annotators. The annotators were instructed to provide a natural phrase or a sentence as the answer and to avoid the use of explicit ‘yes’ and ‘no’ words. Phase 3: Finally the QA pairs (34,268) were given to a third set of annotators who were asked how the question seeker would likely interpret a particular answer. These annotators had the following options to choose from: ``` * 'Yes' * 'Probably yes' / 'sometimes yes' * 'Yes, subject to some conditions' * 'No' * 'Probably no' * 'In the middle, neither yes nor no' * 'I am not sure how X will interpret Y's answer' ``` #### Who are the source language producers? The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset is the work of Annie Louis, Dan Roth, and Filip Radlinski from Google LLC. ### Licensing Information This dataset was made available under the Creative Commons Attribution 4.0 License. A full copy of the license can be found at https://creativecommons.org/licenses/by-sa/4.0/e and link to the license webpage if available. ### Citation Information ``` @InProceedings{louis_emnlp2020, author = "Annie Louis and Dan Roth and Filip Radlinski", title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", year = "2020", } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.
civil_comments	--- language: - en paperswithcode_id: null pretty_name: CivilComments dataset_info: features: - name: text dtype: string - name: toxicity dtype: float32 - name: severe_toxicity dtype: float32 - name: obscene dtype: float32 - name: threat dtype: float32 - name: insult dtype: float32 - name: identity_attack dtype: float32 - name: sexual_explicit dtype: float32 splits: - name: test num_bytes: 32073013 num_examples: 97320 - name: train num_bytes: 596835730 num_examples: 1804874 - name: validation num_bytes: 32326369 num_examples: 97320 download_size: 414947977 dataset_size: 661235112 --- # Dataset Card for "civil_comments" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 414.95 MB - Size of the generated dataset: 661.23 MB - Total amount of disk used: 1.08 GB ### Dataset Summary The comments in this dataset come from an archive of the Civil Comments platform, a commenting plugin for independent news sites. These public comments were created from 2015 - 2017 and appeared on approximately 50 English-language news sites across the world. When Civil Comments shut down in 2017, they chose to make the public comments available in a lasting open archive to enable future research. The original data, published on figshare, includes the public comment text, some associated metadata such as article IDs, timestamps and commenter-generated "civility" labels, but does not include user ids. Jigsaw extended this dataset by adding additional labels for toxicity and identity mentions. This data set is an exact replica of the data released for the Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This dataset is released under CC0, as is the underlying comment text. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 414.95 MB - Size of the generated dataset: 661.23 MB - Total amount of disk used: 1.08 GB An example of 'validation' looks as follows. ``` { "identity_attack": 0.0, "insult": 0.0, "obscene": 0.0, "severe_toxicity": 0.0, "sexual_explicit": 0.0, "text": "The public test.", "threat": 0.0, "toxicity": 0.0 } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `toxicity`: a `float32` feature. - `severe_toxicity`: a `float32` feature. - `obscene`: a `float32` feature. - `threat`: a `float32` feature. - `insult`: a `float32` feature. - `identity_attack`: a `float32` feature. - `sexual_explicit`: a `float32` feature. ### Data Splits \| name \| train \|validation\|test \| \|-------\|------:\|---------:\|----:\| \|default\|1804874\| 97320\|97320\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{DBLP:journals/corr/abs-1903-04561, author = {Daniel Borkan and Lucas Dixon and Jeffrey Sorensen and Nithum Thain and Lucy Vasserman}, title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification}, journal = {CoRR}, volume = {abs/1903.04561}, year = {2019}, url = {http://arxiv.org/abs/1903.04561}, archivePrefix = {arXiv}, eprint = {1903.04561}, timestamp = {Sun, 31 Mar 2019 19:01:24 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
clickbait_news_bg	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - bg license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking pretty_name: Clickbait/Fake News in Bulgarian dataset_info: features: - name: fake_news_score dtype: class_label: names: '0': legitimate '1': fake - name: click_bait_score dtype: class_label: names: '0': normal '1': clickbait - name: content_title dtype: string - name: content_url dtype: string - name: content_published_time dtype: string - name: content dtype: string splits: - name: train num_bytes: 24480402 num_examples: 2815 - name: validation num_bytes: 6752242 num_examples: 761 download_size: 8569575 dataset_size: 31232644 --- # Dataset Card for Clickbait/Fake News in Bulgarian ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Data Science Society / Case Fake News](https://gitlab.com/datasciencesociety/case_fake_news) - Repository: [Data Science Society / Case Fake News / Data](https://gitlab.com/datasciencesociety/case_fake_news/-/tree/master/data) - Paper: [This paper uses the dataset.](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary This is a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned. The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks. The dataset was prepared for the Hack the Fake News hackathon. It was provided by the [Bulgarian Association of PR Agencies](http://www.bapra.bg/) and is available in [Gitlab](https://gitlab.com/datasciencesociety/). The corpus was automatically collected, and then annotated by students of journalism. The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; There are 761 testing examples. There is 98% correlation between fake news and clickbaits. One important aspect about the training dataset is that it contains many repetitions. This should not be surprising as it attempts to represent a natural distribution of factual vs. fake news on-line over a period of time. As publishers of fake news often have a group of websites that feature the same deceiving content, we should expect some repetition. In particular, the training dataset contains 434 unique articles with duplicates. These articles have three reposts each on average, with the most reposted article appearing 45 times. If we take into account the labels of the reposted articles, we can see that if an article is reposted, it is more likely to be fake news. The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322. (The dataset description is from the following [paper](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf).) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Bulgarian ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each entry in the dataset consists of the following elements: * `fake_news_score` - a label indicating whether the article is fake or not * `click_bait_score` - another label indicating whether it is a click-bait * `content_title` - article heading * `content_url` - URL of the original article * `content_published_time` - date of publication * `content` - article content ### Data Splits The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; The validation dataset contains 761 testing examples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@tsvm](https://github.com/tsvm), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
climate_fever	--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|wikipedia - original task_categories: - text-classification - text-retrieval task_ids: - text-scoring - fact-checking - fact-checking-retrieval - semantic-similarity-scoring - multi-input-text-classification paperswithcode_id: climate-fever pretty_name: ClimateFever dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: claim_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO '3': DISPUTED - name: evidences list: - name: evidence_id dtype: string - name: evidence_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO - name: article dtype: string - name: evidence dtype: string - name: entropy dtype: float32 - name: votes list: string splits: - name: test num_bytes: 2429272 num_examples: 1535 download_size: 687133 dataset_size: 2429272 --- # Dataset Card for ClimateFever ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [CLIMATE-FEVER homepage](http://climatefever.ai) - Repository: [CLIMATE-FEVER repository](https://github.com/tdiggelm/climate-fever-dataset) - Paper: [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://arxiv.org/abs/2012.00614) - Leaderboard: [Needs More Information] - Point of Contact: [Thomas Diggelmann](mailto:[email protected]) ### Dataset Summary A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in English, as found in real-world claims about climate-change on the Internet. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances ``` { "claim_id": "0", "claim": "Global warming is driving polar bears toward extinction", "claim_label": 0, # "SUPPORTS" "evidences": [ { "evidence_id": "Extinction risk from global warming:170", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Extinction risk from global warming", "evidence": "\"Recent Research Shows Human Activity Driving Earth Towards Global Extinction Event\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Global warming:14", "evidence_label": 0, # "SUPPORTS" "article": "Global warming", "evidence": "Environmental impacts include the extinction or relocation of many species as their ecosystems change, most immediately the environments of coral reefs, mountains, and the Arctic.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Global warming:178", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Global warming", "evidence": "Rising temperatures push bees to their physiological limits, and could cause the extinction of bee populations.", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Habitat destruction:61", "evidence_label": 0, # "SUPPORTS" "article": "Habitat destruction", "evidence": "Rising global temperatures, caused by the greenhouse effect, contribute to habitat destruction, endangering various species, such as the polar bear.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Polar bear:1328", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Polar bear", "evidence": "\"Bear hunting caught in global warming debate\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] } ] } ``` ### Data Fields - `claim_id`: a `string` feature, unique claim identifier. - `claim`: a `string` feature, claim text. - `claim_label`: a `int` feature, overall label assigned to claim (based on evidence majority vote). The label correspond to 0: "supports", 1: "refutes", 2: "not enough info" and 3: "disputed". - `evidences`: a list of evidences with fields: - `evidence_id`: a `string` feature, unique evidence identifier. - `evidence_label`: a `int` feature, micro-verdict label. The label correspond to 0: "supports", 1: "refutes" and 2: "not enough info". - `article`: a `string` feature, title of source article (Wikipedia page). - `evidence`: a `string` feature, evidence sentence. - `entropy`: a `float32` feature, entropy reflecting uncertainty of `evidence_label`. - `votes`: a `list` of `string` features, corresponding to individual votes. ### Data Splits This benchmark dataset currently consists of a single data split `test` that consists of 1,535 claims or 7,675 claim-evidence pairs. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ```bibtex @misc{diggelmann2020climatefever, title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, year={2020}, eprint={2012.00614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@tdiggelm](https://github.com/tdiggelm) for adding this dataset.
clinc_oos	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: clinc150 pretty_name: CLINC150 dataset_info: - config_name: small features: - name: text dtype: string - name: intent dtype: class_label: names: '0': restaurant_reviews '1': nutrition_info '2': account_blocked '3': oil_change_how '4': time '5': weather '6': redeem_rewards '7': interest_rate '8': gas_type '9': accept_reservations '10': smart_home '11': user_name '12': report_lost_card '13': repeat '14': whisper_mode '15': what_are_your_hobbies '16': order '17': jump_start '18': schedule_meeting '19': meeting_schedule '20': freeze_account '21': what_song '22': meaning_of_life '23': restaurant_reservation '24': traffic '25': make_call '26': text '27': bill_balance '28': improve_credit_score '29': change_language '30': 'no' '31': measurement_conversion '32': timer '33': flip_coin '34': do_you_have_pets '35': balance '36': tell_joke '37': last_maintenance '38': exchange_rate '39': uber '40': car_rental '41': credit_limit '42': oos '43': shopping_list '44': expiration_date '45': routing '46': meal_suggestion '47': tire_change '48': todo_list '49': card_declined '50': rewards_balance '51': change_accent '52': vaccines '53': reminder_update '54': food_last '55': change_ai_name '56': bill_due '57': who_do_you_work_for '58': share_location '59': international_visa '60': calendar '61': translate '62': carry_on '63': book_flight '64': insurance_change '65': todo_list_update '66': timezone '67': cancel_reservation '68': transactions '69': credit_score '70': report_fraud '71': spending_history '72': directions '73': spelling '74': insurance '75': what_is_your_name '76': reminder '77': where_are_you_from '78': distance '79': payday '80': flight_status '81': find_phone '82': greeting '83': alarm '84': order_status '85': confirm_reservation '86': cook_time '87': damaged_card '88': reset_settings '89': pin_change '90': replacement_card_duration '91': new_card '92': roll_dice '93': income '94': taxes '95': date '96': who_made_you '97': pto_request '98': tire_pressure '99': how_old_are_you '100': rollover_401k '101': pto_request_status '102': how_busy '103': application_status '104': recipe '105': calendar_update '106': play_music '107': 'yes' '108': direct_deposit '109': credit_limit_change '110': gas '111': pay_bill '112': ingredients_list '113': lost_luggage '114': goodbye '115': what_can_i_ask_you '116': book_hotel '117': are_you_a_bot '118': next_song '119': change_speed '120': plug_type '121': maybe '122': w2 '123': oil_change_when '124': thank_you '125': shopping_list_update '126': pto_balance '127': order_checks '128': travel_alert '129': fun_fact '130': sync_device '131': schedule_maintenance '132': apr '133': transfer '134': ingredient_substitution '135': calories '136': current_location '137': international_fees '138': calculator '139': definition '140': next_holiday '141': update_playlist '142': mpg '143': min_payment '144': change_user_name '145': restaurant_suggestion '146': travel_notification '147': cancel '148': pto_used '149': travel_suggestion '150': change_volume splits: - name: train num_bytes: 394128 num_examples: 7600 - name: validation num_bytes: 160302 num_examples: 3100 - name: test num_bytes: 286970 num_examples: 5500 download_size: 1702451 dataset_size: 841400 - config_name: imbalanced features: - name: text dtype: string - name: intent dtype: class_label: names: '0': restaurant_reviews '1': nutrition_info '2': account_blocked '3': oil_change_how '4': time '5': weather '6': redeem_rewards '7': interest_rate '8': gas_type '9': accept_reservations '10': smart_home '11': user_name '12': report_lost_card '13': repeat '14': whisper_mode '15': what_are_your_hobbies '16': order '17': jump_start '18': schedule_meeting '19': meeting_schedule '20': freeze_account '21': what_song '22': meaning_of_life '23': restaurant_reservation '24': traffic '25': make_call '26': text '27': bill_balance '28': improve_credit_score '29': change_language '30': 'no' '31': measurement_conversion '32': timer '33': flip_coin '34': do_you_have_pets '35': balance '36': tell_joke '37': last_maintenance '38': exchange_rate '39': uber '40': car_rental '41': credit_limit '42': oos '43': shopping_list '44': expiration_date '45': routing '46': meal_suggestion '47': tire_change '48': todo_list '49': card_declined '50': rewards_balance '51': change_accent '52': vaccines '53': reminder_update '54': food_last '55': change_ai_name '56': bill_due '57': who_do_you_work_for '58': share_location '59': international_visa '60': calendar '61': translate '62': carry_on '63': book_flight '64': insurance_change '65': todo_list_update '66': timezone '67': cancel_reservation '68': transactions '69': credit_score '70': report_fraud '71': spending_history '72': directions '73': spelling '74': insurance '75': what_is_your_name '76': reminder '77': where_are_you_from '78': distance '79': payday '80': flight_status '81': find_phone '82': greeting '83': alarm '84': order_status '85': confirm_reservation '86': cook_time '87': damaged_card '88': reset_settings '89': pin_change '90': replacement_card_duration '91': new_card '92': roll_dice '93': income '94': taxes '95': date '96': who_made_you '97': pto_request '98': tire_pressure '99': how_old_are_you '100': rollover_401k '101': pto_request_status '102': how_busy '103': application_status '104': recipe '105': calendar_update '106': play_music '107': 'yes' '108': direct_deposit '109': credit_limit_change '110': gas '111': pay_bill '112': ingredients_list '113': lost_luggage '114': goodbye '115': what_can_i_ask_you '116': book_hotel '117': are_you_a_bot '118': next_song '119': change_speed '120': plug_type '121': maybe '122': w2 '123': oil_change_when '124': thank_you '125': shopping_list_update '126': pto_balance '127': order_checks '128': travel_alert '129': fun_fact '130': sync_device '131': schedule_maintenance '132': apr '133': transfer '134': ingredient_substitution '135': calories '136': current_location '137': international_fees '138': calculator '139': definition '140': next_holiday '141': update_playlist '142': mpg '143': min_payment '144': change_user_name '145': restaurant_suggestion '146': travel_notification '147': cancel '148': pto_used '149': travel_suggestion '150': change_volume splits: - name: train num_bytes: 546909 num_examples: 10625 - name: validation num_bytes: 160302 num_examples: 3100 - name: test num_bytes: 286970 num_examples: 5500 download_size: 2016773 dataset_size: 994181 - config_name: plus features: - name: text dtype: string - name: intent dtype: class_label: names: '0': restaurant_reviews '1': nutrition_info '2': account_blocked '3': oil_change_how '4': time '5': weather '6': redeem_rewards '7': interest_rate '8': gas_type '9': accept_reservations '10': smart_home '11': user_name '12': report_lost_card '13': repeat '14': whisper_mode '15': what_are_your_hobbies '16': order '17': jump_start '18': schedule_meeting '19': meeting_schedule '20': freeze_account '21': what_song '22': meaning_of_life '23': restaurant_reservation '24': traffic '25': make_call '26': text '27': bill_balance '28': improve_credit_score '29': change_language '30': 'no' '31': measurement_conversion '32': timer '33': flip_coin '34': do_you_have_pets '35': balance '36': tell_joke '37': last_maintenance '38': exchange_rate '39': uber '40': car_rental '41': credit_limit '42': oos '43': shopping_list '44': expiration_date '45': routing '46': meal_suggestion '47': tire_change '48': todo_list '49': card_declined '50': rewards_balance '51': change_accent '52': vaccines '53': reminder_update '54': food_last '55': change_ai_name '56': bill_due '57': who_do_you_work_for '58': share_location '59': international_visa '60': calendar '61': translate '62': carry_on '63': book_flight '64': insurance_change '65': todo_list_update '66': timezone '67': cancel_reservation '68': transactions '69': credit_score '70': report_fraud '71': spending_history '72': directions '73': spelling '74': insurance '75': what_is_your_name '76': reminder '77': where_are_you_from '78': distance '79': payday '80': flight_status '81': find_phone '82': greeting '83': alarm '84': order_status '85': confirm_reservation '86': cook_time '87': damaged_card '88': reset_settings '89': pin_change '90': replacement_card_duration '91': new_card '92': roll_dice '93': income '94': taxes '95': date '96': who_made_you '97': pto_request '98': tire_pressure '99': how_old_are_you '100': rollover_401k '101': pto_request_status '102': how_busy '103': application_status '104': recipe '105': calendar_update '106': play_music '107': 'yes' '108': direct_deposit '109': credit_limit_change '110': gas '111': pay_bill '112': ingredients_list '113': lost_luggage '114': goodbye '115': what_can_i_ask_you '116': book_hotel '117': are_you_a_bot '118': next_song '119': change_speed '120': plug_type '121': maybe '122': w2 '123': oil_change_when '124': thank_you '125': shopping_list_update '126': pto_balance '127': order_checks '128': travel_alert '129': fun_fact '130': sync_device '131': schedule_maintenance '132': apr '133': transfer '134': ingredient_substitution '135': calories '136': current_location '137': international_fees '138': calculator '139': definition '140': next_holiday '141': update_playlist '142': mpg '143': min_payment '144': change_user_name '145': restaurant_suggestion '146': travel_notification '147': cancel '148': pto_used '149': travel_suggestion '150': change_volume splits: - name: train num_bytes: 791255 num_examples: 15250 - name: validation num_bytes: 160302 num_examples: 3100 - name: test num_bytes: 286970 num_examples: 5500 download_size: 2509789 dataset_size: 1238527 --- # Dataset Card for CLINC150 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/clinc/oos-eval/) - Repository: [Github](https://github.com/clinc/oos-eval/) - Paper: [Aclweb](https://www.aclweb.org/anthology/D19-1131) - Leaderboard: [PapersWithCode](https://paperswithcode.com/sota/text-classification-on-clinc-oos) - Point of Contact: ### Dataset Summary Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope (OOS), i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. It offers a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems. ### Supported Tasks and Leaderboards - `intent-classification`: This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data. [here](https://paperswithcode.com/sota/text-classification-on-clinc-oos). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'text' : 'can you walk me through setting up direct deposits to my bank of internet savings account', 'label' : 108 } ``` ### Data Fields - text : Textual data - label : 150 intent classes over 10 domains, the dataset contains one label for 'out-of-scope' intent. The Label Id to Label Name map is mentioned in the table below: \| Label Id \| Label name \| \|--- \|--- \| \| 0 \| restaurant_reviews \| \| 1 \| nutrition_info \| \| 2 \| account_blocked \| \| 3 \| oil_change_how \| \| 4 \| time \| \| 5 \| weather \| \| 6 \| redeem_rewards \| \| 7 \| interest_rate \| \| 8 \| gas_type \| \| 9 \| accept_reservations \| \| 10 \| smart_home \| \| 11 \| user_name \| \| 12 \| report_lost_card \| \| 13 \| repeat \| \| 14 \| whisper_mode \| \| 15 \| what_are_your_hobbies \| \| 16 \| order \| \| 17 \| jump_start \| \| 18 \| schedule_meeting \| \| 19 \| meeting_schedule \| \| 20 \| freeze_account \| \| 21 \| what_song \| \| 22 \| meaning_of_life \| \| 23 \| restaurant_reservation \| \| 24 \| traffic \| \| 25 \| make_call \| \| 26 \| text \| \| 27 \| bill_balance \| \| 28 \| improve_credit_score \| \| 29 \| change_language \| \| 30 \| no \| \| 31 \| measurement_conversion \| \| 32 \| timer \| \| 33 \| flip_coin \| \| 34 \| do_you_have_pets \| \| 35 \| balance \| \| 36 \| tell_joke \| \| 37 \| last_maintenance \| \| 38 \| exchange_rate \| \| 39 \| uber \| \| 40 \| car_rental \| \| 41 \| credit_limit \| \| 42 \| oos \| \| 43 \| shopping_list \| \| 44 \| expiration_date \| \| 45 \| routing \| \| 46 \| meal_suggestion \| \| 47 \| tire_change \| \| 48 \| todo_list \| \| 49 \| card_declined \| \| 50 \| rewards_balance \| \| 51 \| change_accent \| \| 52 \| vaccines \| \| 53 \| reminder_update \| \| 54 \| food_last \| \| 55 \| change_ai_name \| \| 56 \| bill_due \| \| 57 \| who_do_you_work_for \| \| 58 \| share_location \| \| 59 \| international_visa \| \| 60 \| calendar \| \| 61 \| translate \| \| 62 \| carry_on \| \| 63 \| book_flight \| \| 64 \| insurance_change \| \| 65 \| todo_list_update \| \| 66 \| timezone \| \| 67 \| cancel_reservation \| \| 68 \| transactions \| \| 69 \| credit_score \| \| 70 \| report_fraud \| \| 71 \| spending_history \| \| 72 \| directions \| \| 73 \| spelling \| \| 74 \| insurance \| \| 75 \| what_is_your_name \| \| 76 \| reminder \| \| 77 \| where_are_you_from \| \| 78 \| distance \| \| 79 \| payday \| \| 80 \| flight_status \| \| 81 \| find_phone \| \| 82 \| greeting \| \| 83 \| alarm \| \| 84 \| order_status \| \| 85 \| confirm_reservation \| \| 86 \| cook_time \| \| 87 \| damaged_card \| \| 88 \| reset_settings \| \| 89 \| pin_change \| \| 90 \| replacement_card_duration \| \| 91 \| new_card \| \| 92 \| roll_dice \| \| 93 \| income \| \| 94 \| taxes \| \| 95 \| date \| \| 96 \| who_made_you \| \| 97 \| pto_request \| \| 98 \| tire_pressure \| \| 99 \| how_old_are_you \| \| 100 \| rollover_401k \| \| 101 \| pto_request_status \| \| 102 \| how_busy \| \| 103 \| application_status \| \| 104 \| recipe \| \| 105 \| calendar_update \| \| 106 \| play_music \| \| 107 \| yes \| \| 108 \| direct_deposit \| \| 109 \| credit_limit_change \| \| 110 \| gas \| \| 111 \| pay_bill \| \| 112 \| ingredients_list \| \| 113 \| lost_luggage \| \| 114 \| goodbye \| \| 115 \| what_can_i_ask_you \| \| 116 \| book_hotel \| \| 117 \| are_you_a_bot \| \| 118 \| next_song \| \| 119 \| change_speed \| \| 120 \| plug_type \| \| 121 \| maybe \| \| 122 \| w2 \| \| 123 \| oil_change_when \| \| 124 \| thank_you \| \| 125 \| shopping_list_update \| \| 126 \| pto_balance \| \| 127 \| order_checks \| \| 128 \| travel_alert \| \| 129 \| fun_fact \| \| 130 \| sync_device \| \| 131 \| schedule_maintenance \| \| 132 \| apr \| \| 133 \| transfer \| \| 134 \| ingredient_substitution \| \| 135 \| calories \| \| 136 \| current_location \| \| 137 \| international_fees \| \| 138 \| calculator \| \| 139 \| definition \| \| 140 \| next_holiday \| \| 141 \| update_playlist \| \| 142 \| mpg \| \| 143 \| min_payment \| \| 144 \| change_user_name \| \| 145 \| restaurant_suggestion \| \| 146 \| travel_notification \| \| 147 \| cancel \| \| 148 \| pto_used \| \| 149 \| travel_suggestion \| \| 150 \| change_volume \| ### Data Splits The dataset comes in different subsets: - `small` : Small, in which there are only 50 training queries per each in-scope intent - `imbalanced` : Imbalanced, in which intents have either 25, 50, 75, or 100 training queries. - `plus`: OOS+, in which there are 250 out-of-scope training examples, rather than 100. \| name \|train\|validation\|test\| \|----------\|----:\|---------:\|---:\| \|small\|7600\| 3100\| 5500 \| \|imbalanced\|10625\| 3100\| 5500\| \|plus\|15250\| 3100\| 5500\| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{larson-etal-2019-evaluation, title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction", author = "Larson, Stefan and Mahendran, Anish and Peper, Joseph J. and Clarke, Christopher and Lee, Andrew and Hill, Parker and Kummerfeld, Jonathan K. and Leach, Kevin and Laurenzano, Michael A. and Tang, Lingjia and Mars, Jason", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", year = "2019", url = "https://www.aclweb.org/anthology/D19-1131" } ``` ### Contributions Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.
clue	--- annotations_creators: - other language_creators: - other language: - zh license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification - multiple-choice task_ids: - topic-classification - semantic-similarity-scoring - natural-language-inference - multiple-choice-qa paperswithcode_id: clue pretty_name: 'CLUE: Chinese Language Understanding Evaluation benchmark' tags: - coreference-nli - qa-nli dataset_info: - config_name: afqmc features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' - name: idx dtype: int32 splits: - name: test num_bytes: 378726 num_examples: 3861 - name: train num_bytes: 3396535 num_examples: 34334 - name: validation num_bytes: 426293 num_examples: 4316 download_size: 1195044 dataset_size: 4201554 - config_name: tnews features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': '100' '1': '101' '2': '102' '3': '103' '4': '104' '5': '106' '6': '107' '7': '108' '8': '109' '9': '110' '10': '112' '11': '113' '12': '114' '13': '115' '14': '116' - name: idx dtype: int32 splits: - name: test num_bytes: 810974 num_examples: 10000 - name: train num_bytes: 4245701 num_examples: 53360 - name: validation num_bytes: 797926 num_examples: 10000 download_size: 5123575 dataset_size: 5854601 - config_name: iflytek features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' '10': '10' '11': '11' '12': '12' '13': '13' '14': '14' '15': '15' '16': '16' '17': '17' '18': '18' '19': '19' '20': '20' '21': '21' '22': '22' '23': '23' '24': '24' '25': '25' '26': '26' '27': '27' '28': '28' '29': '29' '30': '30' '31': '31' '32': '32' '33': '33' '34': '34' '35': '35' '36': '36' '37': '37' '38': '38' '39': '39' '40': '40' '41': '41' '42': '42' '43': '43' '44': '44' '45': '45' '46': '46' '47': '47' '48': '48' '49': '49' '50': '50' '51': '51' '52': '52' '53': '53' '54': '54' '55': '55' '56': '56' '57': '57' '58': '58' '59': '59' '60': '60' '61': '61' '62': '62' '63': '63' '64': '64' '65': '65' '66': '66' '67': '67' '68': '68' '69': '69' '70': '70' '71': '71' '72': '72' '73': '73' '74': '74' '75': '75' '76': '76' '77': '77' '78': '78' '79': '79' '80': '80' '81': '81' '82': '82' '83': '83' '84': '84' '85': '85' '86': '86' '87': '87' '88': '88' '89': '89' '90': '90' '91': '91' '92': '92' '93': '93' '94': '94' '95': '95' '96': '96' '97': '97' '98': '98' '99': '99' '100': '100' '101': '101' '102': '102' '103': '103' '104': '104' '105': '105' '106': '106' '107': '107' '108': '108' '109': '109' '110': '110' '111': '111' '112': '112' '113': '113' '114': '114' '115': '115' '116': '116' '117': '117' '118': '118' - name: idx dtype: int32 splits: - name: test num_bytes: 2105688 num_examples: 2600 - name: train num_bytes: 10028613 num_examples: 12133 - name: validation num_bytes: 2157123 num_examples: 2599 download_size: 6505938 dataset_size: 14291424 - config_name: cmnli features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 2386837 num_examples: 13880 - name: train num_bytes: 67685309 num_examples: 391783 - name: validation num_bytes: 2051845 num_examples: 12241 download_size: 31404066 dataset_size: 72123991 - config_name: cluewsc2020 features: - name: idx dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': 'true' '1': 'false' - name: target struct: - name: span1_text dtype: string - name: span2_text dtype: string - name: span1_index dtype: int32 - name: span2_index dtype: int32 splits: - name: test num_bytes: 645649 num_examples: 2574 - name: train num_bytes: 288828 num_examples: 1244 - name: validation num_bytes: 72682 num_examples: 304 download_size: 281384 dataset_size: 1007159 - config_name: csl features: - name: idx dtype: int32 - name: corpus_id dtype: int32 - name: abst dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' - name: keyword sequence: string splits: - name: test num_bytes: 2463740 num_examples: 3000 - name: train num_bytes: 16478914 num_examples: 20000 - name: validation num_bytes: 2464575 num_examples: 3000 download_size: 3234594 dataset_size: 21407229 - config_name: cmrc2018 features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 3112066 num_examples: 2000 - name: train num_bytes: 15508110 num_examples: 10142 - name: validation num_bytes: 5183809 num_examples: 3219 - name: trial num_bytes: 1606931 num_examples: 1002 download_size: 3405146 dataset_size: 25410916 - config_name: drcd features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 4982402 num_examples: 3493 - name: train num_bytes: 37443458 num_examples: 26936 - name: validation num_bytes: 5222753 num_examples: 3524 download_size: 7264200 dataset_size: 47648613 - config_name: chid features: - name: idx dtype: int32 - name: candidates sequence: string - name: content sequence: string - name: answers sequence: - name: text dtype: string - name: candidate_id dtype: int32 splits: - name: test num_bytes: 11480463 num_examples: 3447 - name: train num_bytes: 252478178 num_examples: 84709 - name: validation num_bytes: 10117789 num_examples: 3218 download_size: 139199202 dataset_size: 274076430 - config_name: c3 features: - name: id dtype: int32 - name: context sequence: string - name: question dtype: string - name: choice sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1600166 num_examples: 1625 - name: train num_bytes: 9672787 num_examples: 11869 - name: validation num_bytes: 2990967 num_examples: 3816 download_size: 3495930 dataset_size: 14263920 - config_name: ocnli features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 376066 num_examples: 3000 - name: train num_bytes: 6187190 num_examples: 50437 - name: validation num_bytes: 366235 num_examples: 2950 download_size: 4359754 dataset_size: 6929491 - config_name: diagnostics features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 42400 num_examples: 514 download_size: 12062 dataset_size: 42400 --- # Dataset Card for "clue" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.cluebenchmarks.com - Repository: https://github.com/CLUEbenchmark/CLUE - Paper: [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://aclanthology.org/2020.coling-main.419/) - Point of Contact: [Zhenzhong Lan](mailto:[email protected]) - Size of downloaded dataset files: 198.68 MB - Size of the generated dataset: 486.34 MB - Total amount of disk used: 685.02 MB ### Dataset Summary CLUE, A Chinese Language Understanding Evaluation Benchmark (https://www.cluebenchmarks.com/) is a collection of resources for training, evaluating, and analyzing Chinese language understanding systems. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### afqmc - Size of downloaded dataset files: 1.20 MB - Size of the generated dataset: 4.20 MB - Total amount of disk used: 5.40 MB An example of 'validation' looks as follows. ``` { "idx": 0, "label": 0, "sentence1": "双十一花呗提额在哪", "sentence2": "里可以提花呗额度" } ``` #### c3 - Size of downloaded dataset files: 3.20 MB - Size of the generated dataset: 15.69 MB - Total amount of disk used: 18.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "比人的灵敏", "choice": ["没有人的灵敏", "和人的差不多", "和人的一样好", "比人的灵敏"], "context": "[\"许多动物的某些器官感觉特别灵敏，它们能比人类提前知道一些灾害事件的发生，例如，海洋中的水母能预报风暴，老鼠能事先躲避矿井崩塌或有害气体，等等。地震往往能使一些动物的某些感觉器官受到刺激而发生异常反应。如一个地区的重力发生变异，某些动物可能通过它们的平衡...", "id": 1, "question": "动物的器官感觉与人的相比有什么不同?" } ``` #### chid - Size of downloaded dataset files: 139.20 MB - Size of the generated dataset: 274.08 MB - Total amount of disk used: 413.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": { "candidate_id": [3, 5, 6, 1, 7, 4, 0], "text": ["碌碌无为", "无所作为", "苦口婆心", "得过且过", "未雨绸缪", "软硬兼施", "传宗接代"] }, "candidates": "[\"传宗接代\", \"得过且过\", \"咄咄逼人\", \"碌碌无为\", \"软硬兼施\", \"无所作为\", \"苦口婆心\", \"未雨绸缪\", \"和衷共济\", \"人老珠黄\"]...", "content": "[\"谈到巴萨目前的成就，瓜迪奥拉用了“坚持”两个字来形容。自从上世纪90年代克鲁伊夫带队以来，巴萨就坚持每年都有拉玛西亚球员进入一队的传统。即便是范加尔时代，巴萨强力推出的“巴萨五鹰”德拉·佩纳、哈维、莫雷罗、罗杰·加西亚和贝拉乌桑几乎#idiom0000...", "idx": 0 } ``` #### cluewsc2020 - Size of downloaded dataset files: 0.28 MB - Size of the generated dataset: 1.03 MB - Total amount of disk used: 1.29 MB An example of 'train' looks as follows. ``` { "idx": 0, "label": 1, "target": { "span1_index": 3, "span1_text": "伤口", "span2_index": 27, "span2_text": "它们" }, "text": "裂开的伤口涂满尘土，里面有碎石子和木头刺，我小心翼翼把它们剔除出去。" } ``` #### cmnli - Size of downloaded dataset files: 31.40 MB - Size of the generated dataset: 72.12 MB - Total amount of disk used: 103.53 MB An example of 'train' looks as follows. ``` { "idx": 0, "label": 0, "sentence1": "从概念上讲，奶油略读有两个基本维度-产品和地理。", "sentence2": "产品和地理位置是使奶油撇油起作用的原因。" } ``` ### Data Fields The data fields are the same among all splits. #### afqmc - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `0` (0), `1` (1). - `idx`: a `int32` feature. #### c3 - `id`: a `int32` feature. - `context`: a `list` of `string` features. - `question`: a `string` feature. - `choice`: a `list` of `string` features. - `answer`: a `string` feature. #### chid - `idx`: a `int32` feature. - `candidates`: a `list` of `string` features. - `content`: a `list` of `string` features. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `candidate_id`: a `int32` feature. #### cluewsc2020 - `idx`: a `int32` feature. - `text`: a `string` feature. - `label`: a classification label, with possible values including `true` (0), `false` (1). - `span1_text`: a `string` feature. - `span2_text`: a `string` feature. - `span1_index`: a `int32` feature. - `span2_index`: a `int32` feature. #### cmnli - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `neutral` (0), `entailment` (1), `contradiction` (2). - `idx`: a `int32` feature. ### Data Splits \| name \|train \|validation\|test \| \|-----------\|-----:\|---------:\|----:\| \|afqmc \| 34334\| 4316\| 3861\| \|c3 \| 11869\| 3816\| 3892\| \|chid \| 84709\| 3218\| 3231\| \|cluewsc2020\| 1244\| 304\| 290\| \|cmnli \|391783\| 12241\|13880\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{xu-etal-2020-clue, title = "{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark", author = "Xu, Liang and Hu, Hai and Zhang, Xuanwei and Li, Lu and Cao, Chenjie and Li, Yudong and Xu, Yechen and Sun, Kai and Yu, Dian and Yu, Cong and Tian, Yin and Dong, Qianqian and Liu, Weitang and Shi, Bo and Cui, Yiming and Li, Junyi and Zeng, Jun and Wang, Rongzhao and Xie, Weijian and Li, Yanting and Patterson, Yina and Tian, Zuoyu and Zhang, Yiwen and Zhou, He and Liu, Shaoweihua and Zhao, Zhe and Zhao, Qipeng and Yue, Cong and Zhang, Xinrui and Yang, Zhengliang and Richardson, Kyle and Lan, Zhenzhong", booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2020.coling-main.419", doi = "10.18653/v1/2020.coling-main.419", pages = "4762--4772", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@JetRunner](https://github.com/JetRunner) for adding this dataset.
cmrc2018	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - zh license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: cmrc-2018 pretty_name: Chinese Machine Reading Comprehension 2018 dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 15508110 num_examples: 10142 - name: validation num_bytes: 5183809 num_examples: 3219 - name: test num_bytes: 1606931 num_examples: 1002 download_size: 11508117 dataset_size: 22298850 --- # Dataset Card for "cmrc2018" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/ymcui/cmrc2018](https://github.com/ymcui/cmrc2018) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 11.50 MB - Size of the generated dataset: 22.31 MB - Total amount of disk used: 33.83 MB ### Dataset Summary A Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 11.50 MB - Size of the generated dataset: 22.31 MB - Total amount of disk used: 33.83 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [11, 11], "text": ["光荣和ω-force", "光荣和ω-force"] }, "context": "\"《战国无双3》（）是由光荣和ω-force开发的战国无双系列的正统第三续作。本作以三大故事为主轴，分别是以武田信玄等人为主的《关东三国志》，织田信长等人为主的《战国三杰》，石田三成等人为主的《关原的年轻武者》，丰富游戏内的剧情。此部份专门介绍角色，欲知武...", "id": "DEV_0_QUERY_0", "question": "《战国无双3》是由哪两个公司合作开发的？" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits \| name \| train \| validation \| test \| \| ------- \| ----: \| ---------: \| ---: \| \| default \| 10142 \| 3219 \| 1002 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{cui-emnlp2019-cmrc2018, title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension", author = "Cui, Yiming and Liu, Ting and Che, Wanxiang and Xiao, Li and Chen, Zhipeng and Ma, Wentao and Wang, Shijin and Hu, Guoping", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1600", doi = "10.18653/v1/D19-1600", pages = "5886--5891", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
cmu_hinglish_dog	--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - en - hi license: - cc-by-sa-3.0 - gfdl multilinguality: - multilingual - translation pretty_name: CMU Document Grounded Conversations size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] dataset_info: features: - name: date dtype: string - name: docIdx dtype: int64 - name: translation dtype: translation: languages: - en - hi_en - name: uid dtype: string - name: utcTimestamp dtype: string - name: rating dtype: int64 - name: status dtype: int64 - name: uid1LogInTime dtype: string - name: uid1LogOutTime dtype: string - name: uid1response struct: - name: response sequence: int64 - name: type dtype: string - name: uid2response struct: - name: response sequence: int64 - name: type dtype: string - name: user2_id dtype: string - name: whoSawDoc sequence: string - name: wikiDocumentIdx dtype: int64 splits: - name: train num_bytes: 3142398 num_examples: 8060 - name: test num_bytes: 379521 num_examples: 960 - name: validation num_bytes: 368726 num_examples: 942 download_size: 8749685 dataset_size: 3890645 --- # Dataset Card for CMU Document Grounded Conversations ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [CMU Hinglish DoG](http://festvox.org/cedar/data/notyet/) - Repository: [CMU Document Grounded Conversations (English version)](https://github.com/festvox/datasets-CMU_DoG) - Paper: [CMU Document Grounded Conversations (English version)](https://arxiv.org/pdf/1809.07358.pdf) - Point of Contact: ### Dataset Summary This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English versions. Can be used for Translating between the two. The dataset has been provided by Prof. Alan Black's group from CMU. ### Supported Tasks and Leaderboards - `abstractive-mt` ### Languages ## Dataset Structure ### Data Instances A typical data point comprises a Hinglish text, with key `hi_en` and its English version with key `en`. The `docIdx` contains the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document. The `uid` has the user id of this utterance. An example from the CMU_Hinglish_DoG train set looks as follows: ``` {'rating': 2, 'wikiDocumentIdx': 13, 'utcTimestamp': '2018-03-16T17:48:22.037Z', 'uid': 'user2', 'date': '2018-03-16T17:47:21.964Z', 'uid2response': {'response': [1, 2, 3, 5], 'type': 'finish'}, 'uid1LogInTime': '2018-03-16T17:47:21.964Z', 'user2_id': 'USR664', 'uid1LogOutTime': '2018-03-16T18:02:29.072Z', 'whoSawDoc': ['user1', 'user2'], 'status': 1, 'docIdx': 0, 'uid1response': {'response': [1, 2, 3, 4], 'type': 'finish'}, 'translation': {'en': 'The director is Zack Snyder, 27% Rotten Tomatoes, 4.9/10.', 'hi_en': 'Zack Snyder director hai, 27% Rotten Tomatoes, 4.9/10.'}} ``` ### Data Fields - `date`: the time the file is created, as a string - `docIdx`: the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document. - `translation`: - `hi_en`: The text in Hinglish - `en`: The text in English - `uid`: the user id of this utterance. - `utcTimestamp`: the server utc timestamp of this utterance, as a string - `rating`: A number from 1 or 2 or 3. A larger number means the quality of the conversation is better. - `status`: status as an integer - `uid1LogInTime`: optional login time of user 1, as a string - `uid1LogOutTime`: optional logout time of user 1, as a string - `uid1response`: a json object contains the status and response of user after finishing the conversation. Fields in the object includes: - `type`: should be one of ['finish', 'abandon','abandonWithouAnsweringFeedbackQuestion']. 'finish' means the user successfully finishes the conversation, either by completing 12 or 15 turns or in the way that the other user leaves the conversation first. 'abandon' means the user abandons the conversation in the middle, but entering the feedback page. 'abandonWithouAnsweringFeedbackQuestion' means the user just disconnects or closes the web page without providing the feedback. - `response`: the answer to the post-conversation questions. The worker can choose multiple of them. The options presented to the user are as follows: For type 'finish' 1: The conversation is understandable. 2: The other user is actively responding me. 3: The conversation goes smoothly. For type 'abandon' 1: The other user is too rude. 2: I don't know how to proceed with the conversation. 3: The other user is not responding to me. For users given the document 4: I have watched the movie before. 5: I have not watched the movie before. For the users without the document 4: I will watch the movie after the other user's introduction. 5: I will not watch the movie after the other user's introduction. - `uid2response`: same as uid1response - `user2_id`: the generated user id of user 2 - `whoSawDoc`: Should be one of ['user1'], ['user2'], ['user1', 'user2']. Indicating which user read the document. - `wikiDocumentId`: the index of the wiki document. ### Data Splits \| name \|train\|validation\|test\| \|----------\|----:\|---------:\|---:\| \|CMU DOG \| 8060\| 942\| 960\| ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data The Hinglish dataset is derived from the original CMU DoG (Document Grounded Conversations Dataset). More info about that can be found in the [repo](https://github.com/festvox/datasets-CMU_DoG) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was initially created by Prof Alan W Black's group at CMU ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @inproceedings{ cmu_dog_emnlp18, title={A Dataset for Document Grounded Conversations}, author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W}, year={2018}, booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing} } ``` ### Contributions Thanks to [@Ishan-Kumar2](https://github.com/Ishan-Kumar2) for adding this dataset.
cnn_dailymail	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: cnn-daily-mail-1 pretty_name: CNN / Daily Mail train-eval-index: - config: 3.0.0 task: summarization task_id: summarization splits: eval_split: test col_mapping: article: text highlights: target dataset_info: - config_name: 3.0.0 features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string splits: - name: train num_bytes: 1261704133 num_examples: 287113 - name: validation num_bytes: 57732436 num_examples: 13368 - name: test num_bytes: 49925756 num_examples: 11490 download_size: 585439472 dataset_size: 1369362325 - config_name: 1.0.0 features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string splits: - name: train num_bytes: 1261704133 num_examples: 287113 - name: validation num_bytes: 57732436 num_examples: 13368 - name: test num_bytes: 49925756 num_examples: 11490 download_size: 585439472 dataset_size: 1369362325 - config_name: 2.0.0 features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string splits: - name: train num_bytes: 1261704133 num_examples: 287113 - name: validation num_bytes: 57732436 num_examples: 13368 - name: test num_bytes: 49925756 num_examples: 11490 download_size: 585439472 dataset_size: 1369362325 --- # Dataset Card for CNN Dailymail Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: [CNN / DailyMail Dataset repository](https://github.com/abisee/cnn-dailymail) - Paper: [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), [Get To The Point: Summarization with Pointer-Generator Networks](https://www.aclweb.org/anthology/K16-1028.pdf) - Leaderboard: [Papers with Code leaderboard for CNN / Dailymail Dataset](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) - Point of Contact: [Abigail See](mailto:[email protected]) ### Dataset Summary The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering. ### Supported Tasks and Leaderboards - 'summarization': [Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models. ### Languages The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data. ## Dataset Structure ### Data Instances For each instance, there is a string for the article, a string for the highlights, and a string for the id. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=cnn_dailymail&config=3.0.0) to explore more examples. ``` {'id': '0054d6d30dbcad772e20b22771153a2a9cbeaf62', 'article': '(CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.' 'highlights': 'The elderly woman suffered from diabetes and hypertension, ship's doctors say .\nPreviously, 86 passengers had fallen ill on the ship, Agencia Brasil says .'} ``` The average token count for the articles and the highlights are provided below: \| Feature \| Mean Token Count \| \| ---------- \| ---------------- \| \| Article \| 781 \| \| Highlights \| 56 \| ### Data Fields - `id`: a string containing the heximal formated SHA1 hash of the url where the story was retrieved from - `article`: a string containing the body of the news article - `highlights`: a string containing the highlight of the article as written by the article author ### Data Splits The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset. \| Dataset Split \| Number of Instances in Split \| \| ------------- \| ------------------------------------------- \| \| Train \| 287,113 \| \| Validation \| 13,368 \| \| Test \| 11,490 \| ## Dataset Creation ### Curation Rationale Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels. ### Source Data #### Initial Data Collection and Normalization The data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015. The code for the original data collection is available at <https://github.com/deepmind/rc-data>. The articles were downloaded using archives of <www.cnn.com> and <www.dailymail.co.uk> on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <https://cs.nyu.edu/~kcho/DMQA/>. An updated version of the code that does not anonymize the data is available at <https://github.com/abisee/cnn-dailymail>. Hermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them. #### Who are the source language producers? The text was written by journalists at CNN and the Daily Mail. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Version 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences. This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated. ### Discussion of Biases [Bordia and Bowman (2019)](https://www.aclweb.org/anthology/N19-3002.pdf) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'. Because the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published. ### Other Known Limitations News articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article [(Kryściński et al, 2019)](https://www.aclweb.org/anthology/D19-1051.pdf). [Chen et al (2016)](https://www.aclweb.org/anthology/P16-1223.pdf) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors. It should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles. ## Additional Information ### Dataset Curators The data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format. Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions. The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040. ### Licensing Information The CNN / Daily Mail dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @inproceedings{see-etal-2017-get, title = "Get To The Point: Summarization with Pointer-Generator Networks", author = "See, Abigail and Liu, Peter J. and Manning, Christopher D.", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-1099", doi = "10.18653/v1/P17-1099", pages = "1073--1083", abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.", } ``` ``` @inproceedings{DBLP:conf/nips/HermannKGEKSB15, author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom}, title={Teaching Machines to Read and Comprehend}, year={2015}, cdate={1420070400000}, pages={1693-1701}, url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend}, booktitle={NIPS}, crossref={conf/nips/2015} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.
coached_conv_pref	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - other - text-generation - fill-mask - token-classification task_ids: - dialogue-modeling - parsing paperswithcode_id: coached-conversational-preference-elicitation pretty_name: Coached Conversational Preference Elicitation tags: - Conversational Recommendation dataset_info: features: - name: conversationId dtype: string - name: utterances sequence: - name: index dtype: int32 - name: speaker dtype: class_label: names: '0': USER '1': ASSISTANT - name: text dtype: string - name: segments sequence: - name: startIndex dtype: int32 - name: endIndex dtype: int32 - name: text dtype: string - name: annotations sequence: - name: annotationType dtype: class_label: names: '0': ENTITY_NAME '1': ENTITY_PREFERENCE '2': ENTITY_DESCRIPTION '3': ENTITY_OTHER - name: entityType dtype: class_label: names: '0': MOVIE_GENRE_OR_CATEGORY '1': MOVIE_OR_SERIES '2': PERSON '3': SOMETHING_ELSE config_name: coached_conv_pref splits: - name: train num_bytes: 2295579 num_examples: 502 download_size: 5191959 dataset_size: 2295579 --- # Dataset Card for Coached Conversational Preference Elicitation ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Coached Conversational Preference Elicitation Homepage](https://research.google/tools/datasets/coached-conversational-preference-elicitation/) - Repository: [Coached Conversational Preference Elicitation Repository](https://github.com/google-research-datasets/ccpe) - Paper: [Aclweb](https://www.aclweb.org/anthology/W19-5941/) ### Dataset Summary A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities. ### Supported Tasks and Leaderboards * `other-other-Conversational Recommendation`: The dataset can be used to train a model for Conversational recommendation, which consists in Coached Conversation Preference Elicitation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A typical data point comprises of a series of utterances between the 'assistant' and the 'user'. Each such utterance is annotated into categories mentioned in data fields. An example from the Coached Conversational Preference Elicitation dataset looks as follows: ``` {'conversationId': 'CCPE-6faee', 'utterances': {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'segments': [{'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [0], 'entityType': [0]}, {'annotationType': [1], 'entityType': [0]}], 'endIndex': [20, 27], 'startIndex': [14, 0], 'text': ['comedy', 'I really like comedy movies']}, {'annotations': [{'annotationType': [0], 'entityType': [0]}], 'endIndex': [24], 'startIndex': [16], 'text': ['comedies']}, {'annotations': [{'annotationType': [1], 'entityType': [0]}], 'endIndex': [15], 'startIndex': [0], 'text': ['I love to laugh']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [0], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [21, 21], 'startIndex': [8, 0], 'text': ['Step Brothers', 'I liked Step Brothers']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [1], 'entityType': [1]}], 'endIndex': [32], 'startIndex': [0], 'text': ['Had some amazing one-liners that']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [0], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [15, 15], 'startIndex': [13, 0], 'text': ['RV', "I don't like RV"]}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [1], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [48, 66], 'startIndex': [18, 50], 'text': ['It was just so slow and boring', "I didn't like it"]}, {'annotations': [{'annotationType': [0], 'entityType': [1]}], 'endIndex': [63], 'startIndex': [33], 'text': ['Jurassic World: Fallen Kingdom']}, {'annotations': [{'annotationType': [0], 'entityType': [1]}, {'annotationType': [3], 'entityType': [1]}], 'endIndex': [52, 52], 'startIndex': [22, 0], 'text': ['Jurassic World: Fallen Kingdom', 'I have seen the movie Jurassic World: Fallen Kingdom']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [1], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [24, 125, 161], 'startIndex': [0, 95, 135], 'text': ['I really like the actors', 'I just really like the scenery', 'the dinosaurs were awesome']}], 'speaker': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], 'text': ['What kinds of movies do you like?', 'I really like comedy movies.', 'Why do you like comedies?', "I love to laugh and comedy movies, that's their whole purpose. Make you laugh.", 'Alright, how about a movie you liked?', 'I liked Step Brothers.', 'Why did you like that movie?', 'Had some amazing one-liners that still get used today even though the movie was made awhile ago.', 'Well, is there a movie you did not like?', "I don't like RV.", 'Why not?', "And I just didn't It was just so slow and boring. I didn't like it.", 'Ok, then have you seen the movie Jurassic World: Fallen Kingdom', 'I have seen the movie Jurassic World: Fallen Kingdom.', 'What is it about these kinds of movies that you like or dislike?', 'I really like the actors. I feel like they were doing their best to make the movie better. And I just really like the scenery, and the the dinosaurs were awesome.']}} ``` ### Data Fields Each conversation has the following fields: * `conversationId`: A unique random ID for the conversation. The ID has no meaning. * `utterances`: An array of utterances by the workers. Each utterance has the following fields: * `index`: A 0-based index indicating the order of the utterances in the conversation. * `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance. * `text`: The raw text as written by the ASSISTANT, or transcribed from the spoken recording of USER. * `segments`: An array of semantic annotations of spans in the text. Each semantic annotation segment has the following fields: * `startIndex`: The position of the start of the annotation in the utterance text. * `endIndex`: The position of the end of the annotation in the utterance text. * `text`: The raw text that has been annotated. * `annotations`: An array of annotation details for this segment. Each annotation has two fields: * `annotationType`: The class of annotation (see ontology below). * `entityType`: The class of the entity to which the text refers (see ontology below). EXPLANATION OF ONTOLOGY In the corpus, preferences and the entities that these preferences refer to are annotated with an annotation type as well as an entity type. Annotation types fall into four categories: * `ENTITY_NAME` (0): These mark the names of relevant entities mentioned. * `ENTITY_PREFERENCE` (1): These are defined as statements indicating that the dialog participant does or does not like the relevant entity in general, or that they do or do not like some aspect of the entity. This may also be thought of the participant having some sentiment about what is being discussed. * `ENTITY_DESCRIPTION` (2): Neutral descriptions that describe an entity but do not convey an explicit liking or disliking. * `ENTITY_OTHER` (3): Other relevant statements about an entity that convey relevant information of how the participant relates to the entity but do not provide a sentiment. Most often, these relate to whether a participant has seen a particular movie, or knows a lot about a given entity. Entity types are marked as belonging to one of four categories: * `MOVIE_GENRE_OR_CATEGORY` (0): For genres or general descriptions that capture a particular type or style of movie. * `MOVIE_OR_SERIES` (1): For the full or partial name of a movie or series of movies. * `PERSON` (2): For the full or partial name of an actual person. * `SOMETHING_ELSE ` (3): For other important proper nouns, such as the names of characters or locations. ### Data Splits There is a single split of the dataset named 'train' which contains the whole datset. \| \| Train \| \| ------------------- \| ----- \| \| Input Conversations \| 502 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{radlinski-etal-2019-ccpe, title = {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences}, author = {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi}, booktitle = {Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue ({SIGDIAL})}, year = 2019 } ``` ### Contributions Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset.

acronym_identification

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: [] paperswithcode_id: acronym-identification pretty_name: Acronym Identification Dataset tags: - acronym-identification dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: labels sequence: class_label: names: '0': B-long '1': B-short '2': I-long '3': I-short '4': O splits: - name: train num_bytes: 7792803 num_examples: 14006 - name: validation num_bytes: 952705 num_examples: 1717 - name: test num_bytes: 987728 num_examples: 1750 download_size: 8556464 dataset_size: 9733236 train-eval-index: - config: default task: token-classification task_id: entity_extraction splits: eval_split: test col_mapping: tokens: tokens labels: tags --- # Dataset Card for Acronym Identification Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://sites.google.com/view/sdu-aaai21/shared-task - **Repository:** https://github.com/amirveyseh/AAAI-21-SDU-shared-task-1-AI - **Paper:** [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://arxiv.org/pdf/2010.14678v1.pdf) - **Leaderboard:** https://competitions.codalab.org/competitions/26609 - **Point of Contact:** [More Information Needed] ### Dataset Summary This dataset contains the training, validation, and test data for the **Shared Task 1: Acronym Identification** of the AAAI-21 Workshop on Scientific Document Understanding. ### Supported Tasks and Leaderboards The dataset supports an `acronym-identification` task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared Task which supported a [leaderboard](https://competitions.codalab.org/competitions/26609). ### Languages The sentences in the dataset are in English (`en`). ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` {'id': 'TR-0', 'labels': [4, 4, 4, 4, 0, 2, 2, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4], 'tokens': ['What', 'is', 'here', 'called', 'controlled', 'natural', 'language', '(', 'CNL', ')', 'has', 'traditionally', 'been', 'given', 'many', 'different', 'names', '.']} ``` Please note that in test set sentences only the `id` and `tokens` fields are available. `labels` can be ignored for test set. Labels in the test set are all `O` ### Data Fields The data instances have the following fields: - `id`: a `string` variable representing the example id, unique across the full dataset - `tokens`: a list of `string` variables representing the word-tokenized sentence - `labels`: a list of `categorical` variables with possible values `["B-long", "B-short", "I-long", "I-short", "O"]` corresponding to a BIO scheme. `-long` corresponds to the expanded acronym, such as *controlled natural language* here, and `-short` to the abbrviation, `CNL` here. ### Data Splits The training, validation, and test set contain `14,006`, `1,717`, and `1750` sentences respectively. ## Dataset Creation ### Curation Rationale > First, most of the existing datasets for acronym identification (AI) are either limited in their sizes or created using simple rule-based methods. > This is unfortunate as rules are in general not able to capture all the diverse forms to express acronyms and their long forms in text. > Second, most of the existing datasets are in the medical domain, ignoring the challenges in other scientific domains. > In order to address these limitations this paper introduces two new datasets for Acronym Identification. > Notably, our datasets are annotated by human to achieve high quality and have substantially larger numbers of examples than the existing AI datasets in the non-medical domain. ### Source Data #### Initial Data Collection and Normalization > In order to prepare a corpus for acronym annotation, we collect a corpus of 6,786 English papers from arXiv. > These papers consist of 2,031,592 sentences that would be used for data annotation for AI in this work. The dataset paper does not report the exact tokenization method. #### Who are the source language producers? The language was comes from papers hosted on the online digital archive [arXiv](https://arxiv.org/). No more information is available on the selection process or identity of the writers. ### Annotations #### Annotation process > Each sentence for annotation needs to contain at least one word in which more than half of the characters in are capital letters (i.e., acronym candidates). > Afterward, we search for a sub-sequence of words in which the concatenation of the first one, two or three characters of the words (in the order of the words in the sub-sequence could form an acronym candidate. > We call the sub-sequence a long form candidate. If we cannot find any long form candidate, we remove the sentence. > Using this process, we end up with 17,506 sentences to be annotated manually by the annotators from Amazon Mechanical Turk (MTurk). > In particular, we create a HIT for each sentence and ask the workers to annotate the short forms and the long forms in the sentence. > In case of disagreements, if two out of three workers agree on an annotation, we use majority voting to decide the correct annotation. > Otherwise, a fourth annotator is hired to resolve the conflict #### Who are the annotators? Workers were recruited through Amazon MEchanical Turk and paid $0.05 per annotation. No further demographic information is provided. ### Personal and Sensitive Information Papers published on arXiv are unlikely to contain much personal information, although some do include some poorly chosen examples revealing personal details, so the data should be used with care. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset provided for this shared task is licensed under CC BY-NC-SA 4.0 international license. ### Citation Information ``` @inproceedings{Veyseh2020, author = {Amir Pouran Ben Veyseh and Franck Dernoncourt and Quan Hung Tran and Thien Huu Nguyen}, editor = {Donia Scott and N{\'{u}}ria Bel and Chengqing Zong}, title = {What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics, {COLING} 2020, Barcelona, Spain (Online), December 8-13, 2020}, pages = {3285--3301}, publisher = {International Committee on Computational Linguistics}, year = {2020}, url = {https://doi.org/10.18653/v1/2020.coling-main.292}, doi = {10.18653/v1/2020.coling-main.292} } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

ade_corpus_v2

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-classification - token-classification task_ids: - coreference-resolution - fact-checking pretty_name: Adverse Drug Reaction Data v2 configs: - Ade_corpus_v2_classification - Ade_corpus_v2_drug_ade_relation - Ade_corpus_v2_drug_dosage_relation dataset_info: - config_name: Ade_corpus_v2_classification features: - name: text dtype: string - name: label dtype: class_label: names: '0': Not-Related '1': Related splits: - name: train num_bytes: 3403711 num_examples: 23516 download_size: 3791162 dataset_size: 3403711 - config_name: Ade_corpus_v2_drug_ade_relation features: - name: text dtype: string - name: drug dtype: string - name: effect dtype: string - name: indexes struct: - name: drug sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 - name: effect sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 splits: - name: train num_bytes: 1546021 num_examples: 6821 download_size: 3791162 dataset_size: 1546021 - config_name: Ade_corpus_v2_drug_dosage_relation features: - name: text dtype: string - name: drug dtype: string - name: dosage dtype: string - name: indexes struct: - name: drug sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 - name: dosage sequence: - name: start_char dtype: int32 - name: end_char dtype: int32 splits: - name: train num_bytes: 64725 num_examples: 279 download_size: 3791162 dataset_size: 64725 train-eval-index: - config: Ade_corpus_v2_classification task: text-classification task_id: multi_class_classification splits: train_split: train col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Adverse Drug Reaction Data v2 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.sciencedirect.com/science/article/pii/S1532046412000615 - **Repository:** [Needs More Information] - **Paper:** https://www.sciencedirect.com/science/article/pii/S1532046412000615 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data. This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug. DRUG-AE.rel provides relations between drugs and adverse effects. DRUG-DOSE.rel provides relations between drugs and dosages. ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain any drug-related adverse effects. ### Supported Tasks and Leaderboards Sentiment classification, Relation Extraction ### Languages English ## Dataset Structure ### Data Instances #### Config - `Ade_corpus_v2_classification` ``` { 'label': 1, 'text': 'Intravenous azithromycin-induced ototoxicity.' } ``` #### Config - `Ade_corpus_v2_drug_ade_relation` ``` { 'drug': 'azithromycin', 'effect': 'ototoxicity', 'indexes': { 'drug': { 'end_char': [24], 'start_char': [12] }, 'effect': { 'end_char': [44], 'start_char': [33] } }, 'text': 'Intravenous azithromycin-induced ototoxicity.' } ``` #### Config - `Ade_corpus_v2_drug_dosage_relation` ``` { 'dosage': '4 times per day', 'drug': 'insulin', 'indexes': { 'dosage': { 'end_char': [56], 'start_char': [41] }, 'drug': { 'end_char': [40], 'start_char': [33]} }, 'text': 'She continued to receive regular insulin 4 times per day over the following 3 years with only occasional hives.' } ``` ### Data Fields #### Config - `Ade_corpus_v2_classification` - `text` - Input text. - `label` - Whether the adverse drug effect(ADE) related (1) or not (0). - #### Config - `Ade_corpus_v2_drug_ade_relation` - `text` - Input text. - `drug` - Name of drug. - `effect` - Effect caused by the drug. - `indexes.drug.start_char` - Start index of `drug` string in text. - `indexes.drug.end_char` - End index of `drug` string in text. - `indexes.effect.start_char` - Start index of `effect` string in text. - `indexes.effect.end_char` - End index of `effect` string in text. #### Config - `Ade_corpus_v2_drug_dosage_relation` - `text` - Input text. - `drug` - Name of drug. - `dosage` - Dosage of the drug. - `indexes.drug.start_char` - Start index of `drug` string in text. - `indexes.drug.end_char` - End index of `drug` string in text. - `indexes.dosage.start_char` - Start index of `dosage` string in text. - `indexes.dosage.end_char` - End index of `dosage` string in text. ### Data Splits | Train | | ------ | | 23516 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @article{GURULINGAPPA2012885, title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports", journal = "Journal of Biomedical Informatics", volume = "45", number = "5", pages = "885 - 892", year = "2012", note = "Text Mining and Natural Language Processing in Pharmacogenomics", issn = "1532-0464", doi = "https://doi.org/10.1016/j.jbi.2012.04.008", url = "http://www.sciencedirect.com/science/article/pii/S1532046412000615", author = "Harsha Gurulingappa and Abdul Mateen Rajput and Angus Roberts and Juliane Fluck and Martin Hofmann-Apitius and Luca Toldo", keywords = "Adverse drug effect, Benchmark corpus, Annotation, Harmonization, Sentence classification", abstract = "A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F1 score of 0.70 indicating a potential useful application of the corpus." } ``` ### Contributions Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

adversarial_qa

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: adversarialqa pretty_name: adversarialQA train-eval-index: - config: adversarialQA task: question-answering task_id: extractive_question_answering splits: train_split: train eval_split: validation col_mapping: question: question context: context answers: text: text answer_start: answer_start metrics: - type: squad name: SQuAD dataset_info: - config_name: adversarialQA features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 27858794 num_examples: 30000 - name: validation num_bytes: 2757128 num_examples: 3000 - name: test num_bytes: 2919643 num_examples: 3000 download_size: 9018914 dataset_size: 33535565 - config_name: dbidaf features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9282518 num_examples: 10000 - name: validation num_bytes: 917943 num_examples: 1000 - name: test num_bytes: 947111 num_examples: 1000 download_size: 9018914 dataset_size: 11147572 - config_name: dbert features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9345557 num_examples: 10000 - name: validation num_bytes: 918192 num_examples: 1000 - name: test num_bytes: 971454 num_examples: 1000 download_size: 9018914 dataset_size: 11235203 - config_name: droberta features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9270719 num_examples: 10000 - name: validation num_bytes: 925065 num_examples: 1000 - name: test num_bytes: 1005406 num_examples: 1000 download_size: 9018914 dataset_size: 11201190 --- # Dataset Card for adversarialQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [adversarialQA homepage](https://adversarialqa.github.io/) - **Repository:** [adversarialQA repository](https://github.com/maxbartolo/adversarialQA) - **Paper:** [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293) - **Leaderboard:** [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall) - **Point of Contact:** [Max Bartolo]([email protected]) ### Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards `extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Data is provided in the same format as SQuAD 1.1. An example is shown below: ``` { "data": [ { "title": "Oxygen", "paragraphs": [ { "context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.", "qas": [ { "id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3", "question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?", "answers": [ { "answer_start": 36, "text": "organic compounds" } ] }, { "id": "4240a8e708c703796347a3702cf1463eed05584a", "question": "What letter does the abbreviation for acid anhydrides both begin and end in?", "answers": [ { "answer_start": 244, "text": "R" } ] }, { "id": "0681a0a5ec852ec6920d6a30f7ef65dced493366", "question": "Which of the organic compounds, in the article, contains nitrogen?", "answers": [ { "answer_start": 262, "text": "amides" } ] }, { "id": "2990efe1a56ccf81938fa5e18104f7d3803069fb", "question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?", "answers": [ { "answer_start": 262, "text": "amides" } ] } ] } ] } ] } ``` ### Data Fields - title: the title of the Wikipedia page from which the context is sourced - context: the context/passage - id: a string identifier for each question - answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text. Note that no answers are provided in the test set. Indeed, this dataset is part of the DynaBench benchmark, for which you can submit your predictions on the [website](https://dynabench.org/tasks/2#1). ### Data Splits The dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples. ## Dataset Creation ### Curation Rationale This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models. ### Source Data #### Initial Data Collection and Normalization The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250). #### Who are the source language producers? The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions. ### Annotations #### Annotation process The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model. #### Who are the annotators? The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation. ### Personal and Sensitive Information No annotator identifying details are provided. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question. It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application. ### Discussion of Biases The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol. ### Other Known Limitations N/a ## Additional Information ### Dataset Curators This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL). ### Licensing Information This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{bartolo2020beat, author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus}, title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {662-678}, year = {2020}, doi = {10.1162/tacl\_a\_00338}, URL = { https://doi.org/10.1162/tacl_a_00338 }, eprint = { https://doi.org/10.1162/tacl_a_00338 }, abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). } } ``` ### Contributions Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.

aeslc

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: 'AESLC: Annotated Enron Subject Line Corpus' size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: aeslc tags: - aspect-based-summarization - conversations-summarization - multi-document-summarization - email-headline-generation dataset_info: features: - name: email_body dtype: string - name: subject_line dtype: string splits: - name: train num_bytes: 11902668 num_examples: 14436 - name: validation num_bytes: 1660730 num_examples: 1960 - name: test num_bytes: 1384177 num_examples: 1906 download_size: 11643743 dataset_size: 14947575 --- # Dataset Card for "aeslc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/ryanzhumich/AESLC - **Paper:** [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://arxiv.org/abs/1906.03497) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 11.64 MB - **Size of the generated dataset:** 14.95 MB - **Total amount of disk used:** 26.59 MB ### Dataset Summary A collection of email messages of employees in the Enron Corporation. There are two features: - email_body: email body text. - subject_line: email subject text. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages Monolingual English (mainly en-US) with some exceptions. ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 11.64 MB - **Size of the generated dataset:** 14.95 MB - **Total amount of disk used:** 26.59 MB An example of 'train' looks as follows. ``` { "email_body": "B/C\n<<some doc>>\n", "subject_line": "Service Agreement" } ``` ### Data Fields The data fields are the same among all splits. #### default - `email_body`: a `string` feature. - `subject_line`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|14436| 1960|1906| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{zhang-tetreault-2019-email, title = "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation", author = "Zhang, Rui and Tetreault, Joel", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1043", doi = "10.18653/v1/P19-1043", pages = "446--456", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

afrikaans_ner_corpus

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Afrikaans Ner Corpus license_details: Creative Commons Attribution 2.5 South Africa License dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC config_name: afrikaans_ner_corpus splits: - name: train num_bytes: 4025667 num_examples: 8962 download_size: 25748344 dataset_size: 4025667 --- # Dataset Card for Afrikaans Ner Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Afrikaans Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/299) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [Martin Puttkammer](mailto:[email protected]) ### Dataset Summary The Afrikaans Ner Corpus is an Afrikaans dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Afrikaans. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [0, 0, 0, 0, 0], 'tokens': ['Vertaling', 'van', 'die', 'inligting', 'in'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Afrikaans. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. [More Information Needed] #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{afrikaans_ner_corpus, author = { Gerhard van Huyssteen and Martin Puttkammer and E.B. Trollip and J.C. Liversage and Roald Eiselen}, title = {NCHLT Afrikaans Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/299}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.

ag_news

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - topic-classification paperswithcode_id: ag-news pretty_name: AG’s News Corpus dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': World '1': Sports '2': Business '3': Sci/Tech splits: - name: train num_bytes: 29817351 num_examples: 120000 - name: test num_bytes: 1879478 num_examples: 7600 download_size: 31327765 dataset_size: 31696829 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "ag_news" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html](http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 31.33 MB - **Size of the generated dataset:** 31.70 MB - **Total amount of disk used:** 63.02 MB ### Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . The AG's news topic classification dataset is constructed by Xiang Zhang ([email protected]) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 31.33 MB - **Size of the generated dataset:** 31.70 MB - **Total amount of disk used:** 63.02 MB An example of 'train' looks as follows. ``` { "label": 3, "text": "New iPad released Just like every other September, this one is no different. Apple is planning to release a bigger, heavier, fatter iPad that..." } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `label`: a classification label, with possible values including `World` (0), `Sports` (1), `Business` (2), `Sci/Tech` (3). ### Data Splits | name |train |test| |-------|-----:|---:| |default|120000|7600| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Zhang2015CharacterlevelCN, title={Character-level Convolutional Networks for Text Classification}, author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun}, booktitle={NIPS}, year={2015} } ``` ### Contributions Thanks to [@jxmorris12](https://github.com/jxmorris12), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@lewtun](https://github.com/lewtun) for adding this dataset.

ai2_arc

--- annotations_creators: - found language_creators: - found language: - en language_bcp47: - en-US license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa - multiple-choice-qa paperswithcode_id: null pretty_name: Ai2Arc dataset_info: - config_name: ARC-Challenge features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 351888 num_examples: 1119 - name: test num_bytes: 377740 num_examples: 1172 - name: validation num_bytes: 97254 num_examples: 299 download_size: 680841265 dataset_size: 826882 - config_name: ARC-Easy features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 623254 num_examples: 2251 - name: test num_bytes: 661997 num_examples: 2376 - name: validation num_bytes: 158498 num_examples: 570 download_size: 680841265 dataset_size: 1443749 --- # Dataset Card for "ai2_arc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/arc](https://allenai.org/data/arc) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1361.68 MB - **Size of the generated dataset:** 2.28 MB - **Total amount of disk used:** 1363.96 MB ### Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### ARC-Challenge - **Size of downloaded dataset files:** 680.84 MB - **Size of the generated dataset:** 0.83 MB - **Total amount of disk used:** 681.67 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` #### ARC-Easy - **Size of downloaded dataset files:** 680.84 MB - **Size of the generated dataset:** 1.45 MB - **Total amount of disk used:** 682.29 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` ### Data Fields The data fields are the same among all splits. #### ARC-Challenge - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. #### ARC-Easy - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. ### Data Splits | name |train|validation|test| |-------------|----:|---------:|---:| |ARC-Challenge| 1119| 299|1172| |ARC-Easy | 2251| 570|2376| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{allenai:arc, author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

air_dialogue

--- pretty_name: AirDialogue annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - conversational - text-generation - fill-mask task_ids: - dialogue-generation - dialogue-modeling - language-modeling - masked-language-modeling paperswithcode_id: null dataset_info: - config_name: air_dialogue_data features: - name: action struct: - name: status dtype: string - name: name dtype: string - name: flight sequence: int32 - name: intent struct: - name: return_month dtype: string - name: return_day dtype: string - name: max_price dtype: int32 - name: departure_airport dtype: string - name: max_connections dtype: int32 - name: departure_day dtype: string - name: goal dtype: string - name: departure_month dtype: string - name: name dtype: string - name: return_airport dtype: string - name: timestamps sequence: int64 - name: dialogue sequence: string - name: expected_action struct: - name: status dtype: string - name: name dtype: string - name: flight sequence: int32 - name: search_info list: - name: button_name dtype: string - name: field_name dtype: string - name: field_value dtype: string - name: timestmamp dtype: int64 - name: correct_sample dtype: bool_ splits: - name: train num_bytes: 353721137 num_examples: 321459 - name: validation num_bytes: 44442238 num_examples: 40363 download_size: 272898923 dataset_size: 398163375 - config_name: air_dialogue_kb features: - name: kb list: - name: airline dtype: string - name: class dtype: string - name: departure_airport dtype: string - name: departure_day dtype: string - name: departure_month dtype: string - name: departure_time_num dtype: int32 - name: flight_number dtype: int32 - name: num_connections dtype: int32 - name: price dtype: int32 - name: return_airport dtype: string - name: return_day dtype: string - name: return_month dtype: string - name: return_time_num dtype: int32 - name: reservation dtype: int32 splits: - name: train num_bytes: 782592158 num_examples: 321459 - name: validation num_bytes: 98269789 num_examples: 40363 download_size: 272898923 dataset_size: 880861947 --- # Dataset Card for air_dialogue ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 - **Repository:** https://github.com/google/airdialogue - **Paper:** https://www.aclweb.org/anthology/D18-1419/ - **Leaderboard:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 - **Point of Contact:** [AirDialogue-Google](mailto:[email protected]) [Aakash Gupta](mailto:[email protected]) ### Dataset Summary AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. ### Supported Tasks and Leaderboards We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores The inference competition & leaderboard can be found here: https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 ### Languages The text in the dataset is in English. The BCP 47 code is `en` ## Dataset Structure ### Data Instances The data is provided in two set of files. The first one has the dialogues (`air_dialogue_data`) and the knowledge-base (`air_dialogue_kb`) BuilderConfig: `air_dialogue_data` ``` {"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true} ``` BuilderConfig: `air_dialogue_kb` ``` {"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0} ``` ### Data Fields BuilderConfig: `air_dialogue_data`: Provides for customer context, dialogue states and environment key name | Description | |---|---| |'search_action' | search action performed by customer | |'action' | Action taken by the agent | |'intent' | Intents from the conversation | |'timestamps' | Timestamp for each of the dialogues | |'dialogue' | Dialogue recorded between agent & customer | |'expected_action' | Expected action from agent (human-annotated)| |'correct_sample' | whether action performed by agent was same as expected_action | BuilderConfig: `air_dialogue_kb`: Provides for the Agent Context _ca_ = (_db_, _r_ ) key name | Description | |---|---| |'kb' | Available flights in the database | |'reservation' | whether customer has an existing reservation| ### Data Splits Data is split into Train/Dev & Test in the ration of 80%, 10% and 10% ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information No personal and sensitive information is stored ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [AirDialogue team](mailto:[email protected]) For issues regarding HuggingFace Dataset Hub implementation [Aakash Gupta](mailto:[email protected]) ### Licensing Information cc-by-nc-4.0 ### Citation Information @inproceedings{wei-etal-2018-airdialogue, title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research", author = "Wei, Wei and Le, Quoc and Dai, Andrew and Li, Jia", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1419", doi = "10.18653/v1/D18-1419", pages = "3844--3854", abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.", } ### Contributions Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.

ajgt_twitter_ar

--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Arabic Jordanian General Tweets dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Negative '1': Positive config_name: plain_text splits: - name: train num_bytes: 175424 num_examples: 1800 download_size: 107395 dataset_size: 175424 --- # Dataset Card for Arabic Jordanian General Tweets ## Table of Contents - [Dataset Card for Arabic Jordanian General Tweets](#dataset-card-for-arabic-jordanian-general-tweets) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [|split|num examples|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Arabic Jordanian General Tweets](https://github.com/komari6/Arabic-twitter-corpus-AJGT) - **Paper:** [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66) - **Point of Contact:** [Khaled Alomari]([email protected]) ### Dataset Summary Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66). ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A binary datset with with negative and positive sentiments. ### Data Fields - `text` (str): Tweet text. - `label` (int): Sentiment. ### Data Splits The dataset is not split. | | train | |----------|------:| | no split | 1,800 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Contains 1,800 tweets collected from twitter. #### Who are the source language producers? From tweeter. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{alomari2017arabic, title={Arabic tweets sentimental analysis using machine learning}, author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled}, booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}, pages={602--610}, year={2017}, organization={Springer} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

allegro_reviews

--- annotations_creators: - found language_creators: - found language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-scoring - text-scoring paperswithcode_id: allegro-reviews pretty_name: Allegro Reviews dataset_info: features: - name: text dtype: string - name: rating dtype: float32 splits: - name: train num_bytes: 4899539 num_examples: 9577 - name: test num_bytes: 514527 num_examples: 1006 - name: validation num_bytes: 515785 num_examples: 1002 download_size: 2314847 dataset_size: 5929851 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://klejbenchmark.com/ - **Repository:** https://github.com/allegro/klejbenchmark-allegroreviews - **Paper:** KLEJ: Comprehensive Benchmark for Polish Language Understanding (Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz) - **Leaderboard:** https://klejbenchmark.com/leaderboard/ - **Point of Contact:** [email protected] ### Dataset Summary Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review). We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com. ### Supported Tasks and Leaderboards Product reviews sentiment analysis. https://klejbenchmark.com/leaderboard/ ### Languages Polish ## Dataset Structure ### Data Instances Two tsv files (train, dev) with two columns (text, rating) and one (test) with just one (text). ### Data Fields - text: a product review of at least 50 words - rating: product rating of a scale of one (negative review) to five (positive review) ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale This dataset is one of nine evaluation tasks to improve polish language processing. ### Source Data #### Initial Data Collection and Normalization The Allegro Reviews is a set of product reviews from a popular e-commerce marketplace (Allegro.pl). #### Who are the source language producers? Customers of an e-commerce marketplace. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Allegro Machine Learning Research team [email protected] ### Licensing Information Dataset licensed under CC BY-SA 4.0 ### Citation Information @inproceedings{rybak-etal-2020-klej, title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding", author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.111", pages = "1191--1201", } ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.

allocine

--- annotations_creators: - no-annotation language_creators: - found language: - fr license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: allocine pretty_name: Allociné dataset_info: features: - name: review dtype: string - name: label dtype: class_label: names: '0': neg '1': pos config_name: allocine splits: - name: train num_bytes: 91330696 num_examples: 160000 - name: validation num_bytes: 11546250 num_examples: 20000 - name: test num_bytes: 11547697 num_examples: 20000 download_size: 66625305 dataset_size: 114424643 train-eval-index: - config: allocine task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: review: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Allociné ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [Allociné dataset repository](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/tree/master/allocine_dataset) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Théophile Blard](mailto:[email protected]) ### Dataset Summary The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the [Allociné.fr](https://www.allocine.fr/) community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k). ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset can be used to train a model for sentiment classification. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. A BERT-based model, [tf-allociné](https://huggingface.co/tblard/tf-allocine), achieves 97.44% accuracy on the test set. ### Languages The text is in French, as spoken by users of the [Allociné.fr](https://www.allocine.fr/) website. The BCP-47 code for French is fr. ## Dataset Structure ### Data Instances Each data instance contains the following features: _review_ and _label_. In the Hugging Face distribution of the dataset, the _label_ has 2 possible values, _0_ and _1_, which correspond to _negative_ and _positive_ respectively. See the [Allociné corpus viewer](https://huggingface.co/datasets/viewer/?dataset=allocine) to explore more examples. An example from the Allociné train set looks like the following: ``` {'review': 'Premier film de la saga Kozure Okami, "Le Sabre de la vengeance" est un très bon film qui mêle drame et action, et qui, en 40 ans, n'a pas pris une ride.', 'label': 1} ``` ### Data Fields - 'review': a string containing the review text - 'label': an integer, either _0_ or _1_, indicating a _negative_ or _positive_ review, respectively ### Data Splits The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews. | Dataset Split | Number of Instances in Split | Percent Negative Reviews | Percent Positive Reviews | | ------------- | ---------------------------- | ------------------------ | ------------------------ | | Train | 160,000 | 49.6% | 50.4% | | Validation | 20,000 | 51.0% | 49.0% | | Test | 20,000 | 52.0% | 48.0% | ## Dataset Creation ### Curation Rationale The Allociné dataset was developed to support large-scale sentiment analysis in French. It was released alongside the [tf-allociné](https://huggingface.co/tblard/tf-allocine) model and used to compare the performance of several language models on this task. ### Source Data #### Initial Data Collection and Normalization The reviews and ratings were collected using a list of [film page urls](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_films_urls.txt) and the [allocine_scraper.py](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_scraper.py) tool. Up to 30 reviews were collected for each film. The reviews were originally labeled with a rating from 0.5 to 5.0 with a step of 0.5 between each rating. Ratings less than or equal to 2 are labeled as negative and ratings greater than or equal to 4 are labeled as positive. Only reviews with less than 2000 characters are included in the dataset. #### Who are the source language producers? The dataset contains movie reviews produced by the online community of the [Allociné.fr](https://www.allocine.fr/) website. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Reviewer usernames or personal information were not collected with the reviews, but could potentially be recovered. The content of each review may include information and opinions about the film's actors, film crew, and plot. ## Considerations for Using the Data ### Social Impact of Dataset Sentiment classification is a complex task which requires sophisticated language understanding skills. Successful models can support decision-making based on the outcome of the sentiment analysis, though such models currently require a high degree of domain specificity. It should be noted that the community represented in the dataset may not represent any downstream application's potential users, and the observed behavior of a model trained on this dataset may vary based on the domain and use case. ### Discussion of Biases The Allociné website lists a number of topics which violate their [terms of service](https://www.allocine.fr/service/conditions.html#charte). Further analysis is needed to determine the extent to which moderators have successfully removed such content. ### Other Known Limitations The limitations of the Allociné dataset have not yet been investigated, however [Staliūnaitė and Bonfil (2017)](https://www.aclweb.org/anthology/W17-5410.pdf) detail linguistic phenomena that are generally present in sentiment analysis but difficult for models to accurately label, such as negation, adverbial modifiers, and reviewer pragmatics. ## Additional Information ### Dataset Curators The Allociné dataset was collected by Théophile Blard. ### Licensing Information The Allociné dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information > Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert> ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@TheophileBlard](https://github.com/TheophileBlard), [@lewtun](https://github.com/lewtun) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.

alt

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - bn - en - fil - hi - id - ja - km - lo - ms - my - th - vi - zh license: - cc-by-4.0 multilinguality: - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation - token-classification task_ids: - parsing paperswithcode_id: alt pretty_name: Asian Language Treebank configs: - alt-en - alt-jp - alt-km - alt-my - alt-my-transliteration - alt-my-west-transliteration - alt-parallel dataset_info: - config_name: alt-parallel features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: translation dtype: translation: languages: - bg - en - en_tok - fil - hi - id - ja - khm - lo - ms - my - th - vi - zh splits: - name: train num_bytes: 68445916 num_examples: 18088 - name: validation num_bytes: 3710979 num_examples: 1000 - name: test num_bytes: 3814431 num_examples: 1019 download_size: 21285784 dataset_size: 75971326 - config_name: alt-en features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: status dtype: string - name: value dtype: string splits: - name: train num_bytes: 10075569 num_examples: 17889 - name: validation num_bytes: 544719 num_examples: 988 - name: test num_bytes: 567272 num_examples: 1017 download_size: 3871379 dataset_size: 11187560 - config_name: alt-jp features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: status dtype: string - name: value dtype: string - name: word_alignment dtype: string - name: jp_tokenized dtype: string - name: en_tokenized dtype: string splits: - name: train num_bytes: 21888277 num_examples: 17202 - name: validation num_bytes: 1181555 num_examples: 953 - name: test num_bytes: 1175592 num_examples: 931 download_size: 13191239 dataset_size: 24245424 - config_name: alt-my features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: value dtype: string splits: - name: train num_bytes: 20433275 num_examples: 18088 - name: validation num_bytes: 1111410 num_examples: 1000 - name: test num_bytes: 1135209 num_examples: 1018 download_size: 3028302 dataset_size: 22679894 - config_name: alt-km features: - name: SNT.URLID dtype: string - name: SNT.URLID.SNTID dtype: string - name: url dtype: string - name: km_pos_tag dtype: string - name: km_tokenized dtype: string splits: - name: train num_bytes: 12015411 num_examples: 18088 - name: validation num_bytes: 655232 num_examples: 1000 - name: test num_bytes: 673753 num_examples: 1018 download_size: 2410832 dataset_size: 13344396 - config_name: alt-my-transliteration features: - name: en dtype: string - name: my sequence: string splits: - name: train num_bytes: 4249424 num_examples: 84022 download_size: 1232127 dataset_size: 4249424 - config_name: alt-my-west-transliteration features: - name: en dtype: string - name: my sequence: string splits: - name: train num_bytes: 7412043 num_examples: 107121 download_size: 2830071 dataset_size: 7412043 --- # Dataset Card for Asian Language Treebank (ALT) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ - **Leaderboard:** - **Paper:** [Introduction of the Asian Language Treebank](https://ieeexplore.ieee.org/abstract/document/7918974) - **Point of Contact:** [ALT info]([email protected]) ### Dataset Summary The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under [ASEAN IVO](https://www.nict.go.jp/en/asean_ivo/index.html) as described in this Web page. The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages. ### Supported Tasks and Leaderboards Machine Translation, Dependency Parsing ### Languages It supports 13 language: * Bengali * English * Filipino * Hindi * Bahasa Indonesia * Japanese * Khmer * Lao * Malay * Myanmar (Burmese) * Thai * Vietnamese * Chinese (Simplified Chinese). ## Dataset Structure ### Data Instances #### ALT Parallel Corpus ``` { "SNT.URLID": "80188", "SNT.URLID.SNTID": "1", "url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal", "bg": "[translated sentence]", "en": "[translated sentence]", "en_tok": "[translated sentence]", "fil": "[translated sentence]", "hi": "[translated sentence]", "id": "[translated sentence]", "ja": "[translated sentence]", "khm": "[translated sentence]", "lo": "[translated sentence]", "ms": "[translated sentence]", "my": "[translated sentence]", "th": "[translated sentence]", "vi": "[translated sentence]", "zh": "[translated sentence]" } ``` #### ALT Treebank ``` { "SNT.URLID": "80188", "SNT.URLID.SNTID": "1", "url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal", "status": "draft/reviewed", "value": "(S (S (BASENP (NNP Italy)) (VP (VBP have) (VP (VP (VP (VBN defeated) (BASENP (NNP Portugal))) (ADVP (RB 31-5))) (PP (IN in) (NP (BASENP (NNP Pool) (NNP C)) (PP (IN of) (NP (BASENP (DT the) (NN 2007) (NNP Rugby) (NNP World) (NNP Cup)) (PP (IN at) (NP (BASENP (NNP Parc) (FW des) (NNP Princes)) (COMMA ,) (BASENP (NNP Paris) (COMMA ,) (NNP France))))))))))) (PERIOD .))" } ``` #### ALT Myanmar transliteration ``` { "en": "CASINO", "my": [ "ကက်စီနို", "ကစီနို", "ကာစီနို", "ကာဆီနို" ] } ``` ### Data Fields #### ALT Parallel Corpus - SNT.URLID: URL link to the source article listed in [URL.txt](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ALT-Parallel-Corpus-20191206/URL.txt) - SNT.URLID.SNTID: index number from 1 to 20000. It is a seletected sentence from `SNT.URLID` and bg, en, fil, hi, id, ja, khm, lo, ms, my, th, vi, zh correspond to the target language #### ALT Treebank - status: it indicates how a sentence is annotated; `draft` sentences are annotated by one annotater and `reviewed` sentences are annotated by two annotater The annotatation is different from language to language, please see [their guildlines](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/) for more detail. ### Data Splits | | train | valid | test | |-----------|-------|-------|-------| | # articles | 1698 | 98 | 97 | | # sentences | 18088 | 1000 | 1018 | ## Dataset Creation ### Curation Rationale The ALT project was initiated by the [National Institute of Information and Communications Technology, Japan](https://www.nict.go.jp/en/) (NICT) in 2014. NICT started to build Japanese and English ALT and worked with the University of Computer Studies, Yangon, Myanmar (UCSY) to build Myanmar ALT in 2014. Then, the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT), the Institute for Infocomm Research, Singapore (I2R), the Institute of Information Technology, Vietnam (IOIT), and the National Institute of Posts, Telecoms and ICT, Cambodia (NIPTICT) joined to make ALT for Indonesian, Malay, Vietnamese, and Khmer in 2015. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset is sampled from the English Wikinews in 2014. These will be annotated with word segmentation, POS tags, and syntax information, in addition to the word alignment information by linguistic experts from * National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English * University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar * the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian * the Institute for Infocomm Research, Singapore (I2R) for Malay * the Institute of Information Technology, Vietnam (IOIT) for Vietnamese * the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators * National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English * University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar * the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian * the Institute for Infocomm Research, Singapore (I2R) for Malay * the Institute of Information Technology, Vietnam (IOIT) for Vietnamese * the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer ### Licensing Information [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) ### Citation Information Please cite the following if you make use of the dataset: Hammam Riza, Michael Purwoadi, Gunarso, Teduh Uliniansyah, Aw Ai Ti, Sharifah Mahani Aljunied, Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Vichet Chea, Rapid Sun, Sethserey Sam, Sopheap Seng, Khin Mar Soe, Khin Thandar Nwet, Masao Utiyama, Chenchen Ding. (2016) "Introduction of the Asian Language Treebank" Oriental COCOSDA. BibTeX: ``` @inproceedings{riza2016introduction, title={Introduction of the asian language treebank}, author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others}, booktitle={2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)}, pages={1--6}, year={2016}, organization={IEEE} } ``` ### Contributions Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset.

amazon_polarity

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Amazon Review Polarity dataset_info: features: - name: label dtype: class_label: names: '0': negative '1': positive - name: title dtype: string - name: content dtype: string config_name: amazon_polarity splits: - name: train num_bytes: 1604364432 num_examples: 3600000 - name: test num_bytes: 178176193 num_examples: 400000 download_size: 688339454 dataset_size: 1782540625 train-eval-index: - config: amazon_polarity task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: content: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Amazon Review Polarity ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://registry.opendata.aws/ - **Repository:** https://github.com/zhangxiangxiao/Crepe - **Paper:** https://arxiv.org/abs/1509.01626 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Xiang Zhang](mailto:[email protected]) ### Dataset Summary The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating. ### Languages Mainly English. ## Dataset Structure ### Data Instances A typical data point, comprises of a title, a content and the corresponding label. An example from the AmazonPolarity test set looks as follows: ``` { 'title':'Great CD', 'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""", 'label':1 } ``` ### Data Fields - 'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'label': either 1 (positive) or 0 (negative) rating. ### Data Splits The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples. ## Dataset Creation ### Curation Rationale The Amazon reviews polarity dataset is constructed by Xiang Zhang ([email protected]). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Apache License 2.0 ### Citation Information McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013. Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015) ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

amazon_reviews_multi

--- annotations_creators: - found language_creators: - found language: - de - en - es - fr - ja - zh license: - other multilinguality: - monolingual - multilingual size_categories: - 100K<n<1M - 1M<n<10M source_datasets: - original task_categories: - summarization - text-generation - fill-mask - text-classification task_ids: - text-scoring - language-modeling - masked-language-modeling - sentiment-classification - sentiment-scoring - topic-classification paperswithcode_id: null pretty_name: The Multilingual Amazon Reviews Corpus configs: - all_languages - de - en - es - fr - ja - zh dataset_info: - config_name: all_languages features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 364405048 num_examples: 1200000 - name: validation num_bytes: 9047533 num_examples: 30000 - name: test num_bytes: 9099141 num_examples: 30000 download_size: 640320386 dataset_size: 382551722 - config_name: de features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 64485678 num_examples: 200000 - name: validation num_bytes: 1605727 num_examples: 5000 - name: test num_bytes: 1611044 num_examples: 5000 download_size: 94802490 dataset_size: 67702449 - config_name: en features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 58601089 num_examples: 200000 - name: validation num_bytes: 1474672 num_examples: 5000 - name: test num_bytes: 1460565 num_examples: 5000 download_size: 86094112 dataset_size: 61536326 - config_name: es features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 52375658 num_examples: 200000 - name: validation num_bytes: 1303958 num_examples: 5000 - name: test num_bytes: 1312347 num_examples: 5000 download_size: 81345461 dataset_size: 54991963 - config_name: fr features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 54593565 num_examples: 200000 - name: validation num_bytes: 1340763 num_examples: 5000 - name: test num_bytes: 1364510 num_examples: 5000 download_size: 85917293 dataset_size: 57298838 - config_name: ja features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 82401390 num_examples: 200000 - name: validation num_bytes: 2035391 num_examples: 5000 - name: test num_bytes: 2048048 num_examples: 5000 download_size: 177773783 dataset_size: 86484829 - config_name: zh features: - name: review_id dtype: string - name: product_id dtype: string - name: reviewer_id dtype: string - name: stars dtype: int32 - name: review_body dtype: string - name: review_title dtype: string - name: language dtype: string - name: product_category dtype: string splits: - name: train num_bytes: 51947668 num_examples: 200000 - name: validation num_bytes: 1287106 num_examples: 5000 - name: test num_bytes: 1302711 num_examples: 5000 download_size: 114387247 dataset_size: 54537485 --- # Dataset Card for The Multilingual Amazon Reviews Corpus ## Table of Contents - [Dataset Card for amazon_reviews_multi](#dataset-card-for-amazon_reviews_multi) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [plain_text](#plain_text) - [Data Fields](#data-fields) - [plain_text](#plain_text-1) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Webpage:** https://registry.opendata.aws/amazon-reviews-ml/ - **Paper:** https://arxiv.org/abs/2010.02573 - **Point of Contact:** [[email protected]](mailto:[email protected]) ### Dataset Summary We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long. Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish. ## Dataset Structure ### Data Instances Each data instance corresponds to a review. The original JSON for an instance looks like so (German example): ```json { "review_id": "de_0784695", "product_id": "product_de_0572654", "reviewer_id": "reviewer_de_0645436", "stars": "1", "review_body": "Leider, leider nach einmal waschen ausgeblichen . Es sieht super h\u00fcbsch aus , nur leider stinkt es ganz schrecklich und ein Waschgang in der Maschine ist notwendig ! Nach einem mal waschen sah es aus als w\u00e4re es 10 Jahre alt und hatte 1000 e von Waschg\u00e4ngen hinter sich :( echt schade !", "review_title": "Leider nicht zu empfehlen", "language": "de", "product_category": "home" } ``` ### Data Fields - `review_id`: A string identifier of the review. - `product_id`: A string identifier of the product being reviewed. - `reviewer_id`: A string identifier of the reviewer. - `stars`: An int between 1-5 indicating the number of stars. - `review_body`: The text body of the review. - `review_title`: The text title of the review. - `language`: The string identifier of the review language. - `product_category`: String representation of the product's category. ### Data Splits Each language configuration comes with its own `train`, `validation`, and `test` splits. The `all_languages` split is simply a concatenation of the corresponding split across all languages. That is, the `train` split for `all_languages` is a concatenation of the `train` splits for each of the languages and likewise for `validation` and `test`. ## Dataset Creation ### Curation Rationale The dataset is motivated by the desire to advance sentiment analysis and text classification in other (non-English) languages. ### Source Data #### Initial Data Collection and Normalization The authors gathered the reviews from the marketplaces in the US, Japan, Germany, France, Spain, and China for the English, Japanese, German, French, Spanish, and Chinese languages, respectively. They then ensured the correct language by applying a language detection algorithm, only retaining those of the target language. In a random sample of the resulting reviews, the authors observed a small percentage of target languages that were incorrectly filtered out and a very few mismatched languages that were incorrectly retained. #### Who are the source language producers? The original text comes from Amazon customers reviewing products on the marketplace across a variety of product categories. ### Annotations #### Annotation process Each of the fields included are submitted by the user with the review or otherwise associated with the review. No manual or machine-driven annotation was necessary. #### Who are the annotators? N/A ### Personal and Sensitive Information According to the original dataset [license terms](https://docs.opendata.aws/amazon-reviews-ml/license.txt), you may not: - link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or - attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures. Unfortunately, each of the languages included here is relatively high resource and well studied. ### Discussion of Biases The dataset contains only reviews from verified purchases (as described in the paper, section 2.1), and the reviews should conform the [Amazon Community Guidelines](https://www.amazon.com/gp/help/customer/display.html?nodeId=GLHXEX85MENUE4XF). ### Other Known Limitations The dataset is constructed so that the distribution of star ratings is balanced. This feature has some advantages for purposes of classification, but some types of language may be over or underrepresented relative to the original distribution of reviews to achieve this balance. ## Additional Information ### Dataset Curators Published by Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. Managed by Amazon. ### Licensing Information Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc. A copy of the agreement can be found at the dataset webpage here: https://docs.opendata.aws/amazon-reviews-ml/license.txt By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions: In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. ### Citation Information Please cite the following paper (arXiv) if you found this dataset useful: Phillip Keung, Yichao Lu, György Szarvas and Noah A. Smith. “The Multilingual Amazon Reviews Corpus.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020. ``` @inproceedings{marc_reviews, title={The Multilingual Amazon Reviews Corpus}, author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing}, year={2020} } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

amazon_us_reviews

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - summarization - text-generation - fill-mask - text-classification task_ids: - text-scoring - language-modeling - masked-language-modeling - sentiment-classification - sentiment-scoring - topic-classification pretty_name: Amazon US Reviews dataset_info: - config_name: Books_v1_01 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 6997552259 num_examples: 6106719 download_size: 2692708591 dataset_size: 6997552259 - config_name: Watches_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 458976082 num_examples: 960872 download_size: 162973819 dataset_size: 458976082 - config_name: Personal_Care_Appliances_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 49036547 num_examples: 85981 download_size: 17634794 dataset_size: 49036547 - config_name: Mobile_Electronics_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 63293377 num_examples: 104975 download_size: 22870508 dataset_size: 63293377 - config_name: Digital_Video_Games_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 80176851 num_examples: 145431 download_size: 27442648 dataset_size: 80176851 - config_name: Digital_Software_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 58782931 num_examples: 102084 download_size: 18997559 dataset_size: 58782931 - config_name: Major_Appliances_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 67642424 num_examples: 96901 download_size: 24359816 dataset_size: 67642424 - config_name: Gift_Card_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 47188062 num_examples: 149086 download_size: 12134676 dataset_size: 47188062 - config_name: Video_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 356264426 num_examples: 380604 download_size: 138929896 dataset_size: 356264426 - config_name: Luggage_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 167354173 num_examples: 348657 download_size: 60320191 dataset_size: 167354173 - config_name: Software_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 266020595 num_examples: 341931 download_size: 94010685 dataset_size: 266020595 - config_name: Video_Games_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1291054668 num_examples: 1785997 download_size: 475199894 dataset_size: 1291054668 - config_name: Furniture_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 405212374 num_examples: 792113 download_size: 148982796 dataset_size: 405212374 - config_name: Musical_Instruments_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 518908568 num_examples: 904765 download_size: 193389086 dataset_size: 518908568 - config_name: Digital_Music_Purchase_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 710546079 num_examples: 1688884 download_size: 253570168 dataset_size: 710546079 - config_name: Books_v1_02 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3387034903 num_examples: 3105520 download_size: 1329539135 dataset_size: 3387034903 - config_name: Home_Entertainment_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 534333848 num_examples: 705889 download_size: 193168458 dataset_size: 534333848 - config_name: Grocery_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1072289473 num_examples: 2402458 download_size: 401337166 dataset_size: 1072289473 - config_name: Outdoors_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1172986088 num_examples: 2302401 download_size: 448963100 dataset_size: 1172986088 - config_name: Pet_Products_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1355659812 num_examples: 2643619 download_size: 515815253 dataset_size: 1355659812 - config_name: Video_DVD_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3953234561 num_examples: 5069140 download_size: 1512355451 dataset_size: 3953234561 - config_name: Apparel_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2256558450 num_examples: 5906333 download_size: 648641286 dataset_size: 2256558450 - config_name: PC_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3982684438 num_examples: 6908554 download_size: 1512903923 dataset_size: 3982684438 - config_name: Tools_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 872273119 num_examples: 1741100 download_size: 333782939 dataset_size: 872273119 - config_name: Jewelry_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 703275869 num_examples: 1767753 download_size: 247022254 dataset_size: 703275869 - config_name: Baby_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 956952590 num_examples: 1752932 download_size: 357392893 dataset_size: 956952590 - config_name: Home_Improvement_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1329688315 num_examples: 2634781 download_size: 503339178 dataset_size: 1329688315 - config_name: Camera_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1187101912 num_examples: 1801974 download_size: 442653086 dataset_size: 1187101912 - config_name: Lawn_and_Garden_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1272255987 num_examples: 2557288 download_size: 486772662 dataset_size: 1272255987 - config_name: Office_Products_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1370685534 num_examples: 2642434 download_size: 512323500 dataset_size: 1370685534 - config_name: Electronics_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1875406721 num_examples: 3093869 download_size: 698828243 dataset_size: 1875406721 - config_name: Automotive_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1520191087 num_examples: 3514942 download_size: 582145299 dataset_size: 1520191087 - config_name: Digital_Video_Download_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1484214187 num_examples: 4057147 download_size: 506979922 dataset_size: 1484214187 - config_name: Mobile_Apps_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1627857158 num_examples: 5033376 download_size: 557959415 dataset_size: 1627857158 - config_name: Shoes_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 1781283508 num_examples: 4366916 download_size: 642255314 dataset_size: 1781283508 - config_name: Toys_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2197820069 num_examples: 4864249 download_size: 838451398 dataset_size: 2197820069 - config_name: Sports_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2241349145 num_examples: 4850360 download_size: 872478735 dataset_size: 2241349145 - config_name: Kitchen_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2453735305 num_examples: 4880466 download_size: 930744854 dataset_size: 2453735305 - config_name: Beauty_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2399292506 num_examples: 5115666 download_size: 914070021 dataset_size: 2399292506 - config_name: Music_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3900138839 num_examples: 4751577 download_size: 1521994296 dataset_size: 3900138839 - config_name: Health_Personal_Care_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2679427491 num_examples: 5331449 download_size: 1011180212 dataset_size: 2679427491 - config_name: Digital_Ebook_Purchase_v1_01 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 3470453859 num_examples: 5101693 download_size: 1294879074 dataset_size: 3470453859 - config_name: Home_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 2796680249 num_examples: 6221559 download_size: 1081002012 dataset_size: 2796680249 - config_name: Wireless_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 4633213433 num_examples: 9002021 download_size: 1704713674 dataset_size: 4633213433 - config_name: Books_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 7197687124 num_examples: 10319090 download_size: 2740337188 dataset_size: 7197687124 - config_name: Digital_Ebook_Purchase_v1_00 features: - name: marketplace dtype: string - name: customer_id dtype: string - name: review_id dtype: string - name: product_id dtype: string - name: product_parent dtype: string - name: product_title dtype: string - name: product_category dtype: string - name: star_rating dtype: int32 - name: helpful_votes dtype: int32 - name: total_votes dtype: int32 - name: vine dtype: class_label: names: '0': N '1': Y - name: verified_purchase dtype: class_label: names: '0': N '1': Y - name: review_headline dtype: string - name: review_body dtype: string - name: review_date dtype: string splits: - name: train num_bytes: 7302303804 num_examples: 12520722 download_size: 2689739299 dataset_size: 7302303804 --- # Dataset Card for "amazon_us_reviews" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://s3.amazonaws.com/amazon-reviews-pds/readme.html](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 32377.29 MB - **Size of the generated dataset:** 82820.19 MB - **Total amount of disk used:** 115197.49 MB ### Dataset Summary Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews. Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters). Each Dataset contains the following columns : marketplace - 2 letter country code of the marketplace where the review was written. customer_id - Random identifier that can be used to aggregate reviews written by a single author. review_id - The unique ID of the review. product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id. product_parent - Random identifier that can be used to aggregate reviews for the same product. product_title - Title of the product. product_category - Broad product category that can be used to group reviews (also used to group the dataset into coherent parts). star_rating - The 1-5 star rating of the review. helpful_votes - Number of helpful votes. total_votes - Number of total votes the review received. vine - Review was written as part of the Vine program. verified_purchase - The review is on a verified purchase. review_headline - The title of the review. review_body - The review text. review_date - The date the review was written. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### Apparel_v1_00 - **Size of downloaded dataset files:** 648.64 MB - **Size of the generated dataset:** 2254.36 MB - **Total amount of disk used:** 2903.00 MB An example of 'train' looks as follows. ``` { "customer_id": "45223824", "helpful_votes": 0, "marketplace": "US", "product_category": "Apparel", "product_id": "B016PUU3VO", "product_parent": "893588059", "product_title": "Fruit of the Loom Boys' A-Shirt (Pack of 4)", "review_body": "I ordered the same size as I ordered last time, and these shirts were much larger than the previous order. They were also about 6 inches longer. It was like they sent men's shirts instead of boys' shirts. I'll be returning these...", "review_date": "2015-01-01", "review_headline": "Sizes not correct, too big overall and WAY too long", "review_id": "R1N3Z13931J3O9", "star_rating": 2, "total_votes": 0, "verified_purchase": 1, "vine": 0 } ``` #### Automotive_v1_00 - **Size of downloaded dataset files:** 582.15 MB - **Size of the generated dataset:** 1518.88 MB - **Total amount of disk used:** 2101.03 MB An example of 'train' looks as follows. ``` { "customer_id": "16825098", "helpful_votes": 0, "marketplace": "US", "product_category": "Automotive", "product_id": "B000E4PCGE", "product_parent": "694793259", "product_title": "00-03 NISSAN SENTRA MIRROR RH (PASSENGER SIDE), Power, Non-Heated (2000 00 2001 01 2002 02 2003 03) NS35ER 963015M000", "review_body": "Product was as described, new and a great look. Only bad thing is that one of the screws was stripped so I couldn't tighten all three.", "review_date": "2015-08-31", "review_headline": "new and a great look. Only bad thing is that one of ...", "review_id": "R2RUIDUMDKG7P", "star_rating": 3, "total_votes": 0, "verified_purchase": 1, "vine": 0 } ``` #### Baby_v1_00 - **Size of downloaded dataset files:** 357.40 MB - **Size of the generated dataset:** 956.30 MB - **Total amount of disk used:** 1313.70 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "customer_id": "23299101", "helpful_votes": 2, "marketplace": "US", "product_category": "Baby", "product_id": "B00SN6F9NG", "product_parent": "3470998", "product_title": "Rhoost Nail Clipper for Baby - Ergonomically Designed and Easy to Use Baby Nail Clipper, Natural Wooden Bamboo - Baby Health and Personal Care Kits", "review_body": "\"This is an absolute MUST item to have! I was scared to death to clip my baby's nails. I tried other baby nail clippers and th...", "review_date": "2015-08-31", "review_headline": "If fits so comfortably in my hand and I feel like I have ...", "review_id": "R2DRL5NRODVQ3Z", "star_rating": 5, "total_votes": 2, "verified_purchase": 1, "vine": 0 } ``` #### Beauty_v1_00 - **Size of downloaded dataset files:** 914.08 MB - **Size of the generated dataset:** 2397.39 MB - **Total amount of disk used:** 3311.47 MB An example of 'train' looks as follows. ``` { "customer_id": "24655453", "helpful_votes": 1, "marketplace": "US", "product_category": "Beauty", "product_id": "B00SAQ9DZY", "product_parent": "292127037", "product_title": "12 New, High Quality, Amber 2 ml (5/8 Dram) Glass Bottles, with Orifice Reducer and Black Cap.", "review_body": "These are great for small mixtures for EO's, especially for traveling. I only gave this 4 stars because of the orifice reducer. The hole is so small it is hard to get the oil out. Just needs to be slightly bigger.", "review_date": "2015-08-31", "review_headline": "Good Product", "review_id": "R2A30ALEGLMCGN", "star_rating": 4, "total_votes": 1, "verified_purchase": 1, "vine": 0 } ``` #### Books_v1_00 - **Size of downloaded dataset files:** 2740.34 MB - **Size of the generated dataset:** 7193.86 MB - **Total amount of disk used:** 9934.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "customer_id": "49735028", "helpful_votes": 0, "marketplace": "US", "product_category": "Books", "product_id": "0664254969", "product_parent": "248307276", "product_title": "Presbyterian Creeds: A Guide to the Book of Confessions", "review_body": "\"The Presbyterian Book of Confessions contains multiple Creeds for use by the denomination. This guidebook helps he lay person t...", "review_date": "2015-08-31", "review_headline": "The Presbyterian Book of Confessions contains multiple Creeds for use ...", "review_id": "R2G519UREHRO8M", "star_rating": 3, "total_votes": 1, "verified_purchase": 1, "vine": 0 } ``` ### Data Fields The data fields are the same among all splits. #### Apparel_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Automotive_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Baby_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Beauty_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. #### Books_v1_00 - `marketplace`: a `string` feature. - `customer_id`: a `string` feature. - `review_id`: a `string` feature. - `product_id`: a `string` feature. - `product_parent`: a `string` feature. - `product_title`: a `string` feature. - `product_category`: a `string` feature. - `star_rating`: a `int32` feature. - `helpful_votes`: a `int32` feature. - `total_votes`: a `int32` feature. - `vine`: a classification label, with possible values including `Y` (0), `N` (1). - `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1). - `review_headline`: a `string` feature. - `review_body`: a `string` feature. - `review_date`: a `string` feature. ### Data Splits | name | train | |----------------|-------:| |Apparel_v1_00 | 5906333| |Automotive_v1_00 | 3514942| |Baby_v1_00 | 1752932| |Beauty_v1_00 | 5115666| |Books_v1_00 | 10319090| |Books_v1_01 | 6106719| |Books_v1_02 | 3105520| |Camera_v1_00 | 1801974| |Digital_Ebook_Purchase_v1_00 | 12520722| |Digital_Ebook_Purchase_v1_01 | 5101693| |Digital_Music_Purchase_v1_00 | 1688884| |Digital_Software_v1_00 | 102084| |Digital_Video_Download_v1_00 | 4057147| |Digital_Video_Games_v1_00 | 145431| |Electronics_v1_00 | 3093869| |Furniture_v1_00 | 792113| |Gift_Card_v1_00 | 149086| |Grocery_v1_00 | 2402458| |Health_Personal_Care_v1_00 | 5331449| |Home_Entertainment_v1_00 | 705889| |Home_Improvement_v1_00 | 2634781| |Home_v1_00 | 6221559| |Jewelry_v1_00 | 1767753| |Kitchen_v1_00 | 4880466| |Lawn_and_Garden_v1_00 | 2557288| |Luggage_v1_00 | 348657| |Major_Appliances_v1_00 | 96901| |Mobile_Apps_v1_00 | 5033376| |Mobile_Electronics_v1_00 | 104975| |Music_v1_00 | 4751577| |Musical_Instruments_v1_00 | 904765| |Office_Products_v1_00 | 2642434| |Outdoors_v1_00 | 2302401| |PC_v1_00 | 6908554| |Personal_Care_Appliances_v1_00 | 85981| |Pet_Products_v1_00 | 2643619| |Shoes_v1_00 | 4366916| |Software_v1_00 | 341931| |Sports_v1_00 | 4850360| |Tools_v1_00 | 1741100| |Toys_v1_00 | 4864249| |Video_DVD_v1_00 | 5069140| |Video_Games_v1_00 | 1785997| |Video_v1_00 | 380604| |Watches_v1_00 | 960872| |Wireless_v1_00 | 9002021| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information https://s3.amazonaws.com/amazon-reviews-pds/LICENSE.txt By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the Reviews Library is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions: In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Library for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Library or its contents, including use of the Reviews Library for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Library with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Library. If you violate any of the foregoing conditions, your license to access and use the Reviews Library will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. ### Citation Information No citation information. ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

ambig_qa

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|natural_questions - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: ambigqa pretty_name: 'AmbigQA: Answering Ambiguous Open-domain Questions' dataset_info: - config_name: light features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string splits: - name: train num_bytes: 2739732 num_examples: 10036 - name: validation num_bytes: 805808 num_examples: 2002 download_size: 19700900 dataset_size: 3545540 - config_name: full features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string - name: viewed_doc_titles sequence: string - name: used_queries sequence: - name: query dtype: string - name: results sequence: - name: title dtype: string - name: snippet dtype: string - name: nq_answer sequence: string - name: nq_doc_title dtype: string splits: - name: train num_bytes: 43538733 num_examples: 10036 - name: validation num_bytes: 15383368 num_examples: 2002 download_size: 19700900 dataset_size: 58922101 --- # Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [**Homepage:**](https://nlp.cs.washington.edu/ambigqa/) - [**Repository:**](https://github.com/shmsw25/AmbigQA) - [**Paper:**](https://arxiv.org/pdf/2004.10645.pdf) ### Dataset Summary AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with 14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity. We provide two distributions of our new dataset AmbigNQ: a `full` version with all annotation metadata and a `light` version with only inputs and outputs. ### Supported Tasks and Leaderboards `question-answering` ### Languages English ## Dataset Structure ### Data Instances An example from the data set looks as follows: ``` {'annotations': {'answer': [[]], 'qaPairs': [{'answer': [['April 19, 1987'], ['December 17, 1989']], 'question': ['When did the Simpsons first air on television as an animated short on the Tracey Ullman Show?', 'When did the Simpsons first air as a half-hour prime time show?']}], 'type': ['multipleQAs']}, 'id': '-4469503464110108318', 'nq_answer': ['December 17 , 1989'], 'nq_doc_title': 'The Simpsons', 'question': 'When did the simpsons first air on television?', 'used_queries': {'query': ['When did the simpsons first air on television?'], 'results': [{'snippet': ['The Simpsons is an American animated television sitcom starring the animated \nSimpson family, ... Since its debut on December 17, 1989, the show has \nbroadcast 673 episodes and its 30th season started ... The Simpsons first season \nwas the Fox network's first TV series to rank among a season's top 30 highest-\nrated shows.', 'The Simpsons is an American animated sitcom created by Matt Groening for the \nFox ... Since its debut on December 17, 1989, 674 episodes of The Simpsons \nhave been broadcast. ... When producer James L. Brooks was working on the \ntelevision variety show The Tracey Ullman Show, he decided to include small \nanimated ...', '... in shorts from The Tracey Ullman Show as their television debut in 1987. The \nSimpsons shorts are a series of animated shorts that aired as a recurring \nsegment on Fox variety television series The Tracey ... The final short to air was "\nTV Simpsons", originally airing on May 14, 1989. The Simpsons later debuted on\n ...', 'The first season of the American animated television series The Simpsons \noriginally aired on the Fox network between December 17, 1989, and May 13, \n1990, beginning with the Christmas special "Simpsons Roasting on an Open Fire\n". The executive producers for the first production season were Matt Groening, ...', 'The Simpsons is an American animated television sitcom created by Matt \nGroening for the Fox ... Since its debut on December 17, 1989, The Simpsons \nhas broadcast 674 episodes. The show holds several American television \nlongevity ...', 'The opening sequence of the American animated television series The Simpsons \nis among the most popular opening sequences in television and is accompanied \nby one of television's most recognizable theme songs. The first episode to use \nthis intro was the series' second episode "Bart the ... was the first episode of The \nSimpsons to air in 720p high-definition television, ...', '"Simpsons Roasting on an Open Fire", titled onscreen as "The Simpsons \nChristmas Special", is the premiere episode of the American animated TV series \nThe Simpsons, ... The show was originally intended to debut earlier in 1989 with "\nSome Enchanted Evening", but due to animation problems with that episode, the \nshow ...', '"Stark Raving Dad" is the first episode of the third season of the American \nanimated television series The Simpsons. It first aired on the Fox network in the \nUnited States on September 19, 1991. ... The Simpsons was the second highest \nrated show on Fox the week it aired, behind Married... with Children. "Stark \nRaving Dad," ...', 'The Simpsons' twentieth season aired on Fox from September 28, 2008 to May \n17, 2009. With this season, the show tied Gunsmoke as the longest-running \nAmerican primetime television series in terms of total number ... It was the first-\never episode of the show to air in Europe before being seen in the United States.', 'The animated TV show The Simpsons is an American English language \nanimated sitcom which ... The Simpsons was dubbed for the first time in Punjabi \nand aired on Geo TV in Pakistan. The name of the localised Punjabi version is \nTedi Sim ...'], 'title': ['History of The Simpsons', 'The Simpsons', 'The Simpsons shorts', 'The Simpsons (season 1)', 'List of The Simpsons episodes', 'The Simpsons opening sequence', 'Simpsons Roasting on an Open Fire', 'Stark Raving Dad', 'The Simpsons (season 20)', 'Non-English versions of The Simpsons']}]}, 'viewed_doc_titles': ['The Simpsons']} ``` ### Data Fields Full ``` {'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'annotations': Sequence(feature={'type': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'qaPairs': Sequence(feature={'question': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, length=-1, id=None)}, length=-1, id=None), 'viewed_doc_titles': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'used_queries': Sequence(feature={'query': Value(dtype='string', id=None), 'results': Sequence(feature={'title': Value(dtype='string', id=None), 'snippet': Value(dtype='string', id=None)}, length=-1, id=None)}, length=-1, id=None), 'nq_answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'nq_doc_title': Value(dtype='string', id=None)} ``` In the original data format `annotations` have different keys depending on the `type` field = `singleAnswer` or `multipleQAs`. But this implementation uses an empty list `[]` for the unavailable keys please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. ``` for example in train_light_dataset: for i,t in enumerate(example['annotations']['type']): if t =='singleAnswer': # use the example['annotations']['answer'][i] # example['annotations']['qaPairs'][i] - > is [] print(example['annotations']['answer'][i]) else: # use the example['annotations']['qaPairs'][i] # example['annotations']['answer'][i] - > is [] print(example['annotations']['qaPairs'][i]) ``` please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. Light version only has `id`, `question`, `annotations` fields ### Data Splits - train: 10036 - validation: 2002 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data - Wikipedia - NQ-open: ``` @article{ kwiatkowski2019natural, title={ Natural questions: a benchmark for question answering research}, author={ Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others }, journal={ Transactions of the Association for Computational Linguistics }, year={ 2019 } } ``` #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) ### Citation Information ``` @inproceedings{ min2020ambigqa, title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions }, author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke }, booktitle={ EMNLP }, year={2020} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

americas_nli

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ay - bzd - cni - gn - hch - nah - oto - qu - shp - tar license: - unknown multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended|xnli task_categories: - text-classification task_ids: - natural-language-inference pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.' dataset_info: - config_name: aym features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 117538 num_examples: 743 - name: test num_bytes: 115259 num_examples: 750 download_size: 2256093 dataset_size: 232797 - config_name: bzd features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 143362 num_examples: 743 - name: test num_bytes: 127684 num_examples: 750 download_size: 2256093 dataset_size: 271046 - config_name: cni features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 113264 num_examples: 658 - name: test num_bytes: 116292 num_examples: 750 download_size: 2256093 dataset_size: 229556 - config_name: gn features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 115143 num_examples: 743 - name: test num_bytes: 101956 num_examples: 750 download_size: 2256093 dataset_size: 217099 - config_name: hch features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 127974 num_examples: 743 - name: test num_bytes: 120865 num_examples: 750 download_size: 2256093 dataset_size: 248839 - config_name: nah features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 50749 num_examples: 376 - name: test num_bytes: 102961 num_examples: 738 download_size: 2256093 dataset_size: 153710 - config_name: oto features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 27018 num_examples: 222 - name: test num_bytes: 119658 num_examples: 748 download_size: 2256093 dataset_size: 146676 - config_name: quy features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 125644 num_examples: 743 - name: test num_bytes: 112758 num_examples: 750 download_size: 2256093 dataset_size: 238402 - config_name: shp features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 124508 num_examples: 743 - name: test num_bytes: 118942 num_examples: 750 download_size: 2256093 dataset_size: 243450 - config_name: tar features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 139504 num_examples: 743 - name: test num_bytes: 122632 num_examples: 750 download_size: 2256093 dataset_size: 262136 - config_name: all_languages features: - name: language dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 1129092 num_examples: 6457 - name: test num_bytes: 1210591 num_examples: 7486 download_size: 2256093 dataset_size: 2339683 --- # Dataset Card for AmericasNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** https://github.com/nala-cub/AmericasNLI - **Paper:** https://arxiv.org/abs/2104.08726 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages - aym - bzd - cni - gn - hch - nah - oto - quy - shp - tar ## Dataset Structure ### Data Instances #### all_languages An example of the test split looks as follows: ``` {'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p arlxapxti.', 'label': 2} ``` #### aym An example of the test split looks as follows: ``` {'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label ': 2} ``` #### bzd An example of the test split looks as follows: ``` {'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2} ``` #### cni An example of the test split looks as follows: ``` {'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2} ``` #### gn An example of the test split looks as follows: ``` {'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2} ``` #### hch An example of the test split looks as follows: ``` {'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2} ``` #### nah An example of the test split looks as follows: ``` {'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2} ``` #### oto An example of the test split looks as follows: ``` {'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2} ``` #### quy An example of the test split looks as follows: ``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani .', 'label': 2} ``` #### shp An example of the test split looks as follows: ``` {'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2} ``` #### tar An example of the test split looks as follows: ``` {'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2} ``` ### Data Fields #### all_languages - language: a multilingual string variable, with languages including ar, bg, de, el, en. - premise: a multilingual string variable, with languages including ar, bg, de, el, en. - hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### aym - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### bzd - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### cni - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### hch - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### nah - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### oto - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### quy - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### shp - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### tar - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). ### Data Splits | Language | ISO | Family | Dev | Test | |-------------------|-----|:-------------|-----:|-----:| | all_languages | -- | -- | 6457 | 7486 | | Aymara | aym | Aymaran | 743 | 750 | | Ashaninka | cni | Arawak | 658 | 750 | | Bribri | bzd | Chibchan | 743 | 750 | | Guarani | gn | Tupi-Guarani | 743 | 750 | | Nahuatl | nah | Uto-Aztecan | 376 | 738 | | Otomi | oto | Oto-Manguean | 222 | 748 | | Quechua | quy | Quechuan | 743 | 750 | | Raramuri | tar | Uto-Aztecan | 743 | 750 | | Shipibo-Konibo | shp | Panoan | 743 | 750 | | Wixarika | hch | Uto-Aztecan | 743 | 750 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data The authors translate from the Spanish subset of XNLI. > AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version. As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process The dataset comprises expert translations from Spanish XNLI. > Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.” As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @article{DBLP:journals/corr/abs-2104-08726, author = {Abteen Ebrahimi and Manuel Mager and Arturo Oncevay and Vishrav Chaudhary and Luis Chiruzzo and Angela Fan and John Ortega and Ricardo Ramos and Annette Rios and Ivan Vladimir and Gustavo A. Gim{\'{e}}nez{-}Lugo and Elisabeth Mager and Graham Neubig and Alexis Palmer and Rolando A. Coto Solano and Ngoc Thang Vu and Katharina Kann}, title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages}, journal = {CoRR}, volume = {abs/2104.08726}, year = {2021}, url = {https://arxiv.org/abs/2104.08726}, eprinttype = {arXiv}, eprint = {2104.08726}, timestamp = {Mon, 26 Apr 2021 17:25:10 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset.

ami

--- pretty_name: AMI Corpus annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] dataset_info: - config_name: microphone-single features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 42013753 num_examples: 134 - name: validation num_bytes: 5110497 num_examples: 18 - name: test num_bytes: 4821283 num_examples: 16 download_size: 11387715153 dataset_size: 51945533 - config_name: microphone-multi features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file-1-1 dtype: string - name: file-1-2 dtype: string - name: file-1-3 dtype: string - name: file-1-4 dtype: string - name: file-1-5 dtype: string - name: file-1-6 dtype: string - name: file-1-7 dtype: string - name: file-1-8 dtype: string splits: - name: train num_bytes: 42126341 num_examples: 134 - name: validation num_bytes: 5125645 num_examples: 18 - name: test num_bytes: 4834751 num_examples: 16 download_size: 90941506169 dataset_size: 52086737 - config_name: headset-single features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 42491091 num_examples: 136 - name: validation num_bytes: 5110497 num_examples: 18 - name: test num_bytes: 4821283 num_examples: 16 download_size: 11505070978 dataset_size: 52422871 - config_name: headset-multi features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file-0 dtype: string - name: file-1 dtype: string - name: file-2 dtype: string - name: file-3 dtype: string splits: - name: train num_bytes: 42540063 num_examples: 136 - name: validation num_bytes: 5116989 num_examples: 18 - name: test num_bytes: 4827055 num_examples: 16 download_size: 45951596391 dataset_size: 52484107 --- # Dataset Card for AMI Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> Deprecated: This legacy dataset is outdated. Please, use <a href="https://huggingface.co/datasets/edinburghcstr/ami"> edinburghcstr/ami </a> instead. </div> ## Dataset Description - **Homepage:** [AMI corpus](https://groups.inf.ed.ac.uk/ami/corpus/) - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers. ### Dataset Preprocessing Individual samples of the AMI dataset contain very large audio files (between 10 and 60 minutes). Such lengths are unfeasible for most speech recognition models. In the following, we show how the dataset can effectively be chunked into multiple segments as defined by the dataset creators. The following function cuts the long audio files into the defined segment lengths: ```python import librosa import math from datasets import load_dataset SAMPLE_RATE = 16_000 def chunk_audio(batch): new_batch = { "audio": [], "words": [], "speaker": [], "lengths": [], "word_start_times": [], "segment_start_times": [], } audio, _ = librosa.load(batch["file"][0], sr=SAMPLE_RATE) word_idx = 0 num_words = len(batch["words"][0]) for segment_idx in range(len(batch["segment_start_times"][0])): words = [] word_start_times = [] start_time = batch["segment_start_times"][0][segment_idx] end_time = batch["segment_end_times"][0][segment_idx] # go back and forth with word_idx since segments overlap with each other while (word_idx > 1) and (start_time < batch["word_end_times"][0][word_idx - 1]): word_idx -= 1 while word_idx < num_words and (start_time > batch["word_start_times"][0][word_idx]): word_idx += 1 new_batch["audio"].append(audio[int(start_time * SAMPLE_RATE): int(end_time * SAMPLE_RATE)]) while word_idx < num_words and batch["word_start_times"][0][word_idx] < end_time: words.append(batch["words"][0][word_idx]) word_start_times.append(batch["word_start_times"][0][word_idx]) word_idx += 1 new_batch["lengths"].append(end_time - start_time) new_batch["words"].append(words) new_batch["speaker"].append(batch["segment_speakers"][0][segment_idx]) new_batch["word_start_times"].append(word_start_times) new_batch["segment_start_times"].append(batch["segment_start_times"][0][segment_idx]) return new_batch ami = load_dataset("ami", "headset-single") ami = ami.map(chunk_audio, batched=True, batch_size=1, remove_columns=ami["train"].column_names) ``` The segmented audio files can still be as long as a minute. To further chunk the data into shorter audio chunks, you can use the following script. ```python MAX_LENGTH_IN_SECONDS = 20.0 def chunk_into_max_n_seconds(batch): new_batch = { "audio": [], "text": [], } sample_length = batch["lengths"][0] segment_start = batch["segment_start_times"][0] if sample_length > MAX_LENGTH_IN_SECONDS: num_chunks_per_sample = math.ceil(sample_length / MAX_LENGTH_IN_SECONDS) avg_chunk_length = sample_length / num_chunks_per_sample num_words = len(batch["words"][0]) # start chunking by times start_word_idx = end_word_idx = 0 chunk_start_time = 0 for n in range(num_chunks_per_sample): while (end_word_idx < num_words - 1) and (batch["word_start_times"][0][end_word_idx] < segment_start + (n + 1) * avg_chunk_length): end_word_idx += 1 chunk_end_time = int((batch["word_start_times"][0][end_word_idx] - segment_start) * SAMPLE_RATE) new_batch["audio"].append(batch["audio"][0][chunk_start_time: chunk_end_time]) new_batch["text"].append(" ".join(batch["words"][0][start_word_idx: end_word_idx])) chunk_start_time = chunk_end_time start_word_idx = end_word_idx else: new_batch["audio"].append(batch["audio"][0]) new_batch["text"].append(" ".join(batch["words"][0])) return new_batch ami = ami.map(chunk_into_max_n_seconds, batched=True, batch_size=1, remove_columns=ami["train"].column_names, num_proc=64) ``` A segmented and chunked dataset of the config `"headset-single"`can be found [here](https://huggingface.co/datasets/ami-wav2vec2/ami_single_headset_segmented_and_chunked). ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task does not have an active leaderboard at the moment. - `speaker-diarization`: The dataset can be used to train model for Speaker Diarization (SD). The model is presented with an audio file and asked to predict which speaker spoke at what time. ### Languages The audio is in English. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file (or files in the case of the multi-headset or multi-microphone dataset), called `file` and its transcription as a list of words, called `words`. Additional information about the `speakers`, the `word_start_time`, `word_end_time`, `segment_start_time`, `segment_end_time` is given. In addition and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'word_ids': ["ES2004a.D.words1", "ES2004a.D.words2", ...], 'word_start_times': [0.3700000047683716, 0.949999988079071, ...], 'word_end_times': [0.949999988079071, 1.5299999713897705, ...], 'word_speakers': ['A', 'A', ...], 'segment_ids': ["ES2004a.sync.1", "ES2004a.sync.2", ...] 'segment_start_times': [10.944000244140625, 17.618999481201172, ...], 'segment_end_times': [17.618999481201172, 18.722000122070312, ...], 'segment_speakers': ['A', 'B', ...], 'words', ["hmm", "hmm", ...] 'channels': [0, 0, ..], 'file': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f" 'audio': {'path': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f", 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, } ``` ### Data Fields - word_ids: a list of the ids of the words - word_start_times: a list of the start times of when the words were spoken in seconds - word_end_times: a list of the end times of when the words were spoken in seconds - word_speakers: a list of speakers one for each word - segment_ids: a list of the ids of the segments - segment_start_times: a list of the start times of when the segments start - segment_end_times: a list of the start times of when the segments ends - segment_speakers: a list of speakers one for each segment - words: a list of all the spoken words - channels: a list of all channels that were used for each word - file: a path to the audio file - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. ### Data Splits The dataset consists of several configurations, each one having train/validation/test splits: - headset-single: Close talking audio of single headset. This configuration only includes audio belonging to the headset of the person currently speaking. - headset-multi (4 channels): Close talking audio of four individual headset. This configuration includes audio belonging to four individual headsets. For each annotation there are 4 audio files 0, 1, 2, 3. - microphone-single: Far field audio of single microphone. This configuration only includes audio belonging the first microphone, *i.e.* 1-1, of the microphone array. - microphone-multi (8 channels): Far field audio of microphone array. This configuration includes audio of the first microphone array 1-1, 1-2, ..., 1-8. In general, `headset-single` and `headset-multi` include significantly less noise than `microphone-single` and `microphone-multi`. | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | headset-single | 136 (80h) | 18 (9h) | 16 (9h) | | headset-multi (4 channels) | 136 (320h) | 18 (36h) | 16 (36h) | | microphone-single | 136 (80h) | 18 (9h) | 16 (9h) | | microphone-multi (8 channels) | 136 (640h) | 18 (72h) | 16 (72h) | Note that each sample contains between 10 and 60 minutes of audio data which makes it impractical for direct transcription. One should make use of the segment and word start times and end times to chunk the samples into smaller samples of manageable size. ## Dataset Creation All information about the dataset creation can be found [here](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml) ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information CC BY 4.0 ### Citation Information #### TODO ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) and [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. #### TODO

amttl

--- annotations_creators: - crowdsourced language_creators: - found language: - zh license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - parsing pretty_name: AMTTL dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: class_label: names: '0': B '1': I '2': E '3': S config_name: amttl splits: - name: train num_bytes: 1132212 num_examples: 3063 - name: validation num_bytes: 324374 num_examples: 822 - name: test num_bytes: 328525 num_examples: 908 download_size: 685534 dataset_size: 1785111 --- # Dataset Card for AMTTL ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - **Repository:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - **Paper:** [Aclweb](http://aclweb.org/anthology/C18-1307) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @inproceedings{xing2018adaptive, title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text}, author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian}, booktitle={Proceedings of the 27th International Conference on Computational Linguistics}, pages={3619--3630}, year={2018} } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.

anli

--- annotations_creators: - crowdsourced - machine-generated language_creators: - found language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original - extended|hotpot_qa task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification paperswithcode_id: anli pretty_name: Adversarial NLI dataset_info: features: - name: uid dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: reason dtype: string config_name: plain_text splits: - name: train_r1 num_bytes: 8006920 num_examples: 16946 - name: dev_r1 num_bytes: 573444 num_examples: 1000 - name: test_r1 num_bytes: 574933 num_examples: 1000 - name: train_r2 num_bytes: 20801661 num_examples: 45460 - name: dev_r2 num_bytes: 556082 num_examples: 1000 - name: test_r2 num_bytes: 572655 num_examples: 1000 - name: train_r3 num_bytes: 44720895 num_examples: 100459 - name: dev_r3 num_bytes: 663164 num_examples: 1200 - name: test_r3 num_bytes: 657602 num_examples: 1200 download_size: 18621352 dataset_size: 77127356 --- # Dataset Card for "anli" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [https://github.com/facebookresearch/anli/](https://github.com/facebookresearch/anli/) - **Paper:** [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://arxiv.org/abs/1910.14599) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 18.62 MB - **Size of the generated dataset:** 77.12 MB - **Total amount of disk used:** 95.75 MB ### Dataset Summary The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 18.62 MB - **Size of the generated dataset:** 77.12 MB - **Total amount of disk used:** 95.75 MB An example of 'train_r2' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.", "label": 0, "premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...", "reason": "", "uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `uid`: a `string` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `reason`: a `string` feature. ### Data Splits | name |train_r1|dev_r1|train_r2|dev_r2|train_r3|dev_r3|test_r1|test_r2|test_r3| |----------|-------:|-----:|-------:|-----:|-------:|-----:|------:|------:|------:| |plain_text| 16946| 1000| 45460| 1000| 100459| 1200| 1000| 1000| 1200| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [cc-4 Attribution-NonCommercial](https://github.com/facebookresearch/anli/blob/main/LICENSE) ### Citation Information ``` @InProceedings{nie2019adversarial, title={Adversarial NLI: A New Benchmark for Natural Language Understanding}, author={Nie, Yixin and Williams, Adina and Dinan, Emily and Bansal, Mohit and Weston, Jason and Kiela, Douwe}, booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", year = "2020", publisher = "Association for Computational Linguistics", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@easonnie](https://github.com/easonnie), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

app_reviews

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-scoring paperswithcode_id: null pretty_name: AppReviews dataset_info: features: - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int8 splits: - name: train num_bytes: 32769079 num_examples: 288065 download_size: 42592679 dataset_size: 32769079 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Home Page](https://github.com/sealuzh/user_quality) - **Repository:** [Repo Link](https://github.com/sealuzh/user_quality) - **Paper:** [Link](https://giograno.me/assets/pdf/workshop/wama17.pdf) - **Leaderboard: - **Point of Contact:** [Darshan Gandhi]([email protected]) ### Dataset Summary It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches) ### Supported Tasks and Leaderboards The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective. ### Languages The dataset is a monolingual dataset which has the messages English. ## Dataset Structure ### Data Instances The dataset consists of a message in English. {'package_name': 'com.mantz_it.rfanalyzer', 'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.", 'date': 'October 12 2016', 'star': 4} ### Data Fields * package_name : Name of the Software Application Package * review : Message of the user * date : date when the user posted the review * star : rating provied by the user for the application ### Data Splits There is training data, with a total of : 288065 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues. ### Discussion of Biases The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio) ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{Zurich Open Repository and Archive:dataset, title = {Software Applications User Reviews}, authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo; Panichella, Sebastiano}, year={2017} } ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.

aqua_rat

--- annotations_creators: - crowdsourced language_creators: - crowdsourced - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: aqua-rat pretty_name: Algebra Question Answering with Rationales dataset_info: - config_name: raw features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 42333259 num_examples: 97467 - name: test num_bytes: 116779 num_examples: 254 - name: validation num_bytes: 118636 num_examples: 254 download_size: 47833135 dataset_size: 42568674 - config_name: tokenized features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 46493843 num_examples: 97467 - name: test num_bytes: 126283 num_examples: 254 - name: validation num_bytes: 128873 num_examples: 254 download_size: 52003894 dataset_size: 46748999 --- # Dataset Card for AQUA-RAT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA) - **Repository:** [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA) - **Paper:** [https://arxiv.org/pdf/1705.04146.pdf](https://arxiv.org/pdf/1705.04146.pdf) ### Dataset Summary A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question. ### Supported Tasks and Leaderboards ### Languages en ## Dataset Structure ### Data Instances ``` { "question": "A grocery sells a bag of ice for $1.25, and makes 20% profit. If it sells 500 bags of ice, how much total profit does it make?", "options": ["A)125", "B)150", "C)225", "D)250", "E)275"], "rationale": "Profit per bag = 1.25 * 0.20 = 0.25\nTotal profit = 500 * 0.25 = 125\nAnswer is A.", "correct": "A" } ``` ### Data Fields - `question` : (str) A natural language definition of the problem to solve - `options` : (list(str)) 5 possible options (A, B, C, D and E), among which one is correct - `rationale` : (str) A natural language description of the solution to the problem - `correct` : (str) The correct option ### Data Splits | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | Examples | 97467 | 254 | 254 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ### Citation Information ``` @article{ling2017program, title={Program induction by rationale generation: Learning to solve and explain algebraic word problems}, author={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil}, journal={ACL}, year={2017} } ``` ### Contributions Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset.

aquamuse

--- annotations_creators: - crowdsourced - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|natural_questions - extended|other-Common-Crawl - original task_categories: - other - question-answering - text2text-generation task_ids: - abstractive-qa - extractive-qa paperswithcode_id: aquamuse pretty_name: AQuaMuSe tags: - query-based-multi-document-summarization dataset_info: - config_name: abstractive features: - name: query dtype: string - name: input_urls sequence: string - name: target dtype: string splits: - name: train num_bytes: 6434909 num_examples: 6253 - name: test num_bytes: 843181 num_examples: 811 - name: validation num_bytes: 689109 num_examples: 661 download_size: 7755161 dataset_size: 7967199 - config_name: extractive features: - name: query dtype: string - name: input_urls sequence: string - name: target dtype: string splits: - name: train num_bytes: 6434909 num_examples: 6253 - name: test num_bytes: 843181 num_examples: 811 - name: validation num_bytes: 689109 num_examples: 661 download_size: 7755161 dataset_size: 7967199 --- # Dataset Card for AQuaMuSe ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/google-research-datasets/aquamuse - **Repository:** https://github.com/google-research-datasets/aquamuse - **Paper:** https://arxiv.org/pdf/2010.12694.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl) This dataset contains versions of automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf). ### Supported Tasks and Leaderboards - **Abstractive** and **Extractive** query-based multi-document summarization - Question Answering ### Languages en : English ## Dataset Structure ### Data Instances - `input_urls`: a `list` of `string` features. - `query`: a `string` feature. - `target`: a `string` feature Example: ``` { 'input_urls': ['https://boxofficebuz.com/person/19653-charles-michael-davis'], 'query': 'who is the actor that plays marcel on the originals', 'target': "In February 2013, it was announced that Davis was cast in a lead role on The CW's new show The Originals, a spinoff of The Vampire Diaries, centered on the Original Family as they move to New Orleans, where Davis' character (a vampire named Marcel) currently rules." } ``` ### Data Fields - `input_urls`: a `list` of `string` features. - List of URLs to input documents pointing to [Common Crawl](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available) to be summarized. - Dependencies: Documents URLs references the [Common Crawl June 2017 Archive](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available). - `query`: a `string` feature. - Input query to be used as summarization context. This is derived from [Natural Questions](https://ai.google.com/research/NaturalQuestions/) user queries. - `target`: a `string` feature - Summarization target, derived from [Natural Questions](https://ai.google.com/research/NaturalQuestions/) long answers. ### Data Splits - This dataset has two high-level configurations `abstractive` and `extractive` - Each configuration has the data splits of `train`, `dev` and `test` - The original format of the data was in [TFrecords](https://www.tensorflow.org/tutorials/load_data/tfrecord), which has been parsed to the format as specified in [Data Instances](#data-instances) ## Dataset Creation ### Curation Rationale The dataset is automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf). ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset curator is [sayalikulkarni](https://github.com/google-research-datasets/aquamuse/commits?author=sayalikulkarni), who is the contributor for the official GitHub repository for this dataset and also one of the authors of this dataset’s paper. As the account handles of other authors are not available currently who were also part of the curation of this dataset, the authors of the paper are mentioned here as follows, Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, and Eugene Ie. ### Licensing Information [More Information Needed] ### Citation Information @misc{kulkarni2020aquamuse, title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset.

ar_cov19

--- annotations_creators: - no-annotation language_creators: - found language: - ar multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: arcov-19 pretty_name: ArCOV19 tags: - data-mining dataset_info: features: - name: tweetID dtype: int64 config_name: ar_cov19 splits: - name: train num_bytes: 8724728 num_examples: 1090591 download_size: 54902390 dataset_size: 8724728 --- # Dataset Card for ArCOV19 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://gitlab.com/bigirqu/ArCOV-19 - **Paper:** [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://arxiv.org/abs/2004.05861) - **Leaderboard:** [More Information Needed] - **Point of Contact:** [Fatima Haouari](mailto:[email protected]) ### Dataset Summary ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes over 1M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and-liked). The propagation networks include both retweets and conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world. In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Arabic ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields tweet_id: the Twitter assigned ID for the tweet object. ### Data Splits [More Information Needed] ## Dataset Creation The dataset collection approach is presented in the following paper: [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://arxiv.org/abs/2004.05861) ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations No annotation was provided with the dataset. #### Annotation process No annotation was provided with the dataset. #### Who are the annotators? No annotation was provided with the dataset. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators **Team:** [bigIR](https://sites.google.com/view/bigir) from Qatar University ([@bigIR_group](https://twitter.com/bigIR_group)) - [Fatima Haouari](mailto:[email protected]) - [Maram Hasanain](mailto:[email protected]) - [Reem Suwaileh](mailto:[email protected]) - [Dr. Tamer Elsayed](mailto:[email protected]) ### Licensing Information [More Information Needed] ### Citation Information @article{haouari2020arcov19, title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks}, author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed}, journal={arXiv preprint arXiv:2004.05861}, year={2020} } ### Contributions Thanks to [@Fatima-Haouari](https://github.com/Fatima-Haouari) for adding this dataset.

ar_res_reviews

--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: ArRestReviews dataset_info: features: - name: polarity dtype: class_label: names: '0': negative '1': positive - name: text dtype: string - name: restaurant_id dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 3617097 num_examples: 8364 download_size: 3503230 dataset_size: 3617097 --- # Dataset Card for ArRestReviews ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces) - **Repository:** [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces) - **Paper:** [ Building Large Arabic Multi-domain Resources for Sentiment Analysis](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces/blob/master/Paper%20-%20Building%20Large%20Arabic%20Multi-domain%20Resources%20for%20Sentiment%20Analysis.pdf) - **Point of Contact:** [hady elsahar]([email protected]) ### Dataset Summary Dataset of 8364 restaurant reviews from qaym.com in Arabic for sentiment analysis ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A typical data point comprises of the following: - "polarity": which is a string value of either 0 or 1 indicating the sentiment around the review - "text": is the review plain text of a restaurant in Arabic - "restaurant_id": the restaurant ID on the website - "user_id": the user ID on the website example: ``` { 'polarity': 0, # negative 'restaurant_id': '1412', 'text': 'عادي جدا مامن زود', 'user_id': '21294' } ``` ### Data Fields - "polarity": is a string value of either 0 or 1 indicating the sentiment around the review - "text": is the review plain text of a restaurant in Arabic - "restaurant_id": the restaurant ID on the website (string) - "user_id": the user ID on the website (string) ### Data Splits The dataset is not split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Contains 8364 restaurant reviews from qaym.com #### Who are the source language producers? From tweeter. ### Annotations The polarity field provides a label of 1 or -1 pertaining to the sentiment of the review #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Discussion of Social Impact and Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{10.1007/978-3-319-18117-2_2, author="ElSahar, Hady and El-Beltagy, Samhaa R.", editor="Gelbukh, Alexander", title="Building Large Arabic Multi-domain Resources for Sentiment Analysis", booktitle="Computational Linguistics and Intelligent Text Processing", year="2015", publisher="Springer International Publishing", address="Cham", pages="23--34", isbn="978-3-319-18117-2" } ### Contributions Thanks to [@abdulelahsm](https://github.com/abdulelahsm) for adding this dataset.

ar_sarcasm

--- annotations_creators: - no-annotation language_creators: - found language: - ar license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-semeval_2017 - extended|other-astd task_categories: - text-classification task_ids: - sentiment-classification pretty_name: ArSarcasm tags: - sarcasm-detection dataset_info: features: - name: dialect dtype: class_label: names: '0': egypt '1': gulf '2': levant '3': magreb '4': msa - name: sarcasm dtype: class_label: names: '0': non-sarcastic '1': sarcastic - name: sentiment dtype: class_label: names: '0': negative '1': neutral '2': positive - name: original_sentiment dtype: class_label: names: '0': negative '1': neutral '2': positive - name: tweet dtype: string - name: source dtype: string splits: - name: train num_bytes: 1829167 num_examples: 8437 - name: test num_bytes: 458218 num_examples: 2110 download_size: 750717 dataset_size: 2287385 --- # Dataset Card for ArSarcasm ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub](https://github.com/iabufarha/ArSarcasm) - **Paper:** https://www.aclweb.org/anthology/2020.osact-1.5/ ### Dataset Summary ArSarcasm is a new Arabic sarcasm detection dataset. The dataset was created using previously available Arabic sentiment analysis datasets ([SemEval 2017](https://www.aclweb.org/anthology/S17-2088.pdf) and [ASTD](https://www.aclweb.org/anthology/D15-1299.pdf)) and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic. For more details, please check the paper [From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://www.aclweb.org/anthology/2020.osact-1.5/) ### Supported Tasks and Leaderboards You can get more information about an Arabic sarcasm tasks and leaderboard [here](https://sites.google.com/view/ar-sarcasm-sentiment-detection/). ### Languages Arabic (multiple dialects) ## Dataset Structure ### Data Instances ```javascript {'dialect': 1, 'original_sentiment': 0, 'sarcasm': 0, 'sentiment': 0, 'source': 'semeval', 'tweet': 'نصيحه ما عمرك اتنزل لعبة سوبر ماريو مش زي ما كنّا متوقعين الله يرحم ايامات السيقا والفاميلي #SuperMarioRun'} ``` ### Data Fields - tweet: the original tweet text - sarcasm: 0 for non-sarcastic, 1 for sarcastic - sentiment: 0 for negative, 1 for neutral, 2 for positive - original_sentiment: 0 for negative, 1 for neutral, 2 for positive - source: the original source of tweet: SemEval or ASTD - dialect: 0 for Egypt, 1 for Gulf, 2 for Levant, 3 for Magreb, 4 for Modern Standard Arabic (MSA) ### Data Splits The training set contains 8,437 tweets, while the test set contains 2,110 tweets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them. #### Who are the source language producers? SemEval 2017 and ASTD ### Annotations #### Annotation process For the annotation process, we used Figure-Eight crowdsourcing platform. Our main objective was to annotate the data for sarcasm detection, but due to the challenges imposed by dialectal variations, we decided to add the annotation for dialects. We also include a new annotation for sentiment labels in order to have a glimpse of the variability and subjectivity between different annotators. Thus, the annotators were asked to provide three labels for each tweet as the following: - Sarcasm: sarcastic or non-sarcastic. - Sentiment: positive, negative or neutral. - Dialect: Egyptian, Gulf, Levantine, Maghrebi or Modern Standard Arabic (MSA). #### Who are the annotators? Figure-Eight crowdsourcing platform ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators - Ibrahim Abu-Farha - Walid Magdy ### Licensing Information MIT ### Citation Information ``` @inproceedings{abu-farha-magdy-2020-arabic, title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset", author = "Abu Farha, Ibrahim and Magdy, Walid", booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resource Association", url = "https://www.aclweb.org/anthology/2020.osact-1.5", pages = "32--39", language = "English", ISBN = "979-10-95546-51-1", } ``` ### Contributions Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset.

arabic_billion_words

--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Arabic Billion Words configs: - Alittihad - Almasryalyoum - Almustaqbal - Alqabas - Echoroukonline - Ryiadh - Sabanews - SaudiYoum - Techreen - Youm7 dataset_info: - config_name: Alittihad features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1601790302 num_examples: 349342 download_size: 348259999 dataset_size: 1601790302 - config_name: Almasryalyoum features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1056197870 num_examples: 291723 download_size: 242604438 dataset_size: 1056197870 - config_name: Almustaqbal features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1545659336 num_examples: 446873 download_size: 350826797 dataset_size: 1545659336 - config_name: Alqabas features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 2631729746 num_examples: 817274 download_size: 595274646 dataset_size: 2631729746 - config_name: Echoroukonline features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 464386206 num_examples: 139732 download_size: 108184378 dataset_size: 464386206 - config_name: Ryiadh features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 3101294859 num_examples: 858188 download_size: 691264971 dataset_size: 3101294859 - config_name: Sabanews features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 198019614 num_examples: 92149 download_size: 38214558 dataset_size: 198019614 - config_name: SaudiYoum features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 2723291416 num_examples: 888068 download_size: 605537923 dataset_size: 2723291416 - config_name: Techreen features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 1103458209 num_examples: 314597 download_size: 252976781 dataset_size: 1103458209 - config_name: Youm7 features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 3004689464 num_examples: 1172136 download_size: 617708074 dataset_size: 3004689464 --- # Dataset Card for Arabic Billion Words Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus - **Repository:** - **Paper:** https://arxiv.org/pdf/1611.04033 - **Leaderboard:** - **Point of Contact:**[Ibrahim Abu El-Khair]([email protected]) ### Dataset Summary Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles. It contains over a billion and a half words in total, out of which, there are about three million unique words. The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two mark-up languages, namely: SGML, and XML. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Arabic ## Dataset Structure ### Data Instances This is an example of the "Almasryalyoum" configuration subset: ```python { "url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300", "head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد", "date": "19/5/2007", "text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره. وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري. يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري. وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""", } ``` ### Data Fields The data fields are: - "url": string, original url of the article, - "head_line": string, headline of the article, - "date": string, date of the article, - "text": string, text content of the article, ### Data Splits There is only one "training" split for all configuration subsets, containing the following number of examples: | | Number of examples | |:---------------|-------------------:| | Alittihad | 349342 | | Almasryalyoum | 291723 | | Almustaqbal | 446873 | | Alqabas | 817274 | | Echoroukonline | 139732 | | Ryiadh | 858188 | | Sabanews | 92149 | | SaudiYoum | 888068 | | Techreen | 314597 | | Youm7 | 1172136 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @article{el20161, title={1.5 billion words arabic corpus}, author={El-Khair, Ibrahim Abu}, journal={arXiv preprint arXiv:1611.04033}, year={2016} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

arabic_pos_dialect

--- annotations_creators: - expert-generated language_creators: - found language: - ar license: - apache-2.0 multilinguality: - multilingual size_categories: - n<1K source_datasets: - extended task_categories: - token-classification task_ids: - part-of-speech paperswithcode_id: null pretty_name: Arabic POS Dialect dataset_info: - config_name: egy features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 269657 num_examples: 350 download_size: 1043655 dataset_size: 269657 - config_name: lev features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 263130 num_examples: 350 download_size: 1043655 dataset_size: 263130 - config_name: glf features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 239911 num_examples: 350 download_size: 1043655 dataset_size: 239911 - config_name: mgr features: - name: fold dtype: int32 - name: subfold dtype: string - name: words sequence: string - name: segments sequence: string - name: pos_tags sequence: string splits: - name: train num_bytes: 245745 num_examples: 350 download_size: 1043655 dataset_size: 245745 --- # Dataset Card for Arabic POS Dialect ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://alt.qcri.org/resources/da_resources/ - **Repository:** https://github.com/qcri/dialectal_arabic_resources - **Paper:** http://www.lrec-conf.org/proceedings/lrec2018/pdf/562.pdf - **Contacts:** - Ahmed Abdelali < aabdelali @ hbku dot edu dot qa > - Kareem Darwish < kdarwish @ hbku dot edu dot qa > - Hamdy Mubarak < hmubarak @ hbku dot edu dot qa > ### Dataset Summary This dataset was created to support part of speech (POS) tagging in dialects of Arabic. It contains sets of 350 manually segmented and POS tagged tweets for each of four dialects: Egyptian, Levantine, Gulf, and Maghrebi. ### Supported Tasks and Leaderboards The dataset can be used to train a model for Arabic token segmentation and part of speech tagging in Arabic dialects. Success on this task is typically measured by achieving a high accuracy over a held out dataset. Darwish et al. (2018) train a CRF model across all four dialects and achieve an average accuracy of 89.3%. ### Languages The BCP-47 code is ar-Arab. The dataset consists of four dialects of Arabic, Egyptian (EGY), Levantine (LEV), Gulf (GLF), and Maghrebi (MGR), written in Arabic script. ## Dataset Structure ### Data Instances Below is a partial example from the Egyptian set: ``` - `Fold`: 4 - `SubFold`: A - `Word`: [ليه, لما, تحب, حد, من, قلبك, ...] - `Segmentation`: [ليه, لما, تحب, حد, من, قلب+ك, ...] - `POS`: [PART, PART, V, NOUN, PREP, NOUN+PRON, ...] ``` ### Data Fields The `fold` and the `subfold` fields refer to the crossfold validation splits used by Darwish et al., which can be generated using this [script](https://github.com/qcri/dialectal_arabic_resources/blob/master/generate_splits.sh). - `fold`: An int32 indicating which fold the instance was in for the crossfold validation - `subfold`: A string, either 'A' or 'B', indicating which subfold the instance was in for the crossfold validation - `words`: A sequence of strings of the unsegmented token - `segments`: A sequence of strings consisting of the segments of the word separated by '+' if there is more than one segment - `pos_tags`: A sequence of strings of the part of speech tags of the segments separated by '+' if there is more than one segment The POS tags consist of a set developed by [Darwish et al. (2017)](https://www.aclweb.org/anthology/W17-1316.pdf) for Modern Standard Arabic (MSA) plus an additional 6 tags (2 dialect-specific tags and 4 tweet-specific tags). | Tag | Purpose | Description | | ----- | ------ | ----- | | ADV | MSA | Adverb | | ADJ | MSA | Adjective | | CONJ | MSA | Conjunction | | DET | MSA | Determiner | | NOUN | MSA | Noun | | NSUFF | MSA | Noun suffix | | NUM | MSA | Number | | PART | MSA | Particle | | PREP | MSA | Preposition | | PRON | MSA | Pronoun | | PUNC | MSA | Preposition | | V | MSA | Verb | | ABBREV | MSA | Abbreviation | | CASE | MSA | Alef of tanween fatha | | JUS | MSA | Jussification attached to verbs | | VSUFF | MSA | Verb Suffix | | FOREIGN | MSA | Non-Arabic as well as non-MSA words | | FUR_PART | MSA | Future particle "s" prefix and "swf" | | PROG_PART | Dialect | Progressive particle | | NEG_PART | Dialect | Negation particle | | HASH | Tweet | Hashtag | | EMOT | Tweet | Emoticon/Emoji | | MENTION | Tweet | Mention | | URL | Tweet | URL | ### Data Splits The dataset is split by dialect. | Dialect | Tweets | Words | | ----- | ------ | ----- | | Egyptian (EGY) | 350 | 7481 | | Levantine (LEV) | 350 | 7221 | | Gulf (GLF) | 350 | 6767 | | Maghrebi (MGR) | 350 | 6400 | ## Dataset Creation ### Curation Rationale This dataset was created to address the lack of computational resources available for dialects of Arabic. These dialects are typically used in speech, while written forms of the language are typically in Modern Standard Arabic. Social media, however, has provided a venue for people to use dialects in written format. ### Source Data This dataset builds off of the work of [Eldesouki et al. (2017)](https://arxiv.org/pdf/1708.05891.pdf) and [Samih et al. (2017b)](https://www.aclweb.org/anthology/K17-1043.pdf) who originally collected the tweets. #### Initial Data Collection and Normalization They started with 175 million Arabic tweets returned by the Twitter API using the query "lang:ar" in March 2014. They then filtered this set using author-identified locations and tokens that are unique to each dialect. Finally, they had native speakers of each dialect select 350 tweets that were heavily accented. #### Who are the source language producers? The source language producers are people who posted on Twitter in Arabic using dialectal words from countries where the dialects of interest were spoken, as identified in [Mubarak and Darwish (2014)](https://www.aclweb.org/anthology/W14-3601.pdf). ### Annotations #### Annotation process The segmentation guidelines are available at https://alt.qcri.org/resources1/da_resources/seg-guidelines.pdf. The tagging guidelines are not provided, but Darwish at al. note that there were multiple rounds of quality control and revision. #### Who are the annotators? The POS tags were annotated by native speakers of each dialect. Further information is not known. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset Darwish et al find that the accuracy on the Maghrebi dataset suffered the most when the training set was from another dialect, and conversely training on Maghrebi yielded the worst results for all the other dialects. They suggest that Egyptian, Levantine, and Gulf may be more similar to each other and Maghrebi the most dissimilar to all of them. They also find that training on Modern Standard Arabic (MSA) and testing on dialects yielded significantly lower results compared to training on dialects and testing on MSA. This suggests that dialectal variation should be a significant consideration for future work in Arabic NLP applications, particularly when working with social media text. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Kareem Darwish, Hamdy Mubarak, Mohamed Eldesouki and Ahmed Abdelali with the Qatar Computing Research Institute (QCRI), Younes Samih and Laura Kallmeyer with the University of Dusseldorf, Randah Alharbi and Walid Magdy with the University of Edinburgh, and Mohammed Attia with Google. No funding information was included. ### Licensing Information This dataset is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy and Laura Kallmeyer (2018) Multi-Dialect Arabic POS Tagging: A CRF Approach. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 7-12, 2018. Miyazaki, Japan. ``` @InProceedings{DARWISH18.562, author = {Kareem Darwish ,Hamdy Mubarak ,Ahmed Abdelali ,Mohamed Eldesouki ,Younes Samih ,Randah Alharbi ,Mohammed Attia ,Walid Magdy and Laura Kallmeyer}, title = {Multi-Dialect Arabic POS Tagging: A CRF Approach}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-00-9}, language = {english} } ``` ### Contributions Thanks to [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.

arabic_speech_corpus

--- pretty_name: Arabic Speech Corpus annotations_creators: - expert-generated language_creators: - crowdsourced language: - ar license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: arabic-speech-corpus size_categories: - 1K<n<10K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] train-eval-index: - config: clean task: automatic-speech-recognition task_id: speech_recognition splits: train_split: train eval_split: test col_mapping: file: path text: text metrics: - type: wer name: WER - type: cer name: CER dataset_info: features: - name: file dtype: string - name: text dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: phonetic dtype: string - name: orthographic dtype: string config_name: clean splits: - name: train num_bytes: 1002365 num_examples: 1813 - name: test num_bytes: 65784 num_examples: 100 download_size: 1192302846 dataset_size: 1068149 --- # Dataset Card for Arabic Speech Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Arabic Speech Corpus](http://en.arabicspeechcorpus.com/) - **Repository:** [Needs More Information] - **Paper:** [Modern standard Arabic phonetics for speech synthesis](http://en.arabicspeechcorpus.com/Nawar%20Halabi%20PhD%20Thesis%20Revised.pdf) - **Leaderboard:** [Paperswithcode Leaderboard][Needs More Information] - **Point of Contact:** [Nawar Halabi](mailto:[email protected]) ### Dataset Summary This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton. The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The audio is in Arabic. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. An example from the dataset is: ``` { 'file': '/Users/username/.cache/huggingface/datasets/downloads/extracted/baebe85e2cb67579f6f88e7117a87888c1ace390f4f14cb6c3e585c517ad9db0/arabic-speech-corpus/wav/ARA NORM 0002.wav', 'audio': {'path': '/Users/username/.cache/huggingface/datasets/downloads/extracted/baebe85e2cb67579f6f88e7117a87888c1ace390f4f14cb6c3e585c517ad9db0/arabic-speech-corpus/wav/ARA NORM 0002.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000}, 'orthographic': 'waraj~aHa Alt~aqoriyru Al~a*iy >aEad~ahu maEohadu >aboHaA^i haDabapi Alt~ibiti fiy Alo>akaAdiymiy~api AlS~iyniy~api liloEuluwmi - >ano tasotamir~a darajaAtu AloHaraArapi wamusotawayaAtu Alr~uTuwbapi fiy Alo<irotifaAEi TawaAla ha*aA Aloqarono', 'phonetic': "sil w a r a' jj A H a tt A q r ii0' r u0 ll a * i0 < a E a' dd a h u0 m a' E h a d u0 < a b H aa' ^ i0 h A D A' b a t i0 tt i1' b t i0 f i0 l < a k aa d ii0 m ii0' y a t i0 SS II0 n ii0' y a t i0 l u0 l E u0 l uu0' m i0 sil < a' n t a s t a m i0' rr a d a r a j aa' t u0 l H a r aa' r a t i0 w a m u0 s t a w a y aa' t u0 rr U0 T UU0' b a t i0 f i0 l Ah i0 r t i0 f aa' E i0 T A' w A l a h aa' * a l q A' r n sil", 'text': '\ufeffwaraj~aHa Alt~aqoriyru Al~aTHiy >aEad~ahu maEohadu >aboHaA^i haDabapi Alt~ibiti fiy Alo>akaAdiymiy~api AlS~iyniy~api liloEuluwmi - >ano tasotamir~a darajaAtu AloHaraArapi wamusotawayaAtu Alr~uTuwbapi fiy Alo<irotifaAEi TawaAla haTHaA Aloqarono' } ``` ### Data Fields - file: A path to the downloaded audio file in .wav format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - phonetic: the transcription in phonentics format. - orthographic: the transcriptions written in orthographic format. ### Data Splits | | Train | Test | | ----- | ----- | ---- | | dataset | 1813 | 100 | ## Dataset Creation ### Curation Rationale The corpus was created with Speech Synthesis as the main application in mind. Although it has been used as part of a larger corpus for speech recognition and speech denoising. Here are some explanations why the corpus was built the way it is: * Corpus size: Budget limitations and the research goal resulted in the decision not to gather more data. The goal was to show that high quality speech synthesis is possible with smaller corpora. * Phonetic diversity: Just like with many corpora, the phonetic diversity was acheived using greedy methods. Start with a core set of utterances and add more utterances which contribute to adding more phonetic diversity the most iterativly. The measure of diversity is based on the diphone frequency. * Content: News, sports, economics, fully diacritised content from the internet was gathered. The choice of utterances was random to avoid copyright issues. Because of corpus size, acheiving diversity of content type was difficult and was not the goal. * Non-sense utterances: The corpus contains a large set of utterances that are generated computationally to compensate for the diphones missing in the main part of the corpus. The usefullness of non-sense utterances was not proven in the PhD thesis. * The talent: The voice talent had a Syrian dialect from Damascus and spoke in formal Arabic. Please refer to [PhD thesis](#Citation-Information) for more detailed information. ### Source Data #### Initial Data Collection and Normalization News, sports, economics, fully diacritised content from the internet was gathered. The choice of utterances was random to avoid copyright issues. Because of corpus size, acheiving diversity of content type was difficult and was not the goal. We were restricted to content which was fully diacritised to make the annotation process easier. Just like with many corpora, the phonetic diversity was acheived using greedy methods. Start with a core set of utterances and add more utterances which contribute to adding more phonetic diversity the most iterativly. The measure of diversity is based on the diphone frequency. Please refer to [PhD thesis](#Citation-Information). #### Who are the source language producers? Please refer to [PhD thesis](#Citation-Information). ### Annotations #### Annotation process Three annotators aligned audio with phonemes with the help of HTK forced alignment. They worked on overlapping parts as well to assess annotator agreement and the quality of the annotations. The entire corpus was checked by human annotators. Please refer to [PhD thesis](#Citation-Information). #### Who are the annotators? Nawar Halabi and two anonymous Arabic language teachers. ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. The voice talent agreed in writing for their voice to be used in speech technologies as long as they stay anonymous. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio by Nawar Halabi. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @phdthesis{halabi2016modern, title={Modern standard Arabic phonetics for speech synthesis}, author={Halabi, Nawar}, year={2016}, school={University of Southampton} } ``` ### Contributions This dataset was created by: * Nawar Halabi [@nawarhalabi](https://github.com/nawarhalabi) main creator and annotator. * Two anonymous Arabic langauge teachers as annotators. * One anonymous voice talent. * Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset.

arcd

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar language_bcp47: - ar-SA license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: arcd pretty_name: ARCD dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: plain_text splits: - name: train num_bytes: 811064 num_examples: 693 - name: validation num_bytes: 885648 num_examples: 702 download_size: 1942399 dataset_size: 1696712 --- # Dataset Card for "arcd" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/husseinmozannar/SOQAL/tree/master/data](https://github.com/husseinmozannar/SOQAL/tree/master/data) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.94 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 3.64 MB ### Dataset Summary Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 1.94 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 3.64 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": "{\"answer_start\": [34], \"text\": [\"صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر،\"]}...", "context": "\"حمزة بن عبد المطلب الهاشمي القرشي صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر، وهو خير أع...", "id": "621723207492", "question": "من هو حمزة بن عبد المطلب؟", "title": "حمزة بن عبد المطلب" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | validation | | ---------- | ----: | ---------: | | plain_text | 693 | 702 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@tayciryahmed](https://github.com/tayciryahmed) for adding this dataset.

arsentd_lev

--- annotations_creators: - crowdsourced language_creators: - found language: - apc - ajp license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - topic-classification paperswithcode_id: arsentd-lev pretty_name: ArSenTD-LEV dataset_info: features: - name: Tweet dtype: string - name: Country dtype: class_label: names: '0': jordan '1': lebanon '2': syria '3': palestine - name: Topic dtype: string - name: Sentiment dtype: class_label: names: '0': negative '1': neutral '2': positive '3': very_negative '4': very_positive - name: Sentiment_Expression dtype: class_label: names: '0': explicit '1': implicit '2': none - name: Sentiment_Target dtype: string splits: - name: train num_bytes: 1233980 num_examples: 4000 download_size: 392666 dataset_size: 1233980 --- # Dataset Card for ArSenTD-LEV ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [ArSenTD-LEV homepage](http://oma-project.com/) - **Paper:** [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830) ### Dataset Summary The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria. ### Supported Tasks and Leaderboards Sentriment analysis ### Languages Arabic Levantine Dualect ## Dataset Structure ### Data Instances {'Country': 0, 'Sentiment': 3, 'Sentiment_Expression': 0, 'Sentiment_Target': 'هاي سوالف عصابات ارهابية', 'Topic': 'politics', 'Tweet': 'ثلاث تفجيرات في #كركوك الحصيلة قتيل و 16 جريح بدأت اكلاوات كركوك كانت امان قبل دخول القوات العراقية ، هاي سوالف عصابات ارهابية'} ### Data Fields `Tweet`: the text content of the tweet \ `Country`: the country from which the tweet was collected ('jordan', 'lebanon', 'syria', 'palestine')\ `Topic`: the topic being discussed in the tweet (personal, politics, religion, sports, entertainment and others) \ `Sentiment`: the overall sentiment expressed in the tweet (very_negative, negative, neutral, positive and very_positive) \ `Sentiment_Expression`: the way how the sentiment was expressed: explicit, implicit, or none (the latter when sentiment is neutral) \ `Sentiment_Target`: the segment from the tweet to which sentiment is expressed. If sentiment is neutral, this field takes the 'none' value. ### Data Splits No standard splits are provided ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Make sure to read and agree to the [license](http://oma-project.com/ArSenL/ArSenTD_Lev_Intro) ### Citation Information ``` @article{baly2019arsentd, title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets}, author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir}, journal={arXiv preprint arXiv:1906.01830}, year={2019} } ``` ### Contributions Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset.

art

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - multiple-choice - text-classification task_ids: - natural-language-inference paperswithcode_id: art-dataset pretty_name: Abductive Reasoning in narrative Text tags: - abductive-natural-language-inference dataset_info: features: - name: observation_1 dtype: string - name: observation_2 dtype: string - name: hypothesis_1 dtype: string - name: hypothesis_2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' config_name: anli splits: - name: validation num_bytes: 312314 num_examples: 1532 - name: train num_bytes: 34046304 num_examples: 169654 download_size: 5118294 dataset_size: 34358618 --- # Dataset Card for "art" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://leaderboard.allenai.org/anli/submissions/get-started](https://leaderboard.allenai.org/anli/submissions/get-started) - **Repository:** https://github.com/allenai/abductive-commonsense-reasoning - **Paper:** [Abductive Commonsense Reasoning](https://arxiv.org/abs/1908.05739) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 5.12 MB - **Size of the generated dataset:** 34.36 MB - **Total amount of disk used:** 39.48 MB ### Dataset Summary ART consists of over 20k commonsense narrative contexts and 200k explanations. The Abductive Natural Language Inference Dataset from AI2. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### anli - **Size of downloaded dataset files:** 5.12 MB - **Size of the generated dataset:** 34.36 MB - **Total amount of disk used:** 39.48 MB An example of 'train' looks as follows. ``` { "hypothesis_1": "Chad's car had all sorts of other problems besides alignment.", "hypothesis_2": "Chad's car had all sorts of benefits other than being sexy.", "label": 1, "observation_1": "Chad went to get the wheel alignment measured on his car.", "observation_2": "The mechanic provided a working alignment with new body work." } ``` ### Data Fields The data fields are the same among all splits. #### anli - `observation_1`: a `string` feature. - `observation_2`: a `string` feature. - `hypothesis_1`: a `string` feature. - `hypothesis_2`: a `string` feature. - `label`: a classification label, with possible values including `0` (0), `1` (1), `2` (2). ### Data Splits |name|train |validation| |----|-----:|---------:| |anli|169654| 1532| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Bhagavatula2020Abductive, title={Abductive Commonsense Reasoning}, author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi}, booktitle={International Conference on Learning Representations}, year={2020}, url={https://openreview.net/forum?id=Byg1v1HKDB} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

arxiv_dataset

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation - summarization - text-retrieval task_ids: - document-retrieval - entity-linking-retrieval - explanation-generation - fact-checking-retrieval - text-simplification paperswithcode_id: null pretty_name: arXiv Dataset dataset_info: features: - name: id dtype: string - name: submitter dtype: string - name: authors dtype: string - name: title dtype: string - name: comments dtype: string - name: journal-ref dtype: string - name: doi dtype: string - name: report-no dtype: string - name: categories dtype: string - name: license dtype: string - name: abstract dtype: string - name: update_date dtype: string splits: - name: train num_bytes: 2246545603 num_examples: 1796911 download_size: 0 dataset_size: 2246545603 --- # Dataset Card for arXiv Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Kaggle arXiv Dataset Homepage](https://www.kaggle.com/Cornell-University/arxiv) - **Repository:** - **Paper:** [On the Use of ArXiv as a Dataset](https://arxiv.org/abs/1905.00075) - **Leaderboard:** - **Point of Contact:** [Matt Bierbaum](mailto:[email protected]) ### Dataset Summary A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is English ## Dataset Structure ### Data Instances This dataset is a mirror of the original ArXiv data. Because the full dataset is rather large (1.1TB and growing), this dataset provides only a metadata file in the json format. An example is given below ``` {'id': '0704.0002', 'submitter': 'Louis Theran', 'authors': 'Ileana Streinu and Louis Theran', 'title': 'Sparsity-certifying Graph Decompositions', 'comments': 'To appear in Graphs and Combinatorics', 'journal-ref': None, 'doi': None, 'report-no': None, 'categories': 'math.CO cs.CG', 'license': 'http://arxiv.org/licenses/nonexclusive-distrib/1.0/', 'abstract': ' We describe a new algorithm, the $(k,\\ell)$-pebble game with colors, and use\nit obtain a characterization of the family of $(k,\\ell)$-sparse graphs and\nalgorithmic solutions to a family of problems concerning tree decompositions of\ngraphs. Special instances of sparse graphs appear in rigidity theory and have\nreceived increased attention in recent years. In particular, our colored\npebbles generalize and strengthen the previous results of Lee and Streinu and\ngive a new proof of the Tutte-Nash-Williams characterization of arboricity. We\nalso present a new decomposition that certifies sparsity based on the\n$(k,\\ell)$-pebble game with colors. Our work also exposes connections between\npebble game algorithms and previous sparse graph algorithms by Gabow, Gabow and\nWestermann and Hendrickson.\n', 'update_date': '2008-12-13'} ``` ### Data Fields - `id`: ArXiv ID (can be used to access the paper) - `submitter`: Who submitted the paper - `authors`: Authors of the paper - `title`: Title of the paper - `comments`: Additional info, such as number of pages and figures - `journal-ref`: Information about the journal the paper was published in - `doi`: [Digital Object Identifier](https://www.doi.org) - `report-no`: Report Number - `abstract`: The abstract of the paper - `categories`: Categories / tags in the ArXiv system ### Data Splits The data was not splited. ## Dataset Creation ### Curation Rationale For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming depth. In these times of unique global challenges, efficient extraction of insights from data is essential. To help make the arXiv more accessible, a free, open pipeline on Kaggle to the machine-readable arXiv dataset: a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more is presented to empower new use cases that can lead to the exploration of richer machine learning techniques that combine multi-modal features towards applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces. ### Source Data This data is based on arXiv papers. [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations This dataset contains no annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The original data is maintained by [ArXiv](https://arxiv.org/) ### Licensing Information The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/) ### Citation Information ``` @misc{clement2019arxiv, title={On the Use of ArXiv as a Dataset}, author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi}, year={2019}, eprint={1905.00075}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` ### Contributions Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset.

ascent_kb

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: ascentkb pretty_name: Ascent KB tags: - knowledge-base dataset_info: - config_name: canonical features: - name: arg1 dtype: string - name: rel dtype: string - name: arg2 dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2976697816 num_examples: 8904060 download_size: 710727536 dataset_size: 2976697816 - config_name: open features: - name: subject dtype: string - name: predicate dtype: string - name: object dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2882678298 num_examples: 8904060 download_size: 710727536 dataset_size: 2882678298 --- # Dataset Card for Ascent KB ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://ascent.mpi-inf.mpg.de/ - **Repository:** https://github.com/phongnt570/ascent - **Paper:** https://arxiv.org/abs/2011.00905 - **Point of Contact:** http://tuan-phong.com ### Dataset Summary This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). The focus of this dataset is on everyday concepts such as *elephant*, *car*, *laptop*, etc. The current version of Ascent KB (v1.0.0) is approximately **19 times larger than ConceptNet** (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded). For more details, take a look at [the research paper](https://arxiv.org/abs/2011.00905) and [the website](https://ascent.mpi-inf.mpg.de). ### Supported Tasks and Leaderboards The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances There are two configurations available for this dataset: 1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>` assertions where the relations (`rel`) were mapped to [ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations) with slight modifications: - Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`. - All `/r/HasA` relations were replaced with `/r/HasAspect`. This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020) schema, although they grouped all `/r/HasA` and `/r/HasProperty` into `/r/HasProperty`. - The `/r/UsedFor` relation was replaced with `/r/ObjectUse` which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.). This is also taken from ATOMIC-2020. 2. `open`: This part contains open assertions of the form `<subject ; predicate ; object>` extracted directly from web contents. This is the original form of the `canonical` triples. In both configurations, each assertion is equipped with extra information including: a set of semantic `facets` (e.g., *LOCATION*, *TEMPORAL*, etc.), its `support` (i.e., number of occurrences), and a list of `source_sentences`. An example row in the `canonical` configuration: ```JSON { "arg1": "elephant", "rel": "/r/HasProperty", "arg2": "intelligent", "support": 15, "facets": [ { "value": "extremely", "type": "DEGREE", "support": 11 } ], "source_sentences": [ { "text": "Elephants are extremely intelligent animals.", "source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/" }, { "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.", "source": "https://www.elephantsforafrica.org/elephant-facts/" } ] } ``` ### Data Fields - **For `canonical` configuration** - `arg1`: the first argument to the relationship, e.g., *elephant* - `rel`: the canonical relation, e.g., */r/HasProperty* - `arg2`: the second argument to the relationship, e.g., *intelligence* - `support`: the number of occurrences of the assertion, e.g., *15* - `facets`: an array of semantic facets, each contains - `value`: facet value, e.g., *extremely* - `type`: facet type, e.g., *DEGREE* - `support`: the number of occurrences of the facet, e.g., *11* - `source_sentences`: an array of source sentences from which the assertion was extracted, each contains - `text`: the raw text of the sentence - `source`: the URL to its parent document - **For `open` configuration** - The fields of this configuration are the same as the `canonical` configuration's, except that the (`arg1`, `rel`, `arg2`) fields are replaced with the (`subject`, `predicate`, `object`) fields which are free text phrases extracted directly from the source sentences using an Open Information Extraction (OpenIE) tool. ### Data Splits There are no splits. All data points come to a default split called `train`. ## Dataset Creation ### Curation Rationale The commonsense knowledge base was created to assist in development of robust and reliable AI. ### Source Data #### Initial Data Collection and Normalization Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions. The assertions were then grouped into semantically equivalent clusters. Take a look at the research paper for more details. #### Who are the source language producers? Web users. ### Annotations #### Annotation process None. #### Who are the annotators? None. ### Personal and Sensitive Information Unknown. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The knowledge base has been developed by researchers at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments. ### Licensing Information [The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @InProceedings{nguyen2021www, title={Advanced Semantics for Commonsense Knowledge Extraction}, author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, year={2021}, booktitle={The Web Conference 2021}, } ``` ### Contributions Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.

aslg_pc12

--- annotations_creators: - crowdsourced - expert-generated language: - ase - en language_creators: - found license: - cc-by-nc-4.0 multilinguality: - translation pretty_name: English-ASL Gloss Parallel Corpus 2012 size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: aslg-pc12 dataset_info: features: - name: gloss dtype: string - name: text dtype: string splits: - name: train num_bytes: 13497111 num_examples: 87710 download_size: 12773431 dataset_size: 13497111 --- # Dataset Card for "aslg_pc12" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://achrafothman.net/site/asl-smt/](https://achrafothman.net/site/asl-smt/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 12.77 MB - **Size of the generated dataset:** 13.50 MB - **Total amount of disk used:** 26.27 MB ### Dataset Summary Synthetic English-ASL Gloss Parallel Corpus 2012 ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 12.77 MB - **Size of the generated dataset:** 13.50 MB - **Total amount of disk used:** 26.27 MB An example of 'train' looks as follows. ``` { "gloss": "WRITE STATEMENT AND DESC-ORAL QUESTION TABLE SEE MINUTE\n", "text": "written statements and oral questions tabling see minutes\n" } ``` ### Data Fields The data fields are the same among all splits. #### default - `gloss`: a `string` feature. - `text`: a `string` feature. ### Data Splits | name |train| |-------|----:| |default|87710| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{othman2012english, title={English-asl gloss parallel corpus 2012: Aslg-pc12}, author={Othman, Achraf and Jemni, Mohamed}, booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC}, year={2012} } ``` ### Contributions Thanks to [@AmitMY](https://github.com/AmitMY) for adding this dataset.

asnq

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - extended|natural_questions task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: asnq pretty_name: Answer Sentence Natural Questions (ASNQ) dataset_info: features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': neg '1': pos - name: sentence_in_long_answer dtype: bool - name: short_answer_in_sentence dtype: bool splits: - name: train num_bytes: 3656881376 num_examples: 20377568 - name: validation num_bytes: 168005155 num_examples: 930062 download_size: 3563857920 dataset_size: 3824886531 --- # Dataset Card for "asnq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq](https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://arxiv.org/abs/1911.04118) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 3.56 GB - **Size of the generated dataset:** 3.82 GB - **Total amount of disk used:** 7.39 GB ### Dataset Summary ASNQ is a dataset for answer sentence selection derived from Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). Each example contains a question, candidate sentence, label indicating whether or not the sentence answers the question, and two additional features -- sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence. For more details please see https://arxiv.org/abs/1911.04118 and https://research.google/pubs/pub47761/ ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 3.56 GB - **Size of the generated dataset:** 3.82 GB - **Total amount of disk used:** 7.39 GB An example of 'validation' looks as follows. ``` { "label": 0, "question": "when did somewhere over the rainbow come out", "sentence": "In films and TV shows ( edit ) In the film Third Finger , Left Hand ( 1940 ) with Myrna Loy , Melvyn Douglas , and Raymond Walburn , the tune played throughout the film in short sequences .", "sentence_in_long_answer": false, "short_answer_in_sentence": false } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `sentence`: a `string` feature. - `label`: a classification label, with possible values including `neg` (0), `pos` (1). - `sentence_in_long_answer`: a `bool` feature. - `short_answer_in_sentence`: a `bool` feature. ### Data Splits | name | train |validation| |-------|-------:|---------:| |default|20377568| 930062| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License: https://github.com/alexa/wqa_tanda/blob/master/LICENSE ### Citation Information ``` @article{Garg_2020, title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection}, volume={34}, ISSN={2159-5399}, url={http://dx.doi.org/10.1609/AAAI.V34I05.6282}, DOI={10.1609/aaai.v34i05.6282}, number={05}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, publisher={Association for the Advancement of Artificial Intelligence (AAAI)}, author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro}, year={2020}, month={Apr}, pages={7780–7788} } ``` ### Contributions Thanks to [@mkserge](https://github.com/mkserge) for adding this dataset.

asset

--- annotations_creators: - machine-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original - extended|other-turkcorpus task_categories: - text-classification - text2text-generation task_ids: - text-simplification paperswithcode_id: asset pretty_name: ASSET configs: - ratings - simplification tags: - simplification-evaluation dataset_info: - config_name: simplification features: - name: original dtype: string - name: simplifications sequence: string splits: - name: validation num_bytes: 2303496 num_examples: 2000 - name: test num_bytes: 411031 num_examples: 359 download_size: 3639353 dataset_size: 2714527 - config_name: ratings features: - name: original dtype: string - name: simplification dtype: string - name: original_sentence_id dtype: int32 - name: aspect dtype: class_label: names: '0': meaning '1': fluency '2': simplicity - name: worker_id dtype: int32 - name: rating dtype: int32 splits: - name: full num_bytes: 1036853 num_examples: 4500 download_size: 3639353 dataset_size: 1036853 --- # Dataset Card for ASSET ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [ASSET Github repository](https://github.com/facebookresearch/asset) - **Paper:** [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://www.aclweb.org/anthology/2020.acl-main.424/) - **Point of Contact:** [Louis Martin]([email protected]) ### Dataset Summary [ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus]( https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations. ### Supported Tasks and Leaderboards The dataset supports the evaluation of `text-simplification` systems. Success in this tasks is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf). ### Languages The text in this dataset is in English (`en`). ## Dataset Structure ### Data Instances - `simplification` configuration: an instance consists in an original sentence and 10 possible reference simplifications. - `ratings` configuration: a data instance consists in an original sentence, a simplification obtained by an automated system, and a judgment of quality along one of three axes by a crowd worker. ### Data Fields - `original`: an original sentence from the source datasets - `simplifications`: in the `simplification` config, a set of reference simplifications produced by crowd workers. - `simplification`: in the `ratings` config, a simplification of the original obtained by an automated system - `aspect`: in the `ratings` config, the aspect on which the simplification is evaluated, one of `meaning`, `fluency`, `simplicity` - `rating`: a quality rating between 0 and 100 ### Data Splits ASSET does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) for training. Each input sentence has 10 associated reference simplified sentences. The statistics of ASSET are given below. | | Dev | Test | Total | | ----- | ------ | ---- | ----- | | Input Sentences | 2000 | 359 | 2359 | | Reference Simplifications | 20000 | 3590 | 23590 | The test and validation sets are the same as those of TurkCorpus. The split was random. There are 19.04 tokens per reference on average (lower than 21.29 and 25.49 for TurkCorpus and HSplit, respectively). Most (17,245) of the referece sentences do not involve sentence splitting. ## Dataset Creation ### Curation Rationale ASSET was created in order to improve the evaluation of sentence simplification. It uses the same input sentences as the [TurkCorpus]( https://github.com/cocoxu/simplification/) dataset from [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). The 2,359 input sentences of TurkCorpus are a sample of "standard" (not simple) sentences from the [Parallel Wikipedia Simplification (PWKP)](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/sentence_simplification/simple_complex_sentence_pairs/index.en.jsp) dataset [(Zhu et al., 2010)](https://www.aclweb.org/anthology/C10-1152.pdf), which come from the August 22, 2009 version of Wikipedia. The sentences of TurkCorpus were chosen to be of similar length [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). No further information is provided on the sampling strategy. The TurkCorpus dataset was developed in order to overcome some of the problems with sentence pairs from Standard and Simple Wikipedia: a large fraction of sentences were misaligned, or not actually simpler [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). However, TurkCorpus mainly focused on *lexical paraphrasing*, and so cannot be used to evaluate simplifications involving *compression* (deletion) or *sentence splitting*. HSplit [(Sulem et al., 2018)](https://www.aclweb.org/anthology/D18-1081.pdf), on the other hand, can only be used to evaluate sentence splitting. The reference sentences in ASSET include a wider variety of sentence rewriting strategies, combining splitting, compression and paraphrasing. Annotators were given examples of each kind of transformation individually, as well as all three transformations used at once, but were allowed to decide which transformations to use for any given sentence. An example illustrating the differences between TurkCorpus, HSplit and ASSET is given below: > **Original:** He settled in London, devoting himself chiefly to practical teaching. > > **TurkCorpus:** He rooted in London, devoting himself mainly to practical teaching. > > **HSplit:** He settled in London. He devoted himself chiefly to practical teaching. > > **ASSET:** He lived in London. He was a teacher. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The input sentences are from English Wikipedia (August 22, 2009 version). No demographic information is available for the writers of these sentences. However, most Wikipedia editors are male (Lam, 2011; Graells-Garrido, 2015), which has an impact on the topics covered (see also [the Wikipedia page on Wikipedia gender bias](https://en.wikipedia.org/wiki/Gender_bias_on_Wikipedia)). In addition, Wikipedia editors are mostly white, young, and from the Northern Hemisphere [(Wikipedia: Systemic bias)](https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias). Reference sentences were written by 42 workers on Amazon Mechanical Turk (AMT). The requirements for being an annotator were: - Passing a Qualification Test (appropriately simplifying sentences). Out of 100 workers, 42 passed the test. - Being a resident of the United States, United Kingdom or Canada. - Having a HIT approval rate over 95%, and over 1000 HITs approved. No other demographic or compensation information is provided in the ASSET paper. ### Annotations #### Annotation process The instructions given to the annotators are available [here](https://github.com/facebookresearch/asset/blob/master/crowdsourcing/AMT_AnnotationInstructions.pdf). #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases (Schmahl et al., 2020) and racial biases (Adams et al., 2019). > Adams, Julia, Hannah Brückner, and Cambria Naslund. "Who Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test”." Socius 5 (2019): 2378023118823946. > Schmahl, Katja Geertruida, et al. "Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings." Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science. 2020. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators ASSET was developed by researchers at the University of Sheffield, Inria, Facebook AI Research, and Imperial College London. The work was partly supported by Benoît Sagot's chair in the PRAIRIE institute, funded by the French National Research Agency (ANR) as part of the "Investissements d’avenir" program (reference ANR-19-P3IA-0001). ### Licensing Information [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) ### Citation Information ``` @inproceedings{alva-manchego-etal-2020-asset, title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations", author = "Alva-Manchego, Fernando and Martin, Louis and Bordes, Antoine and Scarton, Carolina and Sagot, Beno{\^\i}t and Specia, Lucia", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.424", pages = "4668--4679", } ``` This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r). ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.

assin

--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - natural-language-inference - semantic-similarity-scoring paperswithcode_id: assin pretty_name: ASSIN dataset_info: - config_name: full features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 986507 num_examples: 5000 - name: test num_bytes: 767312 num_examples: 4000 - name: validation num_bytes: 196829 num_examples: 1000 download_size: 749735 dataset_size: 1950648 - config_name: ptpt features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 523002 num_examples: 2500 - name: test num_bytes: 392888 num_examples: 2000 - name: validation num_bytes: 105626 num_examples: 500 download_size: 749735 dataset_size: 1021516 - config_name: ptbr features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 463513 num_examples: 2500 - name: test num_bytes: 374432 num_examples: 2000 - name: validation num_bytes: 91211 num_examples: 500 download_size: 749735 dataset_size: 929156 --- # Dataset Card for ASSIN ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [ASSIN homepage](http://nilc.icmc.usp.br/assin/) - **Repository:** [ASSIN repository](http://nilc.icmc.usp.br/assin/) - **Paper:** [ASSIN: Evaluation of Semantic Similarity and Textual Inference](http://propor2016.di.fc.ul.pt/wp-content/uploads/2015/10/assin-overview.pdf) - **Point of Contact:** [Erick Rocha Fonseca](mailto:[email protected]) ### Dataset Summary The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal and Brazil, respectively. To create the corpus, the authors started by collecting a set of news articles describing the same event (one news article from Google News Portugal and another from Google News Brazil) from Google News. Then, they employed Latent Dirichlet Allocation (LDA) models to retrieve pairs of similar sentences between sets of news articles that were grouped together around the same topic. For that, two LDA models were trained (for EP and for BP) on external and large-scale collections of unannotated news articles from Portuguese and Brazilian news providers, respectively. Then, the authors defined a lower and upper threshold for the sentence similarity score of the retrieved pairs of sentences, taking into account that high similarity scores correspond to sentences that contain almost the same content (paraphrase candidates), and low similarity scores correspond to sentences that are very different in content from each other (no-relation candidates). From the collection of pairs of sentences obtained at this stage, the authors performed some manual grammatical corrections and discarded some of the pairs wrongly retrieved. Furthermore, from a preliminary analysis made to the retrieved sentence pairs the authors noticed that the number of contradictions retrieved during the previous stage was very low. Additionally, they also noticed that event though paraphrases are not very frequent, they occur with some frequency in news articles. Consequently, in contrast with the majority of the currently available corpora for other languages, which consider as labels “neutral”, “entailment” and “contradiction” for the task of RTE, the authors of the ASSIN corpus decided to use as labels “none”, “entailment” and “paraphrase”. Finally, the manual annotation of pairs of sentences was performed by human annotators. At least four annotators were randomly selected to annotate each pair of sentences, which is done in two steps: (i) assigning a semantic similarity label (a score between 1 and 5, from unrelated to very similar); and (ii) providing an entailment label (one sentence entails the other, sentences are paraphrases, or no relation). Sentence pairs where at least three annotators do not agree on the entailment label were considered controversial and thus discarded from the gold standard annotations. The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese (ptbr) and half in European Portuguese (ptpt). Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the ASSIN dataset looks as follows: ``` { "entailment_judgment": 0, "hypothesis": "André Gomes entra em campo quatro meses depois de uma lesão na perna esquerda o ter afastado dos relvados.", "premise": "Relembre-se que o atleta estava afastado dos relvados desde maio, altura em que contraiu uma lesão na perna esquerda.", "relatedness_score": 3.5, "sentence_pair_id": 1 } ``` ### Data Fields - `sentence_pair_id`: a `int64` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `relatedness_score`: a `float32` feature. - `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`, `PARAPHRASE`. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | | Train | Val | Test | | ----- | ------ | ----- | ---- | | full | 5000 | 1000 | 4000 | | ptbr | 2500 | 500 | 2000 | | ptpt | 2500 | 500 | 2000 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.

assin2

--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - natural-language-inference - semantic-similarity-scoring paperswithcode_id: assin2 pretty_name: ASSIN 2 dataset_info: features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT splits: - name: train num_bytes: 864816 num_examples: 6500 - name: test num_bytes: 339580 num_examples: 2448 - name: validation num_bytes: 66895 num_examples: 500 download_size: 2113646 dataset_size: 1271291 --- # Dataset Card for ASSIN 2 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [ASSIN 2 homepage](https://sites.google.com/view/assin2) - **Repository:** [ASSIN 2 repository](https://sites.google.com/view/assin2) - **Paper:** [The ASSIN 2 shared task: a quick overview](https://drive.google.com/file/d/1ft1VU6xiVm-N58dfAp6FHWjQ4IvcXgqp/view) - **Point of Contact:** [Livy Real](mailto:[email protected]) ### Dataset Summary The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1. The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese, annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same annotation. All data were manually annotated. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the ASSIN 2 dataset looks as follows: ``` { "entailment_judgment": 1, "hypothesis": "Uma criança está segurando uma pistola de água", "premise": "Uma criança risonha está segurando uma pistola de água e sendo espirrada com água", "relatedness_score": 4.5, "sentence_pair_id": 1 } ``` ### Data Fields - `sentence_pair_id`: a `int64` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `relatedness_score`: a `float32` feature. - `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | Train | Val | Test | | ------ | ----- | ---- | | 6500 | 500 | 2448 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.

atomic

--- pretty_name: ATOMIC annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: atomic tags: - common-sense-if-then-reasoning dataset_info: features: - name: event dtype: string - name: oEffect sequence: string - name: oReact sequence: string - name: oWant sequence: string - name: xAttr sequence: string - name: xEffect sequence: string - name: xIntent sequence: string - name: xNeed sequence: string - name: xReact sequence: string - name: xWant sequence: string - name: prefix sequence: string - name: split dtype: string config_name: atomic splits: - name: train num_bytes: 32441878 num_examples: 202271 - name: test num_bytes: 3995624 num_examples: 24856 - name: validation num_bytes: 3629768 num_examples: 22620 download_size: 19083782 dataset_size: 40067270 --- # Dataset Card for An Atlas of Machine Commonsense for If-Then Reasoning - Atomic Common Sense Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://homes.cs.washington.edu/~msap/atomic/ - **Repository:** https://homes.cs.washington.edu/~msap/atomic/ - **Paper:** Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith & Yejin Choi (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI ### Dataset Summary This dataset provides the template sentences and relationships defined in the ATOMIC common sense dataset. There are three splits - train, test, and dev. From the authors. Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us ([email protected]) if you have any concerns. For more information, see: https://homes.cs.washington.edu/~msap/atomic/ ### Supported Tasks and Leaderboards [More Information Needed] ### Languages en ## Dataset Structure ### Data Instances Here is one example from the atomic dataset: `` {'event': "PersonX uses PersonX's ___ to obtain", 'oEffect': [], 'oReact': ['annoyed', 'angry', 'worried'], 'oWant': [], 'prefix': ['uses', 'obtain'], 'split': 'trn', 'xAttr': [], 'xEffect': [], 'xIntent': ['to have an advantage', 'to fulfill a desire', 'to get out of trouble'], 'xNeed': [], 'xReact': ['pleased', 'smug', 'excited'], 'xWant': []} `` ### Data Fields Notes from the authors: * event: just a string representation of the event. * oEffect,oReact,oWant,xAttr,xEffect,xIntent,xNeed,xReact,xWant: annotations for each of the dimensions, stored in a json-dumped string. Note: "none" means the worker explicitly responded with the empty response, whereas [] means the worker did not annotate this dimension. * prefix: json-dumped string that represents the prefix of content words (used to make a better trn/dev/tst split). * split: string rep of which split the event belongs to. ### Data Splits The atomic dataset has three splits: test, train and dev of the form: ## Dataset Creation ### Curation Rationale This dataset was gathered and created over to assist in common sense reasoning. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created by the University of Washington using crowd sourced data #### Who are the source language producers? The Atomic authors and crowd source. ### Annotations #### Annotation process Human annotations directed by forms. #### Who are the annotators? Human annotations. ### Personal and Sensitive Information Unkown, but likely none. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines understand common sense. ### Discussion of Biases Since the data is human annotators, there is likely to be baised. From the authors: Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us ([email protected]) if you have any concerns. ### Other Known Limitations While there are many relationships, the data is quite sparse. Also, each item of the dataset could be expanded into multiple sentences along the vsrious dimensions, oEffect, oRect, etc. For example, given event: "PersonX uses PersonX's ___ to obtain" and dimension oReact: "annoyed", this could be transformed into an entry: "PersonX uses PersonX's ___ to obtain => PersonY is annoyed" ## Additional Information ### Dataset Curators The authors of Aotmic at The University of Washington ### Licensing Information The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/ ### Citation Information @article{Sap2019ATOMICAA, title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning}, author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi}, journal={ArXiv}, year={2019}, volume={abs/1811.00146} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.

autshumato

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - tn - ts - zu license: - cc-by-2.5 multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: autshumato configs: - autshumato-en-tn - autshumato-en-ts - autshumato-en-ts-manual - autshumato-en-zu - autshumato-tn - autshumato-ts dataset_info: - config_name: autshumato-en-tn features: - name: translation dtype: translation: languages: - en - tn splits: - name: train num_bytes: 28826392 num_examples: 159000 download_size: 9458762 dataset_size: 28826392 - config_name: autshumato-en-zu features: - name: translation dtype: translation: languages: - en - zu splits: - name: train num_bytes: 7188970 num_examples: 35489 download_size: 2068891 dataset_size: 7188970 - config_name: autshumato-en-ts features: - name: translation dtype: translation: languages: - en - ts splits: - name: train num_bytes: 50803849 num_examples: 450000 download_size: 15145915 dataset_size: 50803849 - config_name: autshumato-en-ts-manual features: - name: translation dtype: translation: languages: - en - ts splits: - name: train num_bytes: 10408757 num_examples: 92396 download_size: 2876924 dataset_size: 10408757 - config_name: autshumato-tn features: - name: text dtype: string splits: - name: train num_bytes: 5132267 num_examples: 38206 download_size: 1599029 dataset_size: 5132267 - config_name: autshumato-ts features: - name: text dtype: string splits: - name: train num_bytes: 3399674 num_examples: 58398 download_size: 974488 dataset_size: 3399674 --- # Dataset Card for autshumato ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://repo.sadilar.org/handle/20.500.12185/7/discover]() - **Repository:** []() - **Paper:** []() - **Leaderboard:** []() - **Point of Contact:** []() ### Dataset Summary Multilingual information access is stipulated in the South African constitution. In practise, this is hampered by a lack of resources and capacity to perform the large volumes of translation work required to realise multilingual information access. One of the aims of the Autshumato project is to develop machine translation systems for three South African languages pairs. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators [More Information Needed] ### Licensing Information ### Citation Information ``` @article{groenewald2010processing, title={Processing parallel text corpora for three South African language pairs in the Autshumato project}, author={Groenewald, Hendrik J and du Plooy, Liza}, journal={AfLaT 2010}, pages={27}, year={2010} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.

facebook/babi_qa

--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - cc-by-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: babi-1 pretty_name: BabiQa configs: - en-10k-qa1 - en-10k-qa10 - en-10k-qa11 - en-10k-qa12 - en-10k-qa13 - en-10k-qa14 - en-10k-qa15 - en-10k-qa16 - en-10k-qa17 - en-10k-qa18 - en-10k-qa19 - en-10k-qa2 - en-10k-qa20 - en-10k-qa3 - en-10k-qa4 - en-10k-qa5 - en-10k-qa6 - en-10k-qa7 - en-10k-qa8 - en-10k-qa9 - en-qa1 - en-qa10 - en-qa11 - en-qa12 - en-qa13 - en-qa14 - en-qa15 - en-qa16 - en-qa17 - en-qa18 - en-qa19 - en-qa2 - en-qa20 - en-qa3 - en-qa4 - en-qa5 - en-qa6 - en-qa7 - en-qa8 - en-qa9 - en-valid-10k-qa1 - en-valid-10k-qa10 - en-valid-10k-qa11 - en-valid-10k-qa12 - en-valid-10k-qa13 - en-valid-10k-qa14 - en-valid-10k-qa15 - en-valid-10k-qa16 - en-valid-10k-qa17 - en-valid-10k-qa18 - en-valid-10k-qa19 - en-valid-10k-qa2 - en-valid-10k-qa20 - en-valid-10k-qa3 - en-valid-10k-qa4 - en-valid-10k-qa5 - en-valid-10k-qa6 - en-valid-10k-qa7 - en-valid-10k-qa8 - en-valid-10k-qa9 - en-valid-qa1 - en-valid-qa10 - en-valid-qa11 - en-valid-qa12 - en-valid-qa13 - en-valid-qa14 - en-valid-qa15 - en-valid-qa16 - en-valid-qa17 - en-valid-qa18 - en-valid-qa19 - en-valid-qa2 - en-valid-qa20 - en-valid-qa3 - en-valid-qa4 - en-valid-qa5 - en-valid-qa6 - en-valid-qa7 - en-valid-qa8 - en-valid-qa9 - hn-10k-qa1 - hn-10k-qa10 - hn-10k-qa11 - hn-10k-qa12 - hn-10k-qa13 - hn-10k-qa14 - hn-10k-qa15 - hn-10k-qa16 - hn-10k-qa17 - hn-10k-qa18 - hn-10k-qa19 - hn-10k-qa2 - hn-10k-qa20 - hn-10k-qa3 - hn-10k-qa4 - hn-10k-qa5 - hn-10k-qa6 - hn-10k-qa7 - hn-10k-qa8 - hn-10k-qa9 - hn-qa1 - hn-qa10 - hn-qa11 - hn-qa12 - hn-qa13 - hn-qa14 - hn-qa15 - hn-qa16 - hn-qa17 - hn-qa18 - hn-qa19 - hn-qa2 - hn-qa20 - hn-qa3 - hn-qa4 - hn-qa5 - hn-qa6 - hn-qa7 - hn-qa8 - hn-qa9 - shuffled-10k-qa1 - shuffled-10k-qa10 - shuffled-10k-qa11 - shuffled-10k-qa12 - shuffled-10k-qa13 - shuffled-10k-qa14 - shuffled-10k-qa15 - shuffled-10k-qa16 - shuffled-10k-qa17 - shuffled-10k-qa18 - shuffled-10k-qa19 - shuffled-10k-qa2 - shuffled-10k-qa20 - shuffled-10k-qa3 - shuffled-10k-qa4 - shuffled-10k-qa5 - shuffled-10k-qa6 - shuffled-10k-qa7 - shuffled-10k-qa8 - shuffled-10k-qa9 - shuffled-qa1 - shuffled-qa10 - shuffled-qa11 - shuffled-qa12 - shuffled-qa13 - shuffled-qa14 - shuffled-qa15 - shuffled-qa16 - shuffled-qa17 - shuffled-qa18 - shuffled-qa19 - shuffled-qa2 - shuffled-qa20 - shuffled-qa3 - shuffled-qa4 - shuffled-qa5 - shuffled-qa6 - shuffled-qa7 - shuffled-qa8 - shuffled-qa9 tags: - chained-qa dataset_info: - config_name: en-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165386 num_examples: 200 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 330903 - config_name: en-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 302888 num_examples: 200 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 609519 - config_name: en-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 887756 num_examples: 200 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 1770943 - config_name: en-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 205510 num_examples: 1000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 410944 - config_name: en-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 337349 num_examples: 200 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 687806 - config_name: en-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 173053 num_examples: 200 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 345302 - config_name: en-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 224778 num_examples: 200 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 440290 - config_name: en-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 212517 num_examples: 200 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 428761 - config_name: en-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 168350 num_examples: 200 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 336598 - config_name: en-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170257 num_examples: 200 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 340929 - config_name: en-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 178560 num_examples: 200 - name: test num_bytes: 178840 num_examples: 200 download_size: 15719851 dataset_size: 357400 - config_name: en-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 185600 num_examples: 200 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 371129 - config_name: en-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 190556 num_examples: 200 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 381040 - config_name: en-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 234355 num_examples: 200 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 467559 - config_name: en-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 163728 num_examples: 250 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 327537 - config_name: en-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 456374 num_examples: 1000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 912622 - config_name: en-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 103636 num_examples: 125 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 207254 - config_name: en-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 162875 num_examples: 198 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 324141 - config_name: en-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 404536 num_examples: 1000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 809025 - config_name: en-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 115812 num_examples: 94 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 231675 - config_name: hn-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 168605 num_examples: 200 - name: test num_bytes: 168572 num_examples: 200 download_size: 15719851 dataset_size: 337177 - config_name: hn-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 296391 num_examples: 200 - name: test num_bytes: 288429 num_examples: 200 download_size: 15719851 dataset_size: 584820 - config_name: hn-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 842184 num_examples: 167 - name: test num_bytes: 808460 num_examples: 167 download_size: 15719851 dataset_size: 1650644 - config_name: hn-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 231303 num_examples: 1000 - name: test num_bytes: 231230 num_examples: 1000 download_size: 15719851 dataset_size: 462533 - config_name: hn-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 320859 num_examples: 200 - name: test num_bytes: 315396 num_examples: 200 download_size: 15719851 dataset_size: 636255 - config_name: hn-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170796 num_examples: 200 - name: test num_bytes: 171360 num_examples: 200 download_size: 15719851 dataset_size: 342156 - config_name: hn-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 206981 num_examples: 200 - name: test num_bytes: 208080 num_examples: 200 download_size: 15719851 dataset_size: 415061 - config_name: hn-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 211584 num_examples: 200 - name: test num_bytes: 222232 num_examples: 200 download_size: 15719851 dataset_size: 433816 - config_name: hn-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 187718 num_examples: 200 - name: test num_bytes: 187341 num_examples: 200 download_size: 15719851 dataset_size: 375059 - config_name: hn-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 183583 num_examples: 200 - name: test num_bytes: 182932 num_examples: 200 download_size: 15719851 dataset_size: 366515 - config_name: hn-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 179698 num_examples: 200 - name: test num_bytes: 180461 num_examples: 200 download_size: 15719851 dataset_size: 360159 - config_name: hn-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 187731 num_examples: 200 - name: test num_bytes: 187954 num_examples: 200 download_size: 15719851 dataset_size: 375685 - config_name: hn-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 191395 num_examples: 125 - name: test num_bytes: 191747 num_examples: 125 download_size: 15719851 dataset_size: 383142 - config_name: hn-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 240659 num_examples: 200 - name: test num_bytes: 240436 num_examples: 200 download_size: 15719851 dataset_size: 481095 - config_name: hn-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170358 num_examples: 250 - name: test num_bytes: 170259 num_examples: 250 download_size: 15719851 dataset_size: 340617 - config_name: hn-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 523093 num_examples: 1000 - name: test num_bytes: 523032 num_examples: 1000 download_size: 15719851 dataset_size: 1046125 - config_name: hn-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 103878 num_examples: 125 - name: test num_bytes: 104061 num_examples: 125 download_size: 15719851 dataset_size: 207939 - config_name: hn-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 173056 num_examples: 198 - name: test num_bytes: 176824 num_examples: 198 download_size: 15719851 dataset_size: 349880 - config_name: hn-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 470225 num_examples: 1000 - name: test num_bytes: 470479 num_examples: 1000 download_size: 15719851 dataset_size: 940704 - config_name: hn-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 115021 num_examples: 93 - name: test num_bytes: 115088 num_examples: 94 download_size: 15719851 dataset_size: 230109 - config_name: en-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1654288 num_examples: 2000 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 1819805 - config_name: en-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3062580 num_examples: 2000 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 3369211 - config_name: en-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8921215 num_examples: 2000 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 9804402 - config_name: en-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2055105 num_examples: 10000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 2260539 - config_name: en-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3592157 num_examples: 2000 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 3942614 - config_name: en-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1726716 num_examples: 2000 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 1898965 - config_name: en-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2228087 num_examples: 2000 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 2443599 - config_name: en-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2141880 num_examples: 2000 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 2358124 - config_name: en-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1681213 num_examples: 2000 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 1849461 - config_name: en-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1707675 num_examples: 2000 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 1878347 - config_name: en-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1786179 num_examples: 2000 - name: test num_bytes: 178840 num_examples: 200 download_size: 15719851 dataset_size: 1965019 - config_name: en-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1854745 num_examples: 2000 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 2040274 - config_name: en-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1903149 num_examples: 2000 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 2093633 - config_name: en-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2321511 num_examples: 2000 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 2554715 - config_name: en-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1637398 num_examples: 2500 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 1801207 - config_name: en-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4562844 num_examples: 10000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 5019092 - config_name: en-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1034333 num_examples: 1250 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 1137951 - config_name: en-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1641650 num_examples: 1978 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 1802916 - config_name: en-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4045086 num_examples: 10000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 4449575 - config_name: en-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1157351 num_examples: 933 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 1273214 - config_name: en-valid-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 148887 num_examples: 180 - name: test num_bytes: 165517 num_examples: 200 - name: validation num_bytes: 16539 num_examples: 20 download_size: 15719851 dataset_size: 330943 - config_name: en-valid-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 275106 num_examples: 180 - name: test num_bytes: 306631 num_examples: 200 - name: validation num_bytes: 27822 num_examples: 20 download_size: 15719851 dataset_size: 609559 - config_name: en-valid-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 794565 num_examples: 180 - name: test num_bytes: 883187 num_examples: 200 - name: validation num_bytes: 93231 num_examples: 20 download_size: 15719851 dataset_size: 1770983 - config_name: en-valid-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 184992 num_examples: 900 - name: test num_bytes: 205434 num_examples: 1000 - name: validation num_bytes: 20558 num_examples: 100 download_size: 15719851 dataset_size: 410984 - config_name: en-valid-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 305472 num_examples: 180 - name: test num_bytes: 350457 num_examples: 200 - name: validation num_bytes: 31917 num_examples: 20 download_size: 15719851 dataset_size: 687846 - config_name: en-valid-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 155845 num_examples: 180 - name: test num_bytes: 172249 num_examples: 200 - name: validation num_bytes: 17248 num_examples: 20 download_size: 15719851 dataset_size: 345342 - config_name: en-valid-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 203642 num_examples: 180 - name: test num_bytes: 215512 num_examples: 200 - name: validation num_bytes: 21176 num_examples: 20 download_size: 15719851 dataset_size: 440330 - config_name: en-valid-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 191599 num_examples: 180 - name: test num_bytes: 216244 num_examples: 200 - name: validation num_bytes: 20958 num_examples: 20 download_size: 15719851 dataset_size: 428801 - config_name: en-valid-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 151458 num_examples: 180 - name: test num_bytes: 168248 num_examples: 200 - name: validation num_bytes: 16932 num_examples: 20 download_size: 15719851 dataset_size: 336638 - config_name: en-valid-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 153240 num_examples: 180 - name: test num_bytes: 170672 num_examples: 200 - name: validation num_bytes: 17057 num_examples: 20 download_size: 15719851 dataset_size: 340969 - config_name: en-valid-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 160701 num_examples: 180 - name: test num_bytes: 178840 num_examples: 200 - name: validation num_bytes: 17899 num_examples: 20 download_size: 15719851 dataset_size: 357440 - config_name: en-valid-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 167031 num_examples: 180 - name: test num_bytes: 185529 num_examples: 200 - name: validation num_bytes: 18609 num_examples: 20 download_size: 15719851 dataset_size: 371169 - config_name: en-valid-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 171527 num_examples: 180 - name: test num_bytes: 190484 num_examples: 200 - name: validation num_bytes: 19069 num_examples: 20 download_size: 15719851 dataset_size: 381080 - config_name: en-valid-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 210650 num_examples: 180 - name: test num_bytes: 233204 num_examples: 200 - name: validation num_bytes: 23745 num_examples: 20 download_size: 15719851 dataset_size: 467599 - config_name: en-valid-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 147356 num_examples: 225 - name: test num_bytes: 163809 num_examples: 250 - name: validation num_bytes: 16412 num_examples: 25 download_size: 15719851 dataset_size: 327577 - config_name: en-valid-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 410711 num_examples: 900 - name: test num_bytes: 456248 num_examples: 1000 - name: validation num_bytes: 45703 num_examples: 100 download_size: 15719851 dataset_size: 912662 - config_name: en-valid-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 93596 num_examples: 113 - name: test num_bytes: 103618 num_examples: 125 - name: validation num_bytes: 10080 num_examples: 12 download_size: 15719851 dataset_size: 207294 - config_name: en-valid-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 147338 num_examples: 179 - name: test num_bytes: 161266 num_examples: 199 - name: validation num_bytes: 15577 num_examples: 19 download_size: 15719851 dataset_size: 324181 - config_name: en-valid-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 364090 num_examples: 900 - name: test num_bytes: 404489 num_examples: 1000 - name: validation num_bytes: 40486 num_examples: 100 download_size: 15719851 dataset_size: 809065 - config_name: en-valid-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 104706 num_examples: 85 - name: test num_bytes: 115863 num_examples: 93 - name: validation num_bytes: 11146 num_examples: 9 download_size: 15719851 dataset_size: 231715 - config_name: en-valid-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1488751 num_examples: 1800 - name: test num_bytes: 165517 num_examples: 200 - name: validation num_bytes: 165577 num_examples: 200 download_size: 15719851 dataset_size: 1819845 - config_name: en-valid-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2746462 num_examples: 1800 - name: test num_bytes: 306631 num_examples: 200 - name: validation num_bytes: 316158 num_examples: 200 download_size: 15719851 dataset_size: 3369251 - config_name: en-valid-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8021847 num_examples: 1800 - name: test num_bytes: 883187 num_examples: 200 - name: validation num_bytes: 899408 num_examples: 200 download_size: 15719851 dataset_size: 9804442 - config_name: en-valid-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1849497 num_examples: 9000 - name: test num_bytes: 205434 num_examples: 1000 - name: validation num_bytes: 205648 num_examples: 1000 download_size: 15719851 dataset_size: 2260579 - config_name: en-valid-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3234186 num_examples: 1800 - name: test num_bytes: 350457 num_examples: 200 - name: validation num_bytes: 358011 num_examples: 200 download_size: 15719851 dataset_size: 3942654 - config_name: en-valid-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1553957 num_examples: 1800 - name: test num_bytes: 172249 num_examples: 200 - name: validation num_bytes: 172799 num_examples: 200 download_size: 15719851 dataset_size: 1899005 - config_name: en-valid-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2003820 num_examples: 1800 - name: test num_bytes: 215512 num_examples: 200 - name: validation num_bytes: 224307 num_examples: 200 download_size: 15719851 dataset_size: 2443639 - config_name: en-valid-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1926339 num_examples: 1800 - name: test num_bytes: 216244 num_examples: 200 - name: validation num_bytes: 215581 num_examples: 200 download_size: 15719851 dataset_size: 2358164 - config_name: en-valid-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1512917 num_examples: 1800 - name: test num_bytes: 168248 num_examples: 200 - name: validation num_bytes: 168336 num_examples: 200 download_size: 15719851 dataset_size: 1849501 - config_name: en-valid-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1536416 num_examples: 1800 - name: test num_bytes: 170672 num_examples: 200 - name: validation num_bytes: 171299 num_examples: 200 download_size: 15719851 dataset_size: 1878387 - config_name: en-valid-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1607505 num_examples: 1800 - name: test num_bytes: 178840 num_examples: 200 - name: validation num_bytes: 178714 num_examples: 200 download_size: 15719851 dataset_size: 1965059 - config_name: en-valid-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1669198 num_examples: 1800 - name: test num_bytes: 185529 num_examples: 200 - name: validation num_bytes: 185587 num_examples: 200 download_size: 15719851 dataset_size: 2040314 - config_name: en-valid-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1712558 num_examples: 1800 - name: test num_bytes: 190484 num_examples: 200 - name: validation num_bytes: 190631 num_examples: 200 download_size: 15719851 dataset_size: 2093673 - config_name: en-valid-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2091491 num_examples: 1800 - name: test num_bytes: 233204 num_examples: 200 - name: validation num_bytes: 230060 num_examples: 200 download_size: 15719851 dataset_size: 2554755 - config_name: en-valid-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1473615 num_examples: 2250 - name: test num_bytes: 163809 num_examples: 250 - name: validation num_bytes: 163823 num_examples: 250 download_size: 15719851 dataset_size: 1801247 - config_name: en-valid-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4106444 num_examples: 9000 - name: test num_bytes: 456248 num_examples: 1000 - name: validation num_bytes: 456440 num_examples: 1000 download_size: 15719851 dataset_size: 5019132 - config_name: en-valid-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 930465 num_examples: 1125 - name: test num_bytes: 103618 num_examples: 125 - name: validation num_bytes: 103908 num_examples: 125 download_size: 15719851 dataset_size: 1137991 - config_name: en-valid-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1477467 num_examples: 1781 - name: test num_bytes: 161266 num_examples: 199 - name: validation num_bytes: 164223 num_examples: 197 download_size: 15719851 dataset_size: 1802956 - config_name: en-valid-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3640527 num_examples: 9000 - name: test num_bytes: 404489 num_examples: 1000 - name: validation num_bytes: 404599 num_examples: 1000 download_size: 15719851 dataset_size: 4449615 - config_name: en-valid-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1041856 num_examples: 840 - name: test num_bytes: 115863 num_examples: 93 - name: validation num_bytes: 115535 num_examples: 93 download_size: 15719851 dataset_size: 1273254 - config_name: hn-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1684003 num_examples: 2000 - name: test num_bytes: 168572 num_examples: 200 download_size: 15719851 dataset_size: 1852575 - config_name: hn-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2934642 num_examples: 2000 - name: test num_bytes: 288429 num_examples: 200 download_size: 15719851 dataset_size: 3223071 - config_name: hn-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8440008 num_examples: 1667 - name: test num_bytes: 808460 num_examples: 167 download_size: 15719851 dataset_size: 9248468 - config_name: hn-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2312075 num_examples: 10000 - name: test num_bytes: 231230 num_examples: 1000 download_size: 15719851 dataset_size: 2543305 - config_name: hn-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3301271 num_examples: 2000 - name: test num_bytes: 315396 num_examples: 200 download_size: 15719851 dataset_size: 3616667 - config_name: hn-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1703863 num_examples: 2000 - name: test num_bytes: 171360 num_examples: 200 download_size: 15719851 dataset_size: 1875223 - config_name: hn-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2091460 num_examples: 2000 - name: test num_bytes: 208080 num_examples: 200 download_size: 15719851 dataset_size: 2299540 - config_name: hn-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2178277 num_examples: 2000 - name: test num_bytes: 222232 num_examples: 200 download_size: 15719851 dataset_size: 2400509 - config_name: hn-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1874753 num_examples: 2000 - name: test num_bytes: 187341 num_examples: 200 download_size: 15719851 dataset_size: 2062094 - config_name: hn-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1830698 num_examples: 2000 - name: test num_bytes: 182932 num_examples: 200 download_size: 15719851 dataset_size: 2013630 - config_name: hn-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1798057 num_examples: 2000 - name: test num_bytes: 180461 num_examples: 200 download_size: 15719851 dataset_size: 1978518 - config_name: hn-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1879776 num_examples: 2000 - name: test num_bytes: 187954 num_examples: 200 download_size: 15719851 dataset_size: 2067730 - config_name: hn-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1915482 num_examples: 1250 - name: test num_bytes: 191747 num_examples: 125 download_size: 15719851 dataset_size: 2107229 - config_name: hn-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2392212 num_examples: 2000 - name: test num_bytes: 240436 num_examples: 200 download_size: 15719851 dataset_size: 2632648 - config_name: hn-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1702512 num_examples: 2500 - name: test num_bytes: 170259 num_examples: 250 download_size: 15719851 dataset_size: 1872771 - config_name: hn-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 5229983 num_examples: 10000 - name: test num_bytes: 523032 num_examples: 1000 download_size: 15719851 dataset_size: 5753015 - config_name: hn-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1039729 num_examples: 1250 - name: test num_bytes: 104061 num_examples: 125 download_size: 15719851 dataset_size: 1143790 - config_name: hn-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1738458 num_examples: 1977 - name: test num_bytes: 176824 num_examples: 198 download_size: 15719851 dataset_size: 1915282 - config_name: hn-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4702044 num_examples: 10000 - name: test num_bytes: 470479 num_examples: 1000 download_size: 15719851 dataset_size: 5172523 - config_name: hn-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1147599 num_examples: 934 - name: test num_bytes: 115088 num_examples: 94 download_size: 15719851 dataset_size: 1262687 - config_name: shuffled-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165386 num_examples: 200 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 330903 - config_name: shuffled-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 302888 num_examples: 200 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 609519 - config_name: shuffled-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 887756 num_examples: 200 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 1770943 - config_name: shuffled-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 205510 num_examples: 1000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 410944 - config_name: shuffled-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 337349 num_examples: 200 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 687806 - config_name: shuffled-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 173053 num_examples: 200 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 345302 - config_name: shuffled-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 224778 num_examples: 200 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 440290 - config_name: shuffled-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 212517 num_examples: 200 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 428761 - config_name: shuffled-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 168350 num_examples: 200 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 336598 - config_name: shuffled-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 170257 num_examples: 200 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 340929 - config_name: shuffled-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 178083 num_examples: 200 - name: test num_bytes: 178313 num_examples: 200 download_size: 15719851 dataset_size: 356396 - config_name: shuffled-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 185600 num_examples: 200 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 371129 - config_name: shuffled-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 190556 num_examples: 200 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 381040 - config_name: shuffled-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 234355 num_examples: 200 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 467559 - config_name: shuffled-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 163728 num_examples: 250 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 327537 - config_name: shuffled-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 456374 num_examples: 1000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 912622 - config_name: shuffled-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 103636 num_examples: 125 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 207254 - config_name: shuffled-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 162875 num_examples: 198 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 324141 - config_name: shuffled-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 404536 num_examples: 1000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 809025 - config_name: shuffled-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 115812 num_examples: 94 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 231675 - config_name: shuffled-10k-qa1 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1654288 num_examples: 2000 - name: test num_bytes: 165517 num_examples: 200 download_size: 15719851 dataset_size: 1819805 - config_name: shuffled-10k-qa2 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3062580 num_examples: 2000 - name: test num_bytes: 306631 num_examples: 200 download_size: 15719851 dataset_size: 3369211 - config_name: shuffled-10k-qa3 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 8921215 num_examples: 2000 - name: test num_bytes: 883187 num_examples: 200 download_size: 15719851 dataset_size: 9804402 - config_name: shuffled-10k-qa4 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2055105 num_examples: 10000 - name: test num_bytes: 205434 num_examples: 1000 download_size: 15719851 dataset_size: 2260539 - config_name: shuffled-10k-qa5 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 3592157 num_examples: 2000 - name: test num_bytes: 350457 num_examples: 200 download_size: 15719851 dataset_size: 3942614 - config_name: shuffled-10k-qa6 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1726716 num_examples: 2000 - name: test num_bytes: 172249 num_examples: 200 download_size: 15719851 dataset_size: 1898965 - config_name: shuffled-10k-qa7 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2228087 num_examples: 2000 - name: test num_bytes: 215512 num_examples: 200 download_size: 15719851 dataset_size: 2443599 - config_name: shuffled-10k-qa8 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2141880 num_examples: 2000 - name: test num_bytes: 216244 num_examples: 200 download_size: 15719851 dataset_size: 2358124 - config_name: shuffled-10k-qa9 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1681213 num_examples: 2000 - name: test num_bytes: 168248 num_examples: 200 download_size: 15719851 dataset_size: 1849461 - config_name: shuffled-10k-qa10 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1707675 num_examples: 2000 - name: test num_bytes: 170672 num_examples: 200 download_size: 15719851 dataset_size: 1878347 - config_name: shuffled-10k-qa11 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1781176 num_examples: 2000 - name: test num_bytes: 178313 num_examples: 200 download_size: 15719851 dataset_size: 1959489 - config_name: shuffled-10k-qa12 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1854745 num_examples: 2000 - name: test num_bytes: 185529 num_examples: 200 download_size: 15719851 dataset_size: 2040274 - config_name: shuffled-10k-qa13 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1903149 num_examples: 2000 - name: test num_bytes: 190484 num_examples: 200 download_size: 15719851 dataset_size: 2093633 - config_name: shuffled-10k-qa14 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2321511 num_examples: 2000 - name: test num_bytes: 233204 num_examples: 200 download_size: 15719851 dataset_size: 2554715 - config_name: shuffled-10k-qa15 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1637398 num_examples: 2500 - name: test num_bytes: 163809 num_examples: 250 download_size: 15719851 dataset_size: 1801207 - config_name: shuffled-10k-qa16 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4562844 num_examples: 10000 - name: test num_bytes: 456248 num_examples: 1000 download_size: 15719851 dataset_size: 5019092 - config_name: shuffled-10k-qa17 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1034333 num_examples: 1250 - name: test num_bytes: 103618 num_examples: 125 download_size: 15719851 dataset_size: 1137951 - config_name: shuffled-10k-qa18 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1641650 num_examples: 1978 - name: test num_bytes: 161266 num_examples: 199 download_size: 15719851 dataset_size: 1802916 - config_name: shuffled-10k-qa19 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4045086 num_examples: 10000 - name: test num_bytes: 404489 num_examples: 1000 download_size: 15719851 dataset_size: 4449575 - config_name: shuffled-10k-qa20 features: - name: story sequence: - name: id dtype: string - name: type dtype: class_label: names: '0': context '1': question - name: text dtype: string - name: supporting_ids sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1157351 num_examples: 933 - name: test num_bytes: 115863 num_examples: 93 download_size: 15719851 dataset_size: 1273214 --- # Dataset Card for bAbi QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[The bAbI project](https://research.fb.com/downloads/babi/) - **Repository:** - **Paper:** [arXiv Paper](https://arxiv.org/pdf/1502.05698.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. The aim is to classify these tasks into skill sets,so that researchers can identify (and then rectify) the failings of their systems. ### Supported Tasks and Leaderboards The dataset supports a set of 20 proxy story-based question answering tasks for various "types" in English and Hindi. The tasks are: |task_no|task_name| |----|------------| |qa1 |single-supporting-fact| |qa2 |two-supporting-facts| |qa3 |three-supporting-facts| |qa4 |two-arg-relations| |qa5 |three-arg-relations| |qa6 |yes-no-questions| |qa7 |counting| |qa8 |lists-sets| |qa9 |simple-negation| |qa10| indefinite-knowledge| |qa11| basic-coreference| |qa12| conjunction| |qa13| compound-coreference| |qa14| time-reasoning| |qa15| basic-deduction| |qa16| basic-induction| |qa17| positional-reasoning| |qa18| size-reasoning| |qa19| path-finding| |qa20| agents-motivations| The "types" are are: - `en` - the tasks in English, readable by humans. - `hn` - the tasks in Hindi, readable by humans. - `shuffled` - the same tasks with shuffled letters so they are not readable by humans, and for existing parsers and taggers cannot be used in a straight-forward fashion to leverage extra resources-- in this case the learner is more forced to rely on the given training data. This mimics a learner being first presented a language and having to learn from scratch. - `en-10k`, `shuffled-10k` and `hn-10k` - the same tasks in the three formats, but with 10,000 training examples, rather than 1000 training examples. - `en-valid` and `en-valid-10k` - are the same as `en` and `en10k` except the train sets have been conveniently split into train and valid portions (90% and 10% split). To get a particular dataset, use `load_dataset('babi_qa',type=f'{type}',task_no=f'{task_no}')` where `type` is one of the types, and `task_no` is one of the task numbers. For example, `load_dataset('babi_qa', type='en', task_no='qa1')`. ### Languages ## Dataset Structure ### Data Instances An instance from the `en-qa1` config's `train` split: ``` {'story': {'answer': ['', '', 'bathroom', '', '', 'hallway', '', '', 'hallway', '', '', 'office', '', '', 'bathroom'], 'id': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'], 'supporting_ids': [[], [], ['1'], [], [], ['4'], [], [], ['4'], [], [], ['11'], [], [], ['8']], 'text': ['Mary moved to the bathroom.', 'John went to the hallway.', 'Where is Mary?', 'Daniel went back to the hallway.', 'Sandra moved to the garden.', 'Where is Daniel?', 'John moved to the office.', 'Sandra journeyed to the bathroom.', 'Where is Daniel?', 'Mary moved to the hallway.', 'Daniel travelled to the office.', 'Where is Daniel?', 'John went back to the garden.', 'John moved to the bedroom.', 'Where is Sandra?'], 'type': [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1]}} ``` ### Data Fields - `story`: a dictionary feature containing: - `id`: a `string` feature, which denotes the line number in the example. - `type`: a classification label, with possible values including `context`, `question`, denoting whether the text is context or a question. - `text`: a `string` feature the text present, whether it is a question or context. - `supporting_ids`: a `list` of `string` features containing the line numbers of the lines in the example which support the answer. - `answer`: a `string` feature containing the answer to the question, or an empty string if the `type`s is not `question`. ### Data Splits The splits and corresponding sizes are: | | train | test | validation | |-------------------|---------|--------|--------------| | en-qa1 | 200 | 200 | - | | en-qa2 | 200 | 200 | - | | en-qa3 | 200 | 200 | - | | en-qa4 | 1000 | 1000 | - | | en-qa5 | 200 | 200 | - | | en-qa6 | 200 | 200 | - | | en-qa7 | 200 | 200 | - | | en-qa8 | 200 | 200 | - | | en-qa9 | 200 | 200 | - | | en-qa10 | 200 | 200 | - | | en-qa11 | 200 | 200 | - | | en-qa12 | 200 | 200 | - | | en-qa13 | 200 | 200 | - | | en-qa14 | 200 | 200 | - | | en-qa15 | 250 | 250 | - | | en-qa16 | 1000 | 1000 | - | | en-qa17 | 125 | 125 | - | | en-qa18 | 198 | 199 | - | | en-qa19 | 1000 | 1000 | - | | en-qa20 | 94 | 93 | - | | en-10k-qa1 | 2000 | 200 | - | | en-10k-qa2 | 2000 | 200 | - | | en-10k-qa3 | 2000 | 200 | - | | en-10k-qa4 | 10000 | 1000 | - | | en-10k-qa5 | 2000 | 200 | - | | en-10k-qa6 | 2000 | 200 | - | | en-10k-qa7 | 2000 | 200 | - | | en-10k-qa8 | 2000 | 200 | - | | en-10k-qa9 | 2000 | 200 | - | | en-10k-qa10 | 2000 | 200 | - | | en-10k-qa11 | 2000 | 200 | - | | en-10k-qa12 | 2000 | 200 | - | | en-10k-qa13 | 2000 | 200 | - | | en-10k-qa14 | 2000 | 200 | - | | en-10k-qa15 | 2500 | 250 | - | | en-10k-qa16 | 10000 | 1000 | - | | en-10k-qa17 | 1250 | 125 | - | | en-10k-qa18 | 1978 | 199 | - | | en-10k-qa19 | 10000 | 1000 | - | | en-10k-qa20 | 933 | 93 | - | | en-valid-qa1 | 180 | 200 | 20 | | en-valid-qa2 | 180 | 200 | 20 | | en-valid-qa3 | 180 | 200 | 20 | | en-valid-qa4 | 900 | 1000 | 100 | | en-valid-qa5 | 180 | 200 | 20 | | en-valid-qa6 | 180 | 200 | 20 | | en-valid-qa7 | 180 | 200 | 20 | | en-valid-qa8 | 180 | 200 | 20 | | en-valid-qa9 | 180 | 200 | 20 | | en-valid-qa10 | 180 | 200 | 20 | | en-valid-qa11 | 180 | 200 | 20 | | en-valid-qa12 | 180 | 200 | 20 | | en-valid-qa13 | 180 | 200 | 20 | | en-valid-qa14 | 180 | 200 | 20 | | en-valid-qa15 | 225 | 250 | 25 | | en-valid-qa16 | 900 | 1000 | 100 | | en-valid-qa17 | 113 | 125 | 12 | | en-valid-qa18 | 179 | 199 | 19 | | en-valid-qa19 | 900 | 1000 | 100 | | en-valid-qa20 | 85 | 93 | 9 | | en-valid-10k-qa1 | 1800 | 200 | 200 | | en-valid-10k-qa2 | 1800 | 200 | 200 | | en-valid-10k-qa3 | 1800 | 200 | 200 | | en-valid-10k-qa4 | 9000 | 1000 | 1000 | | en-valid-10k-qa5 | 1800 | 200 | 200 | | en-valid-10k-qa6 | 1800 | 200 | 200 | | en-valid-10k-qa7 | 1800 | 200 | 200 | | en-valid-10k-qa8 | 1800 | 200 | 200 | | en-valid-10k-qa9 | 1800 | 200 | 200 | | en-valid-10k-qa10 | 1800 | 200 | 200 | | en-valid-10k-qa11 | 1800 | 200 | 200 | | en-valid-10k-qa12 | 1800 | 200 | 200 | | en-valid-10k-qa13 | 1800 | 200 | 200 | | en-valid-10k-qa14 | 1800 | 200 | 200 | | en-valid-10k-qa15 | 2250 | 250 | 250 | | en-valid-10k-qa16 | 9000 | 1000 | 1000 | | en-valid-10k-qa17 | 1125 | 125 | 125 | | en-valid-10k-qa18 | 1781 | 199 | 197 | | en-valid-10k-qa19 | 9000 | 1000 | 1000 | | en-valid-10k-qa20 | 840 | 93 | 93 | | hn-qa1 | 200 | 200 | - | | hn-qa2 | 200 | 200 | - | | hn-qa3 | 167 | 167 | - | | hn-qa4 | 1000 | 1000 | - | | hn-qa5 | 200 | 200 | - | | hn-qa6 | 200 | 200 | - | | hn-qa7 | 200 | 200 | - | | hn-qa8 | 200 | 200 | - | | hn-qa9 | 200 | 200 | - | | hn-qa10 | 200 | 200 | - | | hn-qa11 | 200 | 200 | - | | hn-qa12 | 200 | 200 | - | | hn-qa13 | 125 | 125 | - | | hn-qa14 | 200 | 200 | - | | hn-qa15 | 250 | 250 | - | | hn-qa16 | 1000 | 1000 | - | | hn-qa17 | 125 | 125 | - | | hn-qa18 | 198 | 198 | - | | hn-qa19 | 1000 | 1000 | - | | hn-qa20 | 93 | 94 | - | | hn-10k-qa1 | 2000 | 200 | - | | hn-10k-qa2 | 2000 | 200 | - | | hn-10k-qa3 | 1667 | 167 | - | | hn-10k-qa4 | 10000 | 1000 | - | | hn-10k-qa5 | 2000 | 200 | - | | hn-10k-qa6 | 2000 | 200 | - | | hn-10k-qa7 | 2000 | 200 | - | | hn-10k-qa8 | 2000 | 200 | - | | hn-10k-qa9 | 2000 | 200 | - | | hn-10k-qa10 | 2000 | 200 | - | | hn-10k-qa11 | 2000 | 200 | - | | hn-10k-qa12 | 2000 | 200 | - | | hn-10k-qa13 | 1250 | 125 | - | | hn-10k-qa14 | 2000 | 200 | - | | hn-10k-qa15 | 2500 | 250 | - | | hn-10k-qa16 | 10000 | 1000 | - | | hn-10k-qa17 | 1250 | 125 | - | | hn-10k-qa18 | 1977 | 198 | - | | hn-10k-qa19 | 10000 | 1000 | - | | hn-10k-qa20 | 934 | 94 | - | | shuffled-qa1 | 200 | 200 | - | | shuffled-qa2 | 200 | 200 | - | | shuffled-qa3 | 200 | 200 | - | | shuffled-qa4 | 1000 | 1000 | - | | shuffled-qa5 | 200 | 200 | - | | shuffled-qa6 | 200 | 200 | - | | shuffled-qa7 | 200 | 200 | - | | shuffled-qa8 | 200 | 200 | - | | shuffled-qa9 | 200 | 200 | - | | shuffled-qa10 | 200 | 200 | - | | shuffled-qa11 | 200 | 200 | - | | shuffled-qa12 | 200 | 200 | - | | shuffled-qa13 | 200 | 200 | - | | shuffled-qa14 | 200 | 200 | - | | shuffled-qa15 | 250 | 250 | - | | shuffled-qa16 | 1000 | 1000 | - | | shuffled-qa17 | 125 | 125 | - | | shuffled-qa18 | 198 | 199 | - | | shuffled-qa19 | 1000 | 1000 | - | | shuffled-qa20 | 94 | 93 | - | | shuffled-10k-qa1 | 2000 | 200 | - | | shuffled-10k-qa2 | 2000 | 200 | - | | shuffled-10k-qa3 | 2000 | 200 | - | | shuffled-10k-qa4 | 10000 | 1000 | - | | shuffled-10k-qa5 | 2000 | 200 | - | | shuffled-10k-qa6 | 2000 | 200 | - | | shuffled-10k-qa7 | 2000 | 200 | - | | shuffled-10k-qa8 | 2000 | 200 | - | | shuffled-10k-qa9 | 2000 | 200 | - | | shuffled-10k-qa10 | 2000 | 200 | - | | shuffled-10k-qa11 | 2000 | 200 | - | | shuffled-10k-qa12 | 2000 | 200 | - | | shuffled-10k-qa13 | 2000 | 200 | - | | shuffled-10k-qa14 | 2000 | 200 | - | | shuffled-10k-qa15 | 2500 | 250 | - | | shuffled-10k-qa16 | 10000 | 1000 | - | | shuffled-10k-qa17 | 1250 | 125 | - | | shuffled-10k-qa18 | 1978 | 199 | - | | shuffled-10k-qa19 | 10000 | 1000 | - | | shuffled-10k-qa20 | 933 | 93 | - | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Code to generate tasks is available on [github](https://github.com/facebook/bAbI-tasks) #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston, at Facebook Research. ### Licensing Information ``` Creative Commons Attribution 3.0 License ``` ### Citation Information ``` @misc{dodge2016evaluating, title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems}, author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston}, year={2016}, eprint={1511.06931}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.

banking77

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - multi-class-classification pretty_name: BANKING77 dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': activate_my_card '1': age_limit '2': apple_pay_or_google_pay '3': atm_support '4': automatic_top_up '5': balance_not_updated_after_bank_transfer '6': balance_not_updated_after_cheque_or_cash_deposit '7': beneficiary_not_allowed '8': cancel_transfer '9': card_about_to_expire '10': card_acceptance '11': card_arrival '12': card_delivery_estimate '13': card_linking '14': card_not_working '15': card_payment_fee_charged '16': card_payment_not_recognised '17': card_payment_wrong_exchange_rate '18': card_swallowed '19': cash_withdrawal_charge '20': cash_withdrawal_not_recognised '21': change_pin '22': compromised_card '23': contactless_not_working '24': country_support '25': declined_card_payment '26': declined_cash_withdrawal '27': declined_transfer '28': direct_debit_payment_not_recognised '29': disposable_card_limits '30': edit_personal_details '31': exchange_charge '32': exchange_rate '33': exchange_via_app '34': extra_charge_on_statement '35': failed_transfer '36': fiat_currency_support '37': get_disposable_virtual_card '38': get_physical_card '39': getting_spare_card '40': getting_virtual_card '41': lost_or_stolen_card '42': lost_or_stolen_phone '43': order_physical_card '44': passcode_forgotten '45': pending_card_payment '46': pending_cash_withdrawal '47': pending_top_up '48': pending_transfer '49': pin_blocked '50': receiving_money '51': Refund_not_showing_up '52': request_refund '53': reverted_card_payment? '54': supported_cards_and_currencies '55': terminate_account '56': top_up_by_bank_transfer_charge '57': top_up_by_card_charge '58': top_up_by_cash_or_cheque '59': top_up_failed '60': top_up_limits '61': top_up_reverted '62': topping_up_by_card '63': transaction_charged_twice '64': transfer_fee_charged '65': transfer_into_account '66': transfer_not_received_by_recipient '67': transfer_timing '68': unable_to_verify_identity '69': verify_my_identity '70': verify_source_of_funds '71': verify_top_up '72': virtual_card_not_working '73': visa_or_mastercard '74': why_verify_identity '75': wrong_amount_of_cash_received '76': wrong_exchange_rate_for_cash_withdrawal splits: - name: train num_bytes: 715036 num_examples: 10003 - name: test num_bytes: 204014 num_examples: 3080 download_size: 1079034 dataset_size: 919050 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for BANKING77 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets) - **Repository:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets) - **Paper:** [ArXiv](https://arxiv.org/abs/2003.04807) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> Deprecated: Dataset "banking77" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/PolyAI/banking77">PolyAI/banking77</a>" instead. </div> Dataset composed of online banking queries annotated with their corresponding intents. BANKING77 dataset provides a very fine-grained set of intents in a banking domain. It comprises 13,083 customer service queries labeled with 77 intents. It focuses on fine-grained single-domain intent detection. ### Supported Tasks and Leaderboards Intent classification, intent detection ### Languages English ## Dataset Structure ### Data Instances An example of 'train' looks as follows: ``` { 'label': 11, # integer label corresponding to "card_arrival" intent 'text': 'I am still waiting on my card?' } ``` ### Data Fields - `text`: a string feature. - `label`: One of classification labels (0-76) corresponding to unique intents. Intent names are mapped to `label` in the following way: | label | intent (category) | |---:|:-------------------------------------------------| | 0 | activate_my_card | | 1 | age_limit | | 2 | apple_pay_or_google_pay | | 3 | atm_support | | 4 | automatic_top_up | | 5 | balance_not_updated_after_bank_transfer | | 6 | balance_not_updated_after_cheque_or_cash_deposit | | 7 | beneficiary_not_allowed | | 8 | cancel_transfer | | 9 | card_about_to_expire | | 10 | card_acceptance | | 11 | card_arrival | | 12 | card_delivery_estimate | | 13 | card_linking | | 14 | card_not_working | | 15 | card_payment_fee_charged | | 16 | card_payment_not_recognised | | 17 | card_payment_wrong_exchange_rate | | 18 | card_swallowed | | 19 | cash_withdrawal_charge | | 20 | cash_withdrawal_not_recognised | | 21 | change_pin | | 22 | compromised_card | | 23 | contactless_not_working | | 24 | country_support | | 25 | declined_card_payment | | 26 | declined_cash_withdrawal | | 27 | declined_transfer | | 28 | direct_debit_payment_not_recognised | | 29 | disposable_card_limits | | 30 | edit_personal_details | | 31 | exchange_charge | | 32 | exchange_rate | | 33 | exchange_via_app | | 34 | extra_charge_on_statement | | 35 | failed_transfer | | 36 | fiat_currency_support | | 37 | get_disposable_virtual_card | | 38 | get_physical_card | | 39 | getting_spare_card | | 40 | getting_virtual_card | | 41 | lost_or_stolen_card | | 42 | lost_or_stolen_phone | | 43 | order_physical_card | | 44 | passcode_forgotten | | 45 | pending_card_payment | | 46 | pending_cash_withdrawal | | 47 | pending_top_up | | 48 | pending_transfer | | 49 | pin_blocked | | 50 | receiving_money | | 51 | Refund_not_showing_up | | 52 | request_refund | | 53 | reverted_card_payment? | | 54 | supported_cards_and_currencies | | 55 | terminate_account | | 56 | top_up_by_bank_transfer_charge | | 57 | top_up_by_card_charge | | 58 | top_up_by_cash_or_cheque | | 59 | top_up_failed | | 60 | top_up_limits | | 61 | top_up_reverted | | 62 | topping_up_by_card | | 63 | transaction_charged_twice | | 64 | transfer_fee_charged | | 65 | transfer_into_account | | 66 | transfer_not_received_by_recipient | | 67 | transfer_timing | | 68 | unable_to_verify_identity | | 69 | verify_my_identity | | 70 | verify_source_of_funds | | 71 | verify_top_up | | 72 | virtual_card_not_working | | 73 | visa_or_mastercard | | 74 | why_verify_identity | | 75 | wrong_amount_of_cash_received | | 76 | wrong_exchange_rate_for_cash_withdrawal | ### Data Splits | Dataset statistics | Train | Test | | --- | --- | --- | | Number of examples | 10 003 | 3 080 | | Average character length | 59.5 | 54.2 | | Number of intents | 77 | 77 | | Number of domains | 1 | 1 | ## Dataset Creation ### Curation Rationale Previous intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale *multi-domain* datasets ([HWU64](https://github.com/xliuhw/NLU-Evaluation-Data) and [CLINC150](https://github.com/clinc/oos-eval)), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered "in the wild". This dataset tries to fill the gap and provides a very fine-grained set of intents in a *single-domain* i.e. **banking**. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The dataset does not contain any additional annotations. #### Who are the annotators? [N/A] ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset it to help develop better intent detection systems. Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [PolyAI](https://github.com/PolyAI-LDN) ### Licensing Information Creative Commons Attribution 4.0 International ### Citation Information ``` @inproceedings{Casanueva2020, author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic}, title = {Efficient Intent Detection with Dual Sentence Encoders}, year = {2020}, month = {mar}, note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets}, url = {https://arxiv.org/abs/2003.04807}, booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020} } ``` ### Contributions Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.

bbaw_egyptian

--- annotations_creators: - expert-generated language_creators: - found language: - de - egy - en license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - extended|wikipedia task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: BbawEgyptian dataset_info: features: - name: transcription dtype: string - name: translation dtype: string - name: hieroglyphs dtype: string splits: - name: train num_bytes: 18546162 num_examples: 100736 download_size: 35348686 dataset_size: 18546162 --- # Dataset Card for "bbaw_egyptian" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://edoc.bbaw.de/frontdoor/index/index/docId/2919](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) - **Repository:** [Github](https://phiwi.github.io/all.json) - **Paper:** [Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyph](https://zenodo.org/record/3524924) - **Point of Contact:** [Philipp Wiesenbach](https://www.cl.uni-heidelberg.de/~wiesenbach/index.html) - **Size of downloaded dataset files:** 35.65 MB ### Dataset Summary This dataset comprises parallel sentences of hieroglyphic encodings, transcription and translation as used in the paper [Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyph](https://zenodo.org/record/3524924). The data triples are extracted from the [digital corpus of Egyptian texts](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) compiled by the project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache". ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The dataset consists of parallel triples of - `hieroglyphs`: [Encoding of the hieroglyphs with the [Gardiner's sign list](https://en.wikipedia.org/wiki/Gardiner%27s_sign_list) - `transcription`: Transliteration of the above mentioned hieroglyphs with a [transliteration scheme](https://en.wikipedia.org/wiki/Transliteration_of_Ancient_Egyptian) - `translation`: Translation in mostly German language (with some English mixed in) ## Dataset Structure The dataset is not divided into 'train', 'dev' and 'test' splits as it was not built for competitive purposes and we encourage all scientists to use individual partitioning schemes to suit their needs (due to the low resource setting it might be advisable to use cross validation anyway). The only available split 'all' therefore comprises the full 100,708 translation triples, 35,503 of which possess hieroglyphic encodings (the remaining 65,205 triples have empty `hieroglyph` entries). ### Data Instances An example of a data triple looks the following way: ``` { "transcription": "n rḏi̯(.w) gꜣ =j r dbḥ.t m pr-ḥḏ", "translation": "I was not let to suffer lack in the treasury with respect to what was needed;", "hieroglyphs": "D35 D21 -D37 G1&W11 -V32B A1 D21 D46 -D58 *V28 -F18 *X1 -A2 G17 [? *O2 *?]" } ``` *Important*: Only about a third of the instance actually cover hieroglyphic encodings (the rest is the empty string `""`) as the leftover encodings have not yet been incorporated into the BBAW's project database. ### Data Fields #### plain_text - `transcription`: a `string` feature. - `translation`: a `string` feature. - `hieroglyphs`: a `string` feature. ### Data Splits | name |all| |----------|----:| |plain_text|100708| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data source comes from the project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache" which is compiling an extensively annotated digital corpus of Egyptian texts. Their [publication](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) comprises an excerpt of the internal database's contents. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The corpus has not been preprocessed as we encourage every scientist to prepare the corpus to their desired needs. This means, that all textcritic symbols are still included in the transliteration and translation. This concerns the following annotations: - `()`: defective - `[]`: lost - `{}`: surplus - `〈〉`: omitted - `⸢⸣`: damaged - `⸮?`: unclear - `{{}}`: erasure - `(())`: above - `[[]]`: overstrike - `〈〈〉〉`: haplography Their exists a similar sign list for the annotation of the hieroglyphic encoding. If you wish access to this list, please get in contact with the author. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information Source corpus: ``` @misc{OPUS4-2919, title = {Teilauszug der Datenbank des Vorhabens "Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache" vom Januar 2018}, institution = {Akademienvorhaben Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache. Text- und Wissenskultur im alten {\"A}gypten}, type = {other}, year = {2018}, } ``` Translation paper: ``` @article{wiesenbach19, title = {Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs}, author = {Wiesenbach, Philipp and Riezler, Stefan}, journal = {Proceedings of the International Workshop on Spoken Language Translation}, journal-abbrev = {IWSLT}, year = {2019}, url = {https://www.cl.uni-heidelberg.de/statnlpgroup/publications/IWSLT2019_v2.pdf} } ``` ### Contributions Thanks to [@phiwi](https://github.com/phiwi) for adding this dataset.

bbc_hindi_nli

--- annotations_creators: - machine-generated language_creators: - found language: - hi license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|bbc__hindi_news_classification task_categories: - text-classification task_ids: - natural-language-inference pretty_name: BBC Hindi NLI Dataset dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': not-entailment '1': entailment - name: topic dtype: class_label: names: '0': india '1': news '2': international '3': entertainment '4': sport '5': science config_name: bbc hindi nli splits: - name: train num_bytes: 2990080 num_examples: 15552 - name: validation num_bytes: 496808 num_examples: 2580 - name: test num_bytes: 494432 num_examples: 2592 download_size: 3815652 dataset_size: 3981320 --- # Dataset Card for BBC Hindi NLI Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub](https://github.com/midas-research/hindi-nli-data) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71) - **Point of Contact:** [GitHub](https://github.com/midas-research/hindi-nli-data) ### Dataset Summary - Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs. - Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic. - Context and Hypothesis is written in Hindi while Entailment_Label is in English. - Entailment_label is of 2 types - entailed and not-entailed. - Dataset can be used to train models for Natural Language Inference tasks in Hindi Language. [More Information Needed] ### Supported Tasks and Leaderboards - Natural Language Inference for Hindi ### Languages Dataset is in Hindi ## Dataset Structure - Data is structured in TSV format. - Train and Test files are in seperate files ### Dataset Instances An example of 'train' looks as follows. ``` {'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'} ``` ### Data Fields - Each row contatins 4 columns - Premise, Hypothesis, Label and Topic. ### Data Splits - Train : 15553 - Valid : 2581 - Test : 2593 ## Dataset Creation - We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems - In this recasting process, we build template hypotheses for each class in the label taxonomy - Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples. - For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71" ### Source Data Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1) #### Initial Data Collection and Normalization - BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia - We processed this dataset to combine two sets of relevant but low prevalence classes. - Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international. - Likewise, we also merged samples from news, business, social, learning english, and institutional as news. - Lastly, we also removed the class multimedia because there were very few samples. #### Who are the source language producers? Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71" ### Annotations #### Annotation process Annotation process has been described in Dataset Creation Section. #### Who are the annotators? Annotation is done automatically. ### Personal and Sensitive Information No Personal and Sensitive Information is mentioned in the Datasets. ## Considerations for Using the Data Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71 ### Discussion of Biases Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71 ### Other Known Limitations No other known limitations ## Additional Information Pls refer to this link: https://github.com/midas-research/hindi-nli-data ### Dataset Curators It is written in the repo : https://github.com/avinsit123/hindi-nli-data that - This corpus can be used freely for research purposes. - The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper. - If interested in commercial use of the corpus, send email to [email protected]. - If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus. - Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications. - Rather than redistributing the corpus, please direct interested parties to this page - Please feel free to send us an email: - with feedback regarding the corpus. - with information on how you have used the corpus. - if interested in having us analyze your data for natural language inference. - if interested in a collaborative research project. ### Licensing Information Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi). Pls contact authors for any information on the dataset. ### Citation Information ``` @inproceedings{uppal-etal-2020-two, title = "Two-Step Classification using Recasted Data for Low Resource Settings", author = "Uppal, Shagun and Gupta, Vivek and Swaminathan, Avinash and Zhang, Haimin and Mahata, Debanjan and Gosangi, Rakesh and Shah, Rajiv Ratn and Stent, Amanda", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.aacl-main.71", pages = "706--719", abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.", } ``` ### Contributions Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.

bc2gm_corpus

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Bc2GmCorpus dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-GENE '2': I-GENE config_name: bc2gm_corpus splits: - name: train num_bytes: 6095172 num_examples: 12501 - name: validation num_bytes: 1215951 num_examples: 2501 - name: test num_bytes: 2454621 num_examples: 5001 download_size: 4636753 dataset_size: 9765744 --- # Dataset Card for bc2gm_corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/spyysalo/bc2gm-corpus/) - **Repository:** [Github](https://github.com/spyysalo/bc2gm-corpus/) - **Paper:** [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@mahajandiwakar](https://github.com/mahajandiwakar) for adding this dataset.

beans

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification pretty_name: Beans dataset_info: features: - name: image_file_path dtype: string - name: image dtype: image - name: labels dtype: class_label: names: '0': angular_leaf_spot '1': bean_rust '2': healthy splits: - name: train num_bytes: 382110 num_examples: 1034 - name: validation num_bytes: 49711 num_examples: 133 - name: test num_bytes: 46584 num_examples: 128 download_size: 180024906 dataset_size: 478405 --- # Dataset Card for Beans ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Beans Homepage](https://github.com/AI-Lab-Makerere/ibean/) - **Repository:** [AI-Lab-Makerere/ibean](https://github.com/AI-Lab-Makerere/ibean/) - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ### Dataset Summary Beans leaf dataset with images of diseased and health leaves. ### Supported Tasks and Leaderboards - `image-classification`: Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any. ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/0aaa78294d4bf5114f58547e48d91b7826649919505379a167decb629aa92b0a/train/bean_rust/bean_rust_train.109.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x16BAA72A4A8>, 'labels': 1 } ``` ### Data Fields The data instances have the following fields: - `image_file_path`: a `string` filepath to an image. - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ```json { "angular_leaf_spot": 0, "bean_rust": 1, "healthy": 2, } ``` ### Data Splits | |train|validation|test| |-------------|----:|---------:|---:| |# of examples|1034 |133 |128 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @ONLINE {beansdata, author="Makerere AI Lab", title="Bean disease dataset", month="January", year="2020", url="https://github.com/AI-Lab-Makerere/ibean/" } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

best2009

--- annotations_creators: - expert-generated language_creators: - found language: - th license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: [] pretty_name: best2009 tags: - word-tokenization dataset_info: features: - name: fname dtype: string - name: char sequence: string - name: char_type sequence: class_label: names: '0': b_e '1': c '2': d '3': n '4': o '5': p '6': q '7': s '8': s_e '9': t '10': v '11': w - name: is_beginning sequence: class_label: names: '0': neg '1': pos config_name: best2009 splits: - name: train num_bytes: 483129998 num_examples: 148995 - name: test num_bytes: 10498726 num_examples: 2252 download_size: 13891260 dataset_size: 493628724 --- # Dataset Card for `best2009` ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://aiforthai.in.th/ - **Repository:** https://aiforthai.in.th/corpus.php - **Paper:** - **Leaderboard:** - **Point of Contact:** https://aiforthai.in.th/ ### Dataset Summary `best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly. ### Supported Tasks and Leaderboards word tokenization ### Languages Thai ## Dataset Structure ### Data Instances ``` {'char': ['?', 'ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', '\n'], 'char_type': [4, 1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]} {'char': ['ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ห', 'ม', 'า', 'ย', 'ถ', 'ึ', 'ง', ' ', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ข', 'อ', 'ง', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ซ', 'ึ', '่', 'ง', 'เ', 'ร', 'ี', 'ย', 'น', 'ร', 'ู', '้', 'ม', 'า', 'จ', 'า', 'ก', 'พ', '่', 'อ', 'แ', 'ม', '่', ' ', 'ป', 'ู', '่', 'ย', '่', 'า', 'ต', 'า', 'ย', 'า', 'ย', ' ', 'ญ', 'า', 'ต', 'ิ', 'พ', 'ี', '่', 'น', '้', 'อ', 'ง', ' ', 'ห', 'ร', 'ื', 'อ', 'ผ', 'ู', '้', 'ม', 'ี', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ใ', 'น', 'ห', 'ม', 'ู', '่', 'บ', '้', 'า', 'น', 'ใ', 'น', 'ท', '้', 'อ', 'ง', 'ถ', 'ิ', '่', 'น', 'ต', '่', 'า', 'ง', 'ๆ', '\n'], 'char_type': [1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 5, 3, 1, 10, 1, 1, 10, 1, 5, 1, 1, 10, 1, 1, 10, 9, 1, 1, 1, 1, 10, 1, 1, 9, 10, 1, 5, 1, 10, 9, 1, 11, 1, 10, 1, 1, 1, 10, 9, 1, 10, 1, 10, 1, 1, 9, 1, 11, 1, 9, 5, 1, 10, 9, 1, 9, 10, 1, 10, 1, 10, 1, 5, 1, 10, 1, 10, 1, 10, 9, 1, 9, 1, 1, 5, 3, 1, 10, 1, 3, 10, 9, 1, 10, 1, 1, 10, 1, 1, 10, 9, 11, 1, 3, 1, 10, 9, 1, 9, 10, 1, 11, 1, 1, 9, 1, 1, 1, 10, 9, 1, 1, 9, 10, 1, 7, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]} ``` ### Data Fields - `fname`: file name; also marks if article is articles, news, encyclopedia or novels - `char`: characters - `char_type`: character types as adopted from []() by [deepcut](https://github.com/rkcosmos/deepcut) - `is_beginning`: is beginning of word ### Data Splits | | train | test | |-------------------------|------------|---------| | # lines | 148,995 | 2,252 | | avg words per line | 39.05 | NA | | total words | 5,818,521 | NA | | avg characters per line | 140.39 | 202.79 | | total characters | 20,918,132 | 456,684 | | # lines articles | 16,990 | NA | | # lines encyclopedia | 50,631 | NA | | # lines novels | 50,140 | NA | | # lines news | 31,234 | NA | ## Dataset Creation ### Curation Rationale The dataset was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10) by [NECTEC](https://www.nectec.or.th/). ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Respective authors of the articles, news, encyclopedia and novels ### Annotations #### Annotation process Detailed annotation guidelines can be found in `BEST_Guideline_Release1.pdf` as part of the uncompressed files. Word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf) #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information All data are curated from public sources. No personal and sensitive information is expected to be included. ## Considerations for Using the Data ### Social Impact of Dataset - word tokenization dataset from articles, news, encyclopedia and novels ### Discussion of Biases - texts are relatively formal ones from articles, news, encyclopedia and novels. - word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf). ### Other Known Limitations - some tags unrelated to word tokenization (`<NE>` and `<AB>`) are cleaned out. - no word boundary provdied for the test set ## Additional Information ### Dataset Curators [NECTEC](https://www.nectec.or.th/) ### Licensing Information CC-BY-NC-SA 3.0 ### Citation Information Dataset: ``` @inproceedings{kosawat2009best, title={BEST 2009: Thai word segmentation software contest}, author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others}, booktitle={2009 Eighth International Symposium on Natural Language Processing}, pages={83--88}, year={2009}, organization={IEEE} } @inproceedings{boriboon2009best, title={Best corpus development and analysis}, author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit}, booktitle={2009 International Conference on Asian Language Processing}, pages={322--327}, year={2009}, organization={IEEE} } ``` Character type features: ``` @inproceedings{haruechaiyasak2009tlex, title={TLex: Thai lexeme analyser based on the conditional random fields}, author={Haruechaiyasak, Choochart and Kongyoung, Sarawoot}, booktitle={Proceedings of 8th International Symposium on Natural Language Processing}, year={2009} } ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.

bianet

--- annotations_creators: - found language_creators: - found language: - en - ku - tr license: - unknown multilinguality: - translation size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: bianet pretty_name: Bianet configs: - en-to-ku - en-to-tr - en_to_ku - en_to_tr - ku-to-tr - ku_to_tr dataset_info: - config_name: en_to_ku features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ku splits: - name: train num_bytes: 1800806 num_examples: 6402 download_size: 622420 dataset_size: 1800806 - config_name: en_to_tr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - tr splits: - name: train num_bytes: 10231043 num_examples: 34770 download_size: 3544116 dataset_size: 10231043 - config_name: ku_to_tr features: - name: id dtype: string - name: translation dtype: translation: languages: - ku - tr splits: - name: train num_bytes: 2086550 num_examples: 7325 download_size: 725227 dataset_size: 2086550 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Bianet](http://opus.nlpl.eu/Bianet.php) - **Repository:** - **Paper:** [Ataman, D. (2018) Bianet: A Parallel News Corpus in Turkish, Kurdish and English. In Proceedings of the LREC 2018 Workshop MLP-Moment. pp. 14-17. pdf](http://lrec-conf.org/workshops/lrec2018/W19/pdf/6_W19.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A parallel news corpus in Turkish, Kurdish and English; Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper. 3 languages, 3 bitexts total number of files: 6 total number of tokens: 2.25M total number of sentence fragments: 0.14M ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC-BY-SA-4.0 ### Citation Information @InProceedings{ATAMAN18.6, author = {Duygu Ataman}, title = {Bianet: A Parallel News Corpus in Turkish, Kurdish and English}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Jinhua Du and Mihael Arcan and Qun Liu and Hitoshi Isahara}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-15-3}, language = {english} } ### Contributions Thanks to [@param087](https://github.com/param087) for adding this dataset.

bible_para

--- annotations_creators: - found language_creators: - found language: - acu - af - agr - ake - am - amu - ar - bg - bsn - cak - ceb - ch - chq - chr - cjp - cni - cop - crp - cs - da - de - dik - dje - djk - dop - ee - el - en - eo - es - et - eu - fi - fr - gbi - gd - gu - gv - he - hi - hr - hu - hy - id - is - it - ja - jak - jiv - kab - kbh - kek - kn - ko - la - lt - lv - mam - mi - ml - mr - my - ne - nhg - nl - 'no' - ojb - pck - pes - pl - plt - pot - ppk - pt - quc - quw - ro - rom - ru - shi - sk - sl - sn - so - sq - sr - ss - sv - syr - te - th - tl - tmh - tr - uk - usp - vi - wal - wo - xh - zh - zu license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: BiblePara dataset_info: - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 17262178 num_examples: 62195 download_size: 5440713 dataset_size: 17262178 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 17536445 num_examples: 62195 download_size: 5470044 dataset_size: 17536445 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 17105724 num_examples: 62191 download_size: 5418998 dataset_size: 17105724 - config_name: en-fi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 17486055 num_examples: 62026 download_size: 5506407 dataset_size: 17486055 - config_name: en-no features: - name: id dtype: string - name: translation dtype: translation: languages: - en - 'no' splits: - name: train num_bytes: 16681323 num_examples: 62107 download_size: 5293164 dataset_size: 16681323 - config_name: en-hi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - hi splits: - name: train num_bytes: 27849361 num_examples: 62073 download_size: 6224765 dataset_size: 27849361 --- # Dataset Card for BiblePara ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/bible-uedin.php - **Repository:** None - **Paper:** https://link.springer.com/article/10.1007/s10579-014-9287-y - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php E.g. `dataset = load_dataset("bible_para", lang1="fi", lang2="hi")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

big_patent

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: bigpatent pretty_name: Big Patent configs: - a - all - b - c - d - e - f - g - h - y tags: - patent-summarization dataset_info: - config_name: all features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 38367048389 num_examples: 1207222 - name: validation num_bytes: 2115827002 num_examples: 67068 - name: test num_bytes: 2129505280 num_examples: 67072 download_size: 10142923776 dataset_size: 42612380671 - config_name: a features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 5683460620 num_examples: 174134 - name: validation num_bytes: 313324505 num_examples: 9674 - name: test num_bytes: 316633277 num_examples: 9675 download_size: 10142923776 dataset_size: 6313418402 - config_name: b features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4236070976 num_examples: 161520 - name: validation num_bytes: 234425138 num_examples: 8973 - name: test num_bytes: 231538734 num_examples: 8974 download_size: 10142923776 dataset_size: 4702034848 - config_name: c features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4506249306 num_examples: 101042 - name: validation num_bytes: 244684775 num_examples: 5613 - name: test num_bytes: 252566793 num_examples: 5614 download_size: 10142923776 dataset_size: 5003500874 - config_name: d features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 264717412 num_examples: 10164 - name: validation num_bytes: 14560482 num_examples: 565 - name: test num_bytes: 14403430 num_examples: 565 download_size: 10142923776 dataset_size: 293681324 - config_name: e features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 881101433 num_examples: 34443 - name: validation num_bytes: 48646158 num_examples: 1914 - name: test num_bytes: 48586429 num_examples: 1914 download_size: 10142923776 dataset_size: 978334020 - config_name: f features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2146383473 num_examples: 85568 - name: validation num_bytes: 119632631 num_examples: 4754 - name: test num_bytes: 119596303 num_examples: 4754 download_size: 10142923776 dataset_size: 2385612407 - config_name: g features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8877854206 num_examples: 258935 - name: validation num_bytes: 492581177 num_examples: 14385 - name: test num_bytes: 496324853 num_examples: 14386 download_size: 10142923776 dataset_size: 9866760236 - config_name: h features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8075621958 num_examples: 257019 - name: validation num_bytes: 447602356 num_examples: 14279 - name: test num_bytes: 445460513 num_examples: 14279 download_size: 10142923776 dataset_size: 8968684827 - config_name: y features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 3695589005 num_examples: 124397 - name: validation num_bytes: 200369780 num_examples: 6911 - name: test num_bytes: 204394948 num_examples: 6911 download_size: 10142923776 dataset_size: 4100353733 --- # Dataset Card for Big Patent ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Big Patent](https://evasharma.github.io/bigpatent/) - **Repository:** - **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741) - **Leaderboard:** - **Point of Contact:** [Lu Wang](mailto:[email protected]) ### Dataset Summary BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories: - a: Human Necessities - b: Performing Operations; Transporting - c: Chemistry; Metallurgy - d: Textiles; Paper - e: Fixed Constructions - f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting - g: Physics - h: Electricity - y: General tagging of new or cross-sectional technology Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes: ```python from datasets import load_dataset ds = load_dataset("big_patent") # default is 'all' CPC codes ds = load_dataset("big_patent", "all") # the same as above ds = load_dataset("big_patent", "a") # only 'a' CPC codes ds = load_dataset("big_patent", codes=["a", "b"]) ``` To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ds = load_dataset("big_patent", codes="a", version="1.0.0") ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0") ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section. ``` { 'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...', 'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...' } ``` ### Data Fields - `description`: detailed description of patent. - `abstract`: Patent abastract. ### Data Splits | | train | validation | test | |:----|------------------:|-------------:|-------:| | all | 1207222 | 67068 | 67072 | | a | 174134 | 9674 | 9675 | | b | 161520 | 8973 | 8974 | | c | 101042 | 5613 | 5614 | | d | 10164 | 565 | 565 | | e | 34443 | 1914 | 1914 | | f | 85568 | 4754 | 4754 | | g | 258935 | 14385 | 14386 | | h | 257019 | 14279 | 14279 | | y | 124397 | 6911 | 6911 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @article{DBLP:journals/corr/abs-1906-03741, author = {Eva Sharma and Chen Li and Lu Wang}, title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent Summarization}, journal = {CoRR}, volume = {abs/1906.03741}, year = {2019}, url = {http://arxiv.org/abs/1906.03741}, eprinttype = {arXiv}, eprint = {1906.03741}, timestamp = {Wed, 26 Jun 2019 07:14:58 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.

billsum

--- annotations_creators: - found language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: billsum pretty_name: BillSum train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: text: text summary: target metrics: - type: rouge name: Rouge tags: - bills-summarization dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: title dtype: string splits: - name: train num_bytes: 219596090 num_examples: 18949 - name: test num_bytes: 37866257 num_examples: 3269 - name: ca_test num_bytes: 14945291 num_examples: 1237 download_size: 67260676 dataset_size: 272407638 --- # Dataset Card for "billsum" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/FiscalNote/BillSum](https://github.com/FiscalNote/BillSum) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 67.26 MB - **Size of the generated dataset:** 272.42 MB - **Total amount of disk used:** 339.68 MB ### Dataset Summary BillSum, summarization of US Congressional and California state bills. There are several features: - text: bill text. - summary: summary of the bills. - title: title of the bills. features for us bills. ca bills does not have. - text_len: number of chars in text. - sum_len: number of chars in summary. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 67.26 MB - **Size of the generated dataset:** 272.42 MB - **Total amount of disk used:** 339.68 MB An example of 'train' looks as follows. ``` { "summary": "some summary", "text": "some text.", "title": "An act to amend Section xxx." } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. ### Data Splits | name |train|ca_test|test| |-------|----:|------:|---:| |default|18949| 1237|3269| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data consists of three parts: US training bills, US test bills and California test bills. The US bills were collected from the [Govinfo](https://github.com/unitedstates/congress) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license. The California, bills from the 2015-2016 session are available from the legislature’s [website](https://leginfo.legislature.ca.gov/). #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{kornilova2019billsum, title={BillSum: A Corpus for Automatic Summarization of US Legislation}, author={Anastassia Kornilova and Vlad Eidelman}, year={2019}, eprint={1910.00523}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun) for adding this dataset.

bing_coronavirus_query_set

--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: null pretty_name: BingCoronavirusQuerySet dataset_info: features: - name: id dtype: int32 - name: Date dtype: string - name: Query dtype: string - name: IsImplicitIntent dtype: string - name: Country dtype: string - name: PopularityScore dtype: int32 config_name: country_2020-09-01_2020-09-30 splits: - name: train num_bytes: 22052706 num_examples: 317856 download_size: 16351450 dataset_size: 22052706 --- # Dataset Card for BingCoronavirusQuerySet ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** None - **Repository:** https://github.com/microsoft/BingCoronavirusQuerySet - **Paper:** Nonewww - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary Please note that you can specify the start and end date of the data. You can get start and end dates from here: https://github.com/microsoft/BingCoronavirusQuerySet/tree/master/data/2020 example: ``` load_dataset("bing_coronavirus_query_set", queries_by="state", start_date="2020-09-01", end_date="2020-09-30") ``` You can also load the data by country by using `queries_by="country"`. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

biomrc

--- language: - en paperswithcode_id: biomrc pretty_name: BIOMRC dataset_info: - config_name: plain_text features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1653301820 num_examples: 700000 - name: validation num_bytes: 119697683 num_examples: 50000 - name: test num_bytes: 147832373 num_examples: 62707 download_size: 408080356 dataset_size: 1920831876 - config_name: biomrc_large_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 1325877001 num_examples: 700000 - name: validation num_bytes: 96414040 num_examples: 50000 - name: test num_bytes: 118708586 num_examples: 62707 download_size: 343061539 dataset_size: 1540999627 - config_name: biomrc_small_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 206553549 num_examples: 87500 - name: validation num_bytes: 14957163 num_examples: 6250 - name: test num_bytes: 14807799 num_examples: 6250 download_size: 68879274 dataset_size: 236318511 - config_name: biomrc_small_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 165662937 num_examples: 87500 - name: validation num_bytes: 12047304 num_examples: 6250 - name: test num_bytes: 11911172 num_examples: 6250 download_size: 57706889 dataset_size: 189621413 - config_name: biomrc_tiny_A features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 70914 num_examples: 30 download_size: 22519 dataset_size: 70914 - config_name: biomrc_tiny_B features: - name: abstract dtype: string - name: title dtype: string - name: entities_list sequence: string - name: answer dtype: string splits: - name: test num_bytes: 59925 num_examples: 30 download_size: 19685 dataset_size: 59925 --- # Dataset Card for "biomrc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://nlp.cs.aueb.gr/](http://nlp.cs.aueb.gr/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.29 GB - **Size of the generated dataset:** 5.81 GB - **Total amount of disk used:** 7.09 GB ### Dataset Summary We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### biomrc_large_A - **Size of downloaded dataset files:** 408.08 MB - **Size of the generated dataset:** 1.92 GB - **Total amount of disk used:** 2.33 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...", "answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"], "title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n" } ``` #### biomrc_large_B - **Size of downloaded dataset files:** 343.06 MB - **Size of the generated dataset:** 1.54 GB - **Total amount of disk used:** 1.88 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...", "answer": "@entity2", "entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"], "title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n" } ``` #### biomrc_small_A - **Size of downloaded dataset files:** 68.88 MB - **Size of the generated dataset:** 236.32 MB - **Total amount of disk used:** 305.20 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...", "answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n", "entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...", "title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n" } ``` #### biomrc_small_B - **Size of downloaded dataset files:** 57.70 MB - **Size of the generated dataset:** 189.62 MB - **Total amount of disk used:** 247.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...", "answer": "@entity10", "entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"], "title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n" } ``` #### biomrc_tiny_A - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.09 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...", "answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n", "entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"], "title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n" } ``` ### Data Fields The data fields are the same among all splits. #### biomrc_large_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_large_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_small_B - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. #### biomrc_tiny_A - `abstract`: a `string` feature. - `title`: a `string` feature. - `entities_list`: a `list` of `string` features. - `answer`: a `string` feature. ### Data Splits #### biomrc_large_A | |train |validation|test | |--------------|-----:|---------:|----:| |biomrc_large_A|700000| 50000|62707| #### biomrc_large_B | |train |validation|test | |--------------|-----:|---------:|----:| |biomrc_large_B|700000| 50000|62707| #### biomrc_small_A | |train|validation|test| |--------------|----:|---------:|---:| |biomrc_small_A|87500| 6250|6250| #### biomrc_small_B | |train|validation|test| |--------------|----:|---------:|---:| |biomrc_small_B|87500| 6250|6250| #### biomrc_tiny_A | |test| |-------------|---:| |biomrc_tiny_A| 30| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{pappas-etal-2020-biomrc, title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension", author = "Pappas, Dimitris and Stavropoulos, Petros and Androutsopoulos, Ion and McDonald, Ryan", booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.bionlp-1.15", pages = "140--149", abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.", } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@PetrosStav](https://github.com/PetrosStav), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

biosses

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - gpl-3.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - semantic-similarity-scoring paperswithcode_id: biosses pretty_name: BIOSSES dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float32 splits: - name: train num_bytes: 32783 num_examples: 100 download_size: 36324 dataset_size: 32783 --- # Dataset Card for BIOSSES ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html - **Repository:** https://github.com/gizemsogancioglu/biosses - **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954) - **Point of Contact:** [Gizem Soğancıoğlu]([email protected]) and [Arzucan Özgür]([email protected]) ### Dataset Summary BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article. The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows: - very strong: 0.80–1.00 - strong: 0.60–0.79 - moderate: 0.40–0.59 - weak: 0.20–0.39 - very weak: 0.00–0.19 ### Supported Tasks and Leaderboards Biomedical Semantic Similarity Scoring. ### Languages English. ## Dataset Structure ### Data Instances For each instance, there are two sentences (i.e. sentence 1 and 2), and its corresponding similarity score (the mean of the scores assigned by the five human annotators). ``` {'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation' 'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase' 'score': 2.2} ``` ### Data Fields - `sentence 1`: string - `sentence 2`: string - `score`: float ranging from 0 (no relation) to 4 (equivalent) ### Data Splits No data splits provided. ## Dataset Creation ### Curation Rationale ### Source Data The [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/). #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees. The table below shows the Pearson correlation of the scores of each annotator with respect to the average scores of the remaining four annotators. It is observed that there is strong association among the scores of the annotators. The lowest correlations are 0.902, which can be considered as an upper bound for an algorithmic measure evaluated on this dataset. | |Correlation r | |----------:|--------------:| |Annotator A| 0.952| |Annotator B| 0.958| |Annotator C| 0.917| |Annotator D| 0.902| |Annotator E| 0.941| #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators - Gizem Soğancıoğlu, [email protected] - Hakime Öztürk, [email protected] - Arzucan Özgür, [email protected] Bogazici University, Istanbul, Turkey ### Licensing Information BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html). ### Citation Information @article{souganciouglu2017biosses, title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain}, author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan}, journal={Bioinformatics}, volume={33}, number={14}, pages={i49--i58}, year={2017}, publisher={Oxford University Press} } ### Contributions Thanks to [@bwang482](https://github.com/bwang482) for adding this dataset.

blbooks

--- annotations_creators: - no-annotation language_creators: - machine-generated language: - de - en - es - fr - it - nl license: - cc0-1.0 multilinguality: - multilingual pretty_name: British Library Books size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask - other task_ids: - language-modeling - masked-language-modeling tags: - digital-humanities-research dataset_info: - config_name: all features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30394267732 num_examples: 14011953 download_size: 10486035662 dataset_size: 30394267732 - config_name: 1800s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30020434670 num_examples: 13781747 download_size: 10348577602 dataset_size: 30020434670 - config_name: 1700s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 266382657 num_examples: 178224 download_size: 95137895 dataset_size: 266382657 - config_name: '1510_1699' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 107667469 num_examples: 51982 download_size: 42320165 dataset_size: 107667469 - config_name: '1500_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30452067039 num_examples: 14011953 download_size: 10486035662 dataset_size: 30452067039 - config_name: '1800_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30077284377 num_examples: 13781747 download_size: 10348577602 dataset_size: 30077284377 - config_name: '1700_1799' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 267117831 num_examples: 178224 download_size: 95137895 dataset_size: 267117831 --- # Dataset Card for British Library Books ## Table of Contents - [Dataset Card for British Library Books](#dataset-card-for-British-Library-Books) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Language model training](#language-model-training) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Language change](#language-change) - [Optical Character Recognition](#optical-character-recognition) - [OCR word confidence](#ocr-word-confidence) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Date normalization](#date-normalization) - [Metadata included](#metadata-included) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Colonialism](#colonialism) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.bl.uk/collection-guides/digitised-printed-books - **Repository:** https://doi.org/10.21250/db14 - **Paper:** - **Leaderboard:** - **Point of Contact:** [email protected] ### Dataset Summary This dataset consists of books digitised by the British Library in partnership with Microsoft. The dataset includes ~25 million pages of out of copyright texts. The majority of the texts were published in the 18th and 19th Century, but the collection also consists of a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas, including geography, philosophy, history, poetry and literature and are published in various languages. While the books are predominately from the 18th and 19th Centuries, there are fewer books from earlier periods. The number of pages in the corpus by decade: | | page count | | ---- | ---------- | | 1510 | 94 | | 1520 | 32 | | 1540 | 184 | | 1550 | 16 | | 1580 | 276 | | 1590 | 540 | | 1600 | 1117 | | 1610 | 1132 | | 1620 | 1856 | | 1630 | 9274 | | 1640 | 4232 | | 1650 | 2944 | | 1660 | 5858 | | 1670 | 11415 | | 1680 | 8348 | | 1690 | 13756 | | 1700 | 10160 | | 1710 | 9556 | | 1720 | 10314 | | 1730 | 13282 | | 1740 | 10778 | | 1750 | 12001 | | 1760 | 21415 | | 1770 | 28490 | | 1780 | 32676 | | 1790 | 50014 | | 1800 | 307806 | | 1810 | 478008 | | 1820 | 589419 | | 1830 | 681212 | | 1840 | 1113473 | | 1850 | 1726108 | | 1860 | 1725407 | | 1870 | 2069089 | | 1880 | 2585159 | | 1890 | 3365031 | [More Information Needed] ### Supported Tasks and Leaderboards This collection has been previously used across various digital history and humanities projects since being published. The dataset consists of text and a range of metadata associated with this text. This metadata includes: - date of publication - place of publication - country of publication - language - OCR quality - physical description of the original physical item #### Language model training As a relatively large dataset, `blbooks` provides a source dataset for training language models. The presence of this metadata also offers interesting opportunities to use this dataset as a source for training language models based on: - specific time-periods - specific languages - certain OCR quality thresholds The above is not an exhaustive list but offer some suggestions of how the dataset can be used to explore topics such as the impact of OCR quality on language models, the ‘transferability’ of language models across time or the impact of training multilingual language models on historical languages. #### Supervised tasks Whilst this dataset does not have annotations for a specific NLP task, such as Named Entity Recognition, it does include a wide variety of metadata. This metadata has the potential to be used for training and/or evaluating a variety of supervised tasks predicting this metadata. ### Languages This dataset consists of books published in several languages. The breakdown of the languages included (at the page level) is: | Language | Pages | | --------------------- | -------- | | English | 10039463 | | French | 1442929 | | German | 1172793 | | Spanish | 286778 | | Italian | 214255 | | Dutch | 204759 | | Russian | 193347 | | Danish | 93366 | | Hungarian | 88094 | | Swedish | 76225 | | Polish | 58901 | | Greek, Modern (1453-) | 26104 | | Latin | 25611 | | Portuguese | 25410 | | Czech | 20160 | | Bulgarian | 7891 | | Finnish | 5677 | | Irish | 2743 | | Serbian | 1975 | | Romanian | 1544 | | Norwegian Nynorsk | 1398 | | Croatian | 1306 | | Norwegian | 1227 | | Icelandic | 902 | | Slovak | 840 | | Lithuanian | 714 | | Welsh | 580 | | Slovenian | 545 | | Indonesian | 418 | | Cornish | 223 | This breakdown was derived from the first language in the associated metadata field. Some books include multiple languages. Some of the languages codes for this data were also derived using computational methods. Therefore, the language fields in the dataset should be treated with some caution (discussed in more detail below). #### Language change The publication dates of books in the data cover a broad period of time (1500-1900). For languages in the dataset with broad temporal coverage, significant [language change](https://en.wikipedia.org/wiki/Language_change) might be found. The ability to study this change by taking reasonably large samples of languages covering different time periods is one of the opportunities offered by this dataset. The fact that the text in this dataset was produced via Optical Character Recognition (OCR) causes some challenges for this type of research (see below). #### Optical Character Recognition The digitised books in this collection were transformed into machine-readable text using Optical Character Recognition (OCR) software. The text produced via OCR software will usually include some errors. These errors include; mistakes at the character level; for example, an `i` is mistaken for an `l`, at the word level or across significant passages of text. The books in this dataset can pose some additional challenges for OCR software. OCR errors can stem from: - the quality of the original printing: printing technology was a developing technology during the time period covered by this corpus; some of the original book text will include misprints, blurred or faded ink that is hard to read - damage to the page: some of the books will have become damaged over time, this can obscure all or parts of the text on a page - poor quality scans: scanning books can be challenging; for example, if the book has tight bindings, it can be hard to capture text that has fallen into the [gutter](https://www.abaa.org/glossary/entry/gutter) of the book. - the language used in the books may differ from the languages OCR software is predominantly trained to recognise. ##### OCR word confidence Many OCR engines produce some form of confidence score alongside the predicted text. These confidence scores are usually at the character or word level. The word confidence score was given for each word in the original ALTO XML versions of the text in this dataset in this dataset. The OCR confidence scores should be treated with some scepticism. For historical text or in a lower resource language, for example, a low confidence score may be more likely for words not included in a modern dictionary but may be accurate transcriptions of the original text. With that said, the confidence scores do give some sense of the OCR quality. An example of text with a high (over 90% mean word confidence score): ``` 8 direction to the Conduit, round which is a wide open space, and a good broad pavement called the Parade. It commands a pleasant peep of the slopes and terrace throughout its entire length. The street continuing from the Conduit, in the same general direction, was known anciently as Lodborne Lane, and is now named South Street. From the Conduit two other streets, at right angles to these, are Long Street, leading Eastwards, and Half-Moon Street (formerly Lodborne), leading to Westbury, Trendle Street, and the Horsecastles Road. ``` An example of text with a score below 40%: ``` Hannover. Schrift und Druck von Fr. CultniTmn,', "LeMNs'utluirui.", 'ü 8u«llim» M^äalßwi 01de!lop 1<M.', 'p^dnalmw vom Xr^u/e, lpiti>»**Kmm lie« !»^2!M kleine lii!<! (,«>* ttünee!<»e^ v»n tndzt Lievclum, 1872, ``` The quality of OCR - as measured by mean OCR confidence for a page - across the dataset correlates with other features. A groupby of publication decade and mean word confidence: | decade | mean_wc_ocr | | ------ | ----------- | | 1510 | 0.499151 | | 1520 | 0.544818 | | 1540 | 0.511589 | | 1550 | 0.4505 | | 1580 | 0.321858 | | 1590 | 0.461282 | | 1600 | 0.467318 | | 1610 | 0.495895 | | 1620 | 0.501257 | | 1630 | 0.49766 | | 1640 | 0.512095 | | 1650 | 0.528534 | | 1660 | 0.521014 | | 1670 | 0.592575 | | 1680 | 0.583901 | | 1690 | 0.567202 | | 1700 | 0.575175 | | 1710 | 0.61436 | | 1720 | 0.627725 | | 1730 | 0.658534 | | 1740 | 0.64214 | | 1750 | 0.657357 | | 1760 | 0.6389 | | 1770 | 0.651883 | | 1780 | 0.632326 | | 1790 | 0.664279 | | 1800 | 0.682338 | | 1810 | 0.708915 | | 1820 | 0.730015 | | 1830 | 0.730973 | | 1840 | 0.713886 | | 1850 | 0.697106 | | 1860 | 0.696701 | | 1870 | 0.717233 | | 1880 | 0.733331 | | 1890 | 0.762364 | As might be expected, the earlier periods have lower mean word confidence scores. Again, all of this should be treated with some scepticism, especially as the size of the data grows over time. As with time, the mean word confidence of the OCR software varies across languages: | Language_1 | mean_wc_ocr | | --------------------- | ----------- | | Croatian | 0.755565 | | Welsh | 0.7528 | | Norwegian Nynorsk | 0.751648 | | Slovenian | 0.746007 | | French | 0.740772 | | Finnish | 0.738032 | | Czech | 0.737849 | | Hungarian | 0.736076 | | Dutch | 0.734977 | | Cornish | 0.733682 | | Danish | 0.733106 | | English | 0.733037 | | Irish | 0.732658 | | Portuguese | 0.727746 | | Spanish | 0.725111 | | Icelandic | 0.724427 | | Italian | 0.715839 | | Swedish | 0.715633 | | Polish | 0.715133 | | Lithuanian | 0.700003 | | Bulgarian | 0.694657 | | Romanian | 0.692957 | | Latin | 0.689022 | | Russian | 0.685847 | | Serbian | 0.674329 | | Slovak | 0.66739 | | Greek, Modern (1453-) | 0.632195 | | German | 0.631457 | | Indonesian | 0.6155 | | Norwegian | 0.597987 | Again, these numbers should be treated sceptically since some languages appear very infrequently. For example, the above table suggests the mean word confidence for Welsh is relatively high. However, there isn’t much Welsh in the dataset. Therefore, it is unlikely that this data will be particularly useful for training (historic) Welsh language models. [More Information Needed] ## Dataset Structure The dataset has a number of configurations relating to the different dates of publication in the underlying data: - `1500_1899`: this configuration covers all years - `1800_1899`: this configuration covers the years between 1800 and 1899 - `1700_1799`: this configuration covers the years between 1700 and 1799 - `1510_1699`: this configuration covers the years between 1510 and 1699 ### Configuration option All of the configurations have an optional keyword argument `skip_empty_pages` which is set to `True` by default. The underlying dataset includes some pages where there is no text. This could either be because the underlying book page didn't have any text or the OCR software failed to detect this text. For many uses of this dataset it doesn't make sense to include empty pages so these are skipped by default. However, for some uses you may prefer to retain a representation of the data that includes these empty pages. Passing `skip_empty_pages=False` when loading the dataset will enable this option. ### Data Instances An example data instance: ```python {'Country of publication 1': 'England', 'Language_1': 'English', 'Language_2': None, 'Language_3': None, 'Language_4': None, 'Physical description': None, 'Publisher': None, 'all Countries of publication': 'England', 'all names': 'Settle, Elkanah [person]', 'date': 1689, 'empty_pg': True, 'mean_wc_ocr': 0.0, 'multi_language': False, 'name': 'Settle, Elkanah', 'pg': 1, 'place': 'London', 'raw_date': '1689', 'record_id': '001876770', 'std_wc_ocr': 0.0, 'text': None, ‘title’: ‘The Female Prelate: being the history and the life and death of Pope Joan. A tragedy [in five acts and in verse] . Written by a Person of Quality [i.e. Elkanah Settle]’} ``` Each instance in the dataset represents a single page from an original digitised book. ### Data Fields Included in this dataset are: | Field | Data Type | Description | | ---------------------------- | --------- | ------------------------------------------------------------------------------------------------------------- | | record_id | string | British Library ID for the item | | date | int | parsed/normalised year for the item. i.e. 1850 | | raw_date | string | the original raw date for an item i.e. 1850- | | title | string | title of the book | | place | string | Place of publication, i.e. London | | empty_pg | bool | whether page contains text | | text | string | OCR generated text for a page | | pg | int | page in original book the instance refers to | | mean_wc_ocr | float | mean word confidence values for the page | | std_wc_ocr | float | standard deviation of the word confidence values for the page | | name | string | name associated with the item (usually author) | | all names | string | all names associated with a publication | | Publisher | string | publisher of the book | | Country of publication 1 | string | first country associated with publication | | all Countries of publication | string | all countries associated with a publication | | Physical description | string | physical description of the item (size). This requires some normalisation before use and isn’t always present | | Language_1 | string | first language associated with the book, this is usually present | | Language_2 | string | | | Language_3 | string | | | Language_4 | string | | | multi_language | bool | | Some of these fields are not populated a large proportion of the time. You can get some sense of this from this [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) [report](https://davanstrien.github.io/BL-datasets-pandas-profile-reports/pandas_profile_report_MS_digitised_books_2021-01-09.html) The majority of these fields relate to metadata about the books. Most of these fields were created by staff working for the British Library. The notable exception is the “Languages” fields that have sometimes been determined using computational methods. This work is reported in more detail in [Automated Language Identification of Bibliographic Resources](https://doi.org/10.1080/01639374.2019.1700201). It is important to note that metadata is neither perfect nor static. The metadata associated with this book was generated based on export from the British Library catalogue in 2021. [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation **Note** this section is a work in progress. ### Curation Rationale The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization), i.e. projects intending to quickly digitise large volumes of materials shape the selection of materials to include in several ways. Some considerations which are often involved in the decision of whether to include items for digitisation include (but are not limited to): - copyright status - preservation needs - the size of an item, very large and very small items are often hard to digitise quickly These criteria can have knock-on effects on the makeup of a collection. For example, systematically excluding large books may result in some types of book content not being digitised. Large volumes are likely to be correlated to content to at least some extent, so excluding them from digitisation will mean that material is underrepresented. Similarly, copyright status is often (but not only) determined by publication date. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date. All of the above is largely to make clear that this collection was not curated to create a representative sample of the British Library’s holdings. Some material will be over-represented, and others under-represented. Similarly, the collection should not be considered a representative sample of what was published across the period covered by the dataset (nor that the relative proportions of the data for each time period represent a proportional sample of publications from that period). Finally, and this probably does not need stating, the language included in the text should not be considered representative of either written or spoken language(s) from that time period. [More Information Needed] ### Source Data The source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. “Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It’s existed in English law since 1662.” [source](https://www.bl.uk/legal-deposit/about-legal-deposit). The source data for this version of the data is derived from the original ALTO XML files and a recent metadata export #TODO add links [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created using the original ALTO XML files and, where a match was found, updating the metadata associated with that item with more recent metadata using an export from the British Library catalogue. The process of creating this new dataset is documented here #TODO add link. There are a few decisions made in the above processing steps worth highlighting in particular: ##### Date normalization The metadata around date of publication for an item is not always exact. It often is represented as a date range e.g. `1850-1860`. The `date` field above takes steps to normalise this date to a single integer value. In most cases, this is taking the mean of the values associated with the item. The `raw_date` field includes the unprocessed date string. ##### Metadata included The metadata associated with each item includes most of the fields available via the ALTO XML. However, the data doesn’t include some metadata fields from the metadata export file. The reason fields were excluded because they are frequently not populated. A cut off of 50% was chosen, i.e. values from the metadata which are missing above 50% of the time were not included. This is slightly arbitrary, but since the aim of this version of the data was to support computational research using the collection it was felt that these fields with frequent missing values would be less valuable. #### Who are the source language producers? [More Information Needed] ### Annotations This dataset does not include annotations as usually understood in the context of NLP. The data does include metadata associated with the books. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data There a range of considerations around using the data. These include the representativeness of the dataset, the OCR quality and the language used. Depending on your use case, these may be more or less important. For example, the impact of OCR quality on downstream tasks will depend on the target task. It may also be possible to mitigate this negative impact from OCR through tokenizer choice, Language Model training objectives, oversampling high-quality OCR, etc. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historical text. As a result, the text will reflect this time period's social beliefs and attitudes. The books include both fiction and non-fiction books. Examples of book titles that appear in the data (these are randomly sampled from all titles): - ‘Rhymes and Dreams, Legends of Pendle Forest, and other poems’, - “Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General’s Department, Horse Guards, War Office, etc”, - ‘The fan. A poem’, - ‘Grif; a story of Australian Life’, - ‘Calypso; a masque: in three acts, etc’, - ‘Tales Uncle told [With illustrative woodcuts.]’, - 'Questings', - 'Home Life on an Ostrich Farm. With ... illustrations’, - ‘Bulgarya i Bulgarowie’, - 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc', - ‘The Corsair, a tale’, ‘Poems ... With notes [With a portrait.]’, - ‘Report of the Librarian for the year 1898 (1899, 1901, 1909)’, - “The World of Thought. A novel. By the author of ‘Before I began to speak.’”, - 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc'] While using titles alone is insufficient to integrate bias in this collection, it gives some insight into the topics covered by books. Further, the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list. #### Colonialism Even in the above random sample of titles examples of colonial attitudes, we can see examples of titles. We can try and interrogate this further by searching for the name of places that were part of the British Empire when many of these books were published. Searching for the string `India` in the titles and randomly sampling 10 titles returns: - “Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the ‘Calcutta Weekly Englishman.’”, - ‘A Winter in India and Malaysia among the Methodist Missions’, - “The Tourist’s Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition”, - ‘Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson’, - "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]”, - ‘The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies’, - “From Tonkin to India : by the sources of the Irawadi, January’ 95-January ’96”, - ‘Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844’, - ‘The Andaman Islands; their colonisation, etc. A correspondence addressed to the India Office’, - ‘Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle’] Searching form the string `Africa` in the titles and randomly sampling 10 titles returns: - ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada', - ‘To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]’, - ‘Diamonds and Gold in South Africa ... With maps, etc’, - ‘Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition’, - ‘A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts’, - ‘Side Lights on South Africa ... With a map, etc’, - ‘My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc’, - ‘Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations’, - ‘[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder’, - ‘Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc’] [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license. ### Citation Information ```bibtext @misc{bBritishLibraryBooks2021, author = {British Library Labs}, title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)}, year = {2021}, publisher = {British Library}, howpublished={https://doi.org/10.23636/r7w6-zy15} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

blbooksgenre

--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - de - en - fr - nl license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - text-classification - text-generation - fill-mask task_ids: - topic-classification - multi-label-classification - language-modeling - masked-language-modeling pretty_name: British Library Books Genre configs: - annotated_raw - raw - title_genre_classifiction dataset_info: - config_name: title_genre_classifiction features: - name: BL record ID dtype: string - name: title dtype: string - name: label dtype: class_label: names: '0': Fiction '1': Non-fiction splits: - name: train num_bytes: 187600 num_examples: 1736 download_size: 20111420 dataset_size: 187600 - config_name: annotated_raw features: - name: BL record ID dtype: string - name: Name dtype: string - name: Dates associated with name dtype: string - name: Type of name dtype: string - name: Role dtype: string - name: All names sequence: string - name: Title dtype: string - name: Variant titles dtype: string - name: Series title dtype: string - name: Number within series dtype: string - name: Country of publication sequence: string - name: Place of publication sequence: string - name: Publisher dtype: string - name: Date of publication dtype: string - name: Edition dtype: string - name: Physical description dtype: string - name: Dewey classification dtype: string - name: BL shelfmark dtype: string - name: Topics dtype: string - name: Genre dtype: string - name: Languages sequence: string - name: Notes dtype: string - name: BL record ID for physical resource dtype: string - name: classification_id dtype: string - name: user_id dtype: string - name: subject_ids dtype: string - name: annotator_date_pub dtype: string - name: annotator_normalised_date_pub dtype: string - name: annotator_edition_statement dtype: string - name: annotator_FAST_genre_terms dtype: string - name: annotator_FAST_subject_terms dtype: string - name: annotator_comments dtype: string - name: annotator_main_language dtype: string - name: annotator_other_languages_summaries dtype: string - name: annotator_summaries_language dtype: string - name: annotator_translation dtype: string - name: annotator_original_language dtype: string - name: annotator_publisher dtype: string - name: annotator_place_pub dtype: string - name: annotator_country dtype: string - name: annotator_title dtype: string - name: Link to digitised book dtype: string - name: annotated dtype: bool - name: Type of resource dtype: class_label: names: '0': Monograph '1': Serial - name: created_at dtype: timestamp[s] - name: annotator_genre dtype: class_label: names: '0': Fiction '1': Can't tell '2': Non-fiction '3': The book contains both Fiction and Non-Fiction splits: - name: train num_bytes: 3583138 num_examples: 4398 download_size: 20111420 dataset_size: 3583138 - config_name: raw features: - name: BL record ID dtype: string - name: Name dtype: string - name: Dates associated with name dtype: string - name: Type of name dtype: string - name: Role dtype: string - name: All names sequence: string - name: Title dtype: string - name: Variant titles dtype: string - name: Series title dtype: string - name: Number within series dtype: string - name: Country of publication sequence: string - name: Place of publication sequence: string - name: Publisher dtype: string - name: Date of publication dtype: string - name: Edition dtype: string - name: Physical description dtype: string - name: Dewey classification dtype: string - name: BL shelfmark dtype: string - name: Topics dtype: string - name: Genre dtype: string - name: Languages sequence: string - name: Notes dtype: string - name: BL record ID for physical resource dtype: string - name: classification_id dtype: string - name: user_id dtype: string - name: subject_ids dtype: string - name: annotator_date_pub dtype: string - name: annotator_normalised_date_pub dtype: string - name: annotator_edition_statement dtype: string - name: annotator_FAST_genre_terms dtype: string - name: annotator_FAST_subject_terms dtype: string - name: annotator_comments dtype: string - name: annotator_main_language dtype: string - name: annotator_other_languages_summaries dtype: string - name: annotator_summaries_language dtype: string - name: annotator_translation dtype: string - name: annotator_original_language dtype: string - name: annotator_publisher dtype: string - name: annotator_place_pub dtype: string - name: annotator_country dtype: string - name: annotator_title dtype: string - name: Link to digitised book dtype: string - name: annotated dtype: bool - name: Type of resource dtype: class_label: names: '0': Monograph '1': Serial '2': Monographic component part - name: created_at dtype: string - name: annotator_genre dtype: string splits: - name: train num_bytes: 27518816 num_examples: 55343 download_size: 20111420 dataset_size: 27518816 --- # Dataset Card for blbooksgenre ## Table of Contents - [Dataset Card for blbooksgenre](#dataset-card-for-blbooksgenre) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Colonialism](#colonialism) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**: [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312) - **Repository:** [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset consists of metadata relating to books [digitised by the British Library in partnership with Microsoft](https://www.bl.uk/collection-guides/google-books-digitised-printed-heritage). Some of this metadata was exported from the British Library catalogue whilst others was generated as part of a crowdsourcing project. The text of this book and other metadata can be found on the [date.bl](https://data.bl.uk/bl_labs_datasets/#3) website. The majority of the books in this collection were published in the 18th and 19th Century but the collection also includes a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas including geography, philosophy, history, poetry and literature and are published in a variety of languages. For the subsection of the data which contains additional crowsourced annotations the date of publication breakdown is as follows: | | Date of publication | | ---- | ------------------- | | 1630 | 8 | | 1690 | 4 | | 1760 | 10 | | 1770 | 5 | | 1780 | 5 | | 1790 | 18 | | 1800 | 45 | | 1810 | 96 | | 1820 | 152 | | 1830 | 182 | | 1840 | 259 | | 1850 | 400 | | 1860 | 377 | | 1870 | 548 | | 1880 | 776 | | 1890 | 1484 | | 1900 | 17 | | 1910 | 1 | | 1970 | 1 | [More Information Needed] ### Supported Tasks and Leaderboards The digitised books collection which this dataset describes has been used in a variety of digital history and humanities projects since being published. This dataset is suitable for a variety of unsupervised tasks and for a 'genre classification task'. #### Supervised tasks The main possible use case for this dataset is to develop and evaluate 'genre classification' models. The dataset includes human generated labels for whether a book is 'fiction' or 'non-fiction'. This has been used to train models for genre classifcation which predict whether a book is 'fiction' or 'non-fiction' based on its title. ### Languages [More Information Needed] ## Dataset Structure The dataset currently has three configurations intended to support a range of tasks for which this dataset could be used for: - `title_genre_classifiction` : this creates a de-duplicated version of the dataset with the `BL record`, `title` and `label`. - `annotated_raw`: This version of the dataset includes all fields from the original dataset which are annotated. This includes duplication from different annotators" - `raw`: This version of the dataset includes all the data from the original data including data without annotations. ### Data Instances An example data instance from the `title_genre_classifiction` config: ```python {'BL record ID': '014603046', 'title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'label': 0} ``` An example data instance from the `annotated_raw` config: ```python {'BL record ID': '014603046', 'Name': 'Yates, William Joseph H.', 'Dates associated with name': '', 'Type of name': 'person', 'Role': '', 'All names': ['Yates, William Joseph H. [person] ', ' Y, W. J. H. [person]'], 'Title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'Variant titles': '', 'Series title': '', 'Number within series': '', 'Country of publication': ['England'], 'Place of publication': ['London'], 'Publisher': '', 'Date of publication': '1879', 'Edition': '', 'Physical description': 'pages not numbered, 21 cm', 'Dewey classification': '', 'BL shelfmark': 'Digital Store 11601.f.36. (1.)', 'Topics': '', 'Genre': '', 'Languages': ['English'], 'Notes': 'In verse', 'BL record ID for physical resource': '004079262', 'classification_id': '267476823.0', 'user_id': '15.0', 'subject_ids': '44369003.0', 'annotator_date_pub': '1879', 'annotator_normalised_date_pub': '1879', 'annotator_edition_statement': 'NONE', 'annotator_FAST_genre_terms': '655 7 ‡aPoetry‡2fast‡0(OCoLC)fst01423828', 'annotator_FAST_subject_terms': '60007 ‡aAlice,‡cGrand Duchess, consort of Ludwig IV, Grand Duke of Hesse-Darmstadt,‡d1843-1878‡2fast‡0(OCoLC)fst00093827', 'annotator_comments': '', 'annotator_main_language': '', 'annotator_other_languages_summaries': 'No', 'annotator_summaries_language': '', 'annotator_translation': 'No', 'annotator_original_language': '', 'annotator_publisher': 'NONE', 'annotator_place_pub': 'London', 'annotator_country': 'enk', 'annotator_title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'Link to digitised book': 'http://access.bl.uk/item/viewer/ark:/81055/vdc_00000002842E', 'annotated': True, 'Type of resource': 0, 'created_at': datetime.datetime(2020, 8, 11, 14, 30, 33), 'annotator_genre': 0} ``` ### Data Fields The data fields differ slightly between configs. All possible fields for the `annotated_raw` config are listed below. For the `raw` version of the dataset datatypes are usually string to avoid errors when processing missing values. - `BL record ID`: an internal ID used by the British Library, this can be useful for linking this data to other BL collections. - `Name`: name associated with the item (usually author) - `Dates associated with name`: dates associated with above e.g. DOB - `Type of name`: whether `Name` is a person or an organization etc. - `Role`: i.e. whether `Name` is `author`, `publisher` etc. - `All names`: a fuller list of names associated with the item. - `Title`: The title of the work - `Variant titles` - `Series title` - `Number within series` - `Country of publication`: encoded as a list of countries listed in the metadata - `Place of publication`: encoded as a list of places listed in the metadata - `Publisher` - `Date of publication`: this is encoded as a string since this field can include data ranges i.e.`1850-1855`. - `Edition` - `Physical description`: encoded as a string since the format of this field varies - `Dewey classification` - `BL shelfmark`: a British Library shelf mark - `Topics`: topics included in the catalogue record - `Genre` the genre information included in the original catalogue record note that this is often missing - `Languages`; encoded as a list of languages - `Notes`: notes from the catalogue record - `BL record ID for physical resource` The following fields are all generated via the crowdsourcing task (discussed in more detail below) - `classification_id`: ID for the classification in the annotation task - `user_id` ID for the annotator - `subject_ids`: internal annotation task ID - `annotator_date_pub`: an updated publication data - `annotator_normalised_date_pub`: normalized version of the above - `annotator_edition_statement` updated edition - `annotator_FAST_genre_terms`: [FAST classification genre terms](https://www.oclc.org/research/areas/data-science/fast.html) - `annotator_FAST_subject_terms`: [FAST subject terms](https://www.oclc.org/research/areas/data-science/fast.html) - `annotator_comments`: free form comments - `annotator_main_language` - `annotator_other_languages_summaries` - `'annotator_summaries_language` - `annotator_translation` - `annotator_original_language` - `annotator_publisher` - `annotator_place_pub` - `annotator_country` - `annotator_title` - `Link to digitised book` - `annotated`: `bool` flag to indicate if row has annotations or not - `created_at`: when the annotation was created - `annotator_genre`: the updated annotation for the `genre` of the book. Finally the `label` field of the `title_genre_classifiction` configuration is a class label with values 0 (Fiction) or 1 (Non-fiction). [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation **Note** this section is a work in progress. ### Curation Rationale The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization) i.e. projects where there is a goal to quickly digitise large volumes of materials shape the selection of materials to include in a number of ways. Some consideratoins which are often involved in the decision of whether to include items for digitization include (but are not limited to): - copyright status - preservation needs- the size of an item, very large and very small items are often hard to digitize quickly These criteria can have knock-on effects on the makeup of a collection. For example systematically excluding large books may result in some types of book content not being digitized. Large volumes are likely to be correlated to content to at least some extent so excluding them from digitization will mean that material is under represented. Similarly copyright status is often (but not only) determined by publication data. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date. All of the above is largely to make clear that this collection was not curated with the aim of creating a representative sample of the British Library's holdings. Some material will be over-represented and other under-represented. Similarly, the collection should not be considered a representative sample of what was published across the time period covered by the dataset (nor that that the relative proportions of the data for each time period represent a proportional sample of publications from that period). [More Information Needed] ### Source Data The original source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. "Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It's existed in English law since 1662."[source](https://www.bl.uk/legal-deposit/about-legal-deposit). [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created partially from data exported from British Library catalogue records and partially via data generated from a crowdsourcing task involving British Library staff. #### Who are the source language producers? [More Information Needed] ### Annotations The data does includes metadata associated with the books these are produced by British Library staff. The additional annotations were carried out during 2020 as part of an internal crowdsourcing task. #### Annotation process New annotations were produced via a crowdsourcing tasks. Annotators have the option to pick titles from a particular language subset from the broader digitized 19th century books collection. As a result the annotations are not random and overrepresent some languages. [More Information Needed] #### Who are the annotators? Staff working at the British Library. Most of these staff work with metadata as part of their jobs and so could be considered expert annotators. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data There a range of considerations around using the data. These include the representativeness of the dataset, the bias towards particular languages etc. It is also important to note that library metadata is not static. The metadata held in library catalogues is updated and changed over time for a variety of reasons. The way in which different institutions catalogue items also varies. As a result it is important to evaluate the performance of any models trained on this data before applying to a new collection. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historic text. As a result the text will reflect to social beliefs and attitudes of this time period. The titles of the book give some sense of their content. Examples of book titles which appear in the data (these are randomly sampled from all titles): - 'Rhymes and Dreams, Legends of Pendle Forest, and other poems', - "Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General's Department, Horse Guards, War Office, etc", - 'The fan. A poem', - 'Grif; a story of Australian Life', - 'Calypso; a masque: in three acts, etc', - 'Tales Uncle told [With illustrative woodcuts.]', - 'Questings', - 'Home Life on an Ostrich Farm. With ... illustrations', - 'Bulgarya i Bulgarowie', - 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc', - 'The Corsair, a tale', 'Poems ... With notes [With a portrait.]', - 'Report of the Librarian for the year 1898 (1899, 1901, 1909)', - "The World of Thought. A novel. By the author of 'Before I began to speak.'", - 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc'] Whilst using titles alone, is obviously insufficient to integrate bias in this collection it gives some insight into the topics covered by books in the corpus. Further looking into the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list. #### Colonialism We can see even in the above random sample of titles examples of colonial attitudes. We can try and interrogate this further by searching for the name of countries which were part of the British Empire at the time many of these books were published. Searching for the string `India` in the titles and randomly sampling 10 titles returns: - "Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the 'Calcutta Weekly Englishman.'", - 'A Winter in India and Malaysia among the Methodist Missions', - "The Tourist's Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition", - 'Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson', - "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]", - 'The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies', - "From Tonkin to India : by the sources of the Irawadi, January '95-January '96", - 'Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844', - 'The Andaman Islands; their colonization, etc. A correspondence addressed to the India Office', - 'Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle'] Searching form the string `Africa` in the titles and randomly sampling 10 titles returns: - ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada', - 'To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]', - 'Diamonds and Gold in South Africa ... With maps, etc', - 'Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition', - 'A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts', - 'Side Lights on South Africa ... With a map, etc', - 'My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc', - 'Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations', - '[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder', - 'Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc'] Whilst this dataset doesn't include the underlying text it is important to consider the potential attitudes represented in the title of the books, or the full text if you are using this dataset in conjunction with the full text. [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license. ### Citation Information ```bibtex @misc{british library_genre, title={ 19th Century Books - metadata with additional crowdsourced annotations}, url={https://doi.org/10.23636/BKHQ-0312}, author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi}, year={2021}} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

blended_skill_talk

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual pretty_name: BlendedSkillTalk size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation paperswithcode_id: blended-skill-talk dataset_info: features: - name: personas sequence: string - name: additional_context dtype: string - name: previous_utterance sequence: string - name: context dtype: string - name: free_messages sequence: string - name: guided_messages sequence: string - name: suggestions sequence: - name: convai2 dtype: string - name: empathetic_dialogues dtype: string - name: wizard_of_wikipedia dtype: string - name: guided_chosen_suggestions sequence: string - name: label_candidates sequence: sequence: string splits: - name: train num_bytes: 10831361 num_examples: 4819 - name: validation num_bytes: 43961658 num_examples: 1009 - name: test num_bytes: 44450102 num_examples: 980 download_size: 38101408 dataset_size: 99243121 --- # Dataset Card for "blended_skill_talk" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://parl.ai/projects/bst/](https://parl.ai/projects/bst/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 38.11 MB - **Size of the generated dataset:** 15.08 MB - **Total amount of disk used:** 53.17 MB ### Dataset Summary A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 38.11 MB - **Size of the generated dataset:** 15.08 MB - **Total amount of disk used:** 53.17 MB An example of 'train' looks as follows. ``` { 'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'], 'additional_context': 'Backstreet Boys', 'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "], 'context': 'wizard_of_wikipedia', 'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "], 'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'], 'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']}, 'guided_chosen_suggestions': ['convai2', '', ''], 'label_candidates': []} ``` ### Data Fields The data fields are the same among all splits. #### default - `personas`: a `list` of `string` features. - `additional_context`: a `string` feature. - `previous_utterance`: a `list` of `string` features. - `context`: a `string` feature. - `free_messages`: a `list` of `string` features. - `guided_messgaes`: a `list` of `string` features. - `suggestions`: a dictionary feature containing: - `convai2`: a `string` feature. - `empathetic_dialogues`: a `string` feature. - `wizard_of_wikipedia`: a `string` feature. - `guided_chosen_suggestions`: a `list` of `string` features. - `label_candidates`: a `list` of `lists` of `string` features. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 4819| 1009| 980| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{smith2020evaluating, title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills}, author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau}, year={2020}, eprint={2004.08449}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

blimp

--- annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: cc-by-4.0 multilinguality: - monolingual pretty_name: BLiMP size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - acceptability-classification paperswithcode_id: blimp dataset_info: - config_name: adjunct_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 167289 num_examples: 1000 download_size: 359284 dataset_size: 167289 - config_name: anaphor_gender_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 132313 num_examples: 1000 download_size: 436749 dataset_size: 132313 - config_name: anaphor_number_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 141274 num_examples: 1000 download_size: 450861 dataset_size: 141274 - config_name: animate_subject_passive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 145818 num_examples: 1000 download_size: 462292 dataset_size: 145818 - config_name: animate_subject_trans features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 129193 num_examples: 1000 download_size: 433098 dataset_size: 129193 - config_name: causative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 124167 num_examples: 1000 download_size: 317162 dataset_size: 124167 - config_name: complex_NP_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 200367 num_examples: 1000 download_size: 392362 dataset_size: 200367 - config_name: coordinate_structure_constraint_complex_left_branch features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 212307 num_examples: 1000 download_size: 571696 dataset_size: 212307 - config_name: coordinate_structure_constraint_object_extraction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173050 num_examples: 1000 download_size: 366045 dataset_size: 173050 - config_name: determiner_noun_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 157515 num_examples: 1000 download_size: 468642 dataset_size: 157515 - config_name: determiner_noun_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 157599 num_examples: 1000 download_size: 488856 dataset_size: 157599 - config_name: determiner_noun_agreement_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165868 num_examples: 1000 download_size: 474932 dataset_size: 165868 - config_name: determiner_noun_agreement_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 162469 num_examples: 1000 download_size: 490854 dataset_size: 162469 - config_name: determiner_noun_agreement_with_adj_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 181061 num_examples: 1000 download_size: 526806 dataset_size: 181061 - config_name: determiner_noun_agreement_with_adj_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 185924 num_examples: 1000 download_size: 499664 dataset_size: 185924 - config_name: determiner_noun_agreement_with_adj_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 185791 num_examples: 1000 download_size: 528528 dataset_size: 185791 - config_name: determiner_noun_agreement_with_adjective_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 186521 num_examples: 1000 download_size: 504676 dataset_size: 186521 - config_name: distractor_agreement_relational_noun features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 192868 num_examples: 1000 download_size: 525650 dataset_size: 192868 - config_name: distractor_agreement_relative_clause features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 218151 num_examples: 1000 download_size: 564770 dataset_size: 218151 - config_name: drop_argument features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 111201 num_examples: 1000 download_size: 304196 dataset_size: 111201 - config_name: ellipsis_n_bar_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 218985 num_examples: 1000 download_size: 411980 dataset_size: 218985 - config_name: ellipsis_n_bar_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 234556 num_examples: 1000 download_size: 427551 dataset_size: 234556 - config_name: existential_there_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 225136 num_examples: 1000 download_size: 550672 dataset_size: 225136 - config_name: existential_there_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 164326 num_examples: 1000 download_size: 357321 dataset_size: 164326 - config_name: existential_there_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 166221 num_examples: 1000 download_size: 359216 dataset_size: 166221 - config_name: existential_there_subject_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 201458 num_examples: 1000 download_size: 394453 dataset_size: 201458 - config_name: expletive_it_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 240010 num_examples: 1000 download_size: 587648 dataset_size: 240010 - config_name: inchoative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 105714 num_examples: 1000 download_size: 298709 dataset_size: 105714 - config_name: intransitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 112492 num_examples: 1000 download_size: 305487 dataset_size: 112492 - config_name: irregular_past_participle_adjectives features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 146056 num_examples: 1000 download_size: 444520 dataset_size: 146056 - config_name: irregular_past_participle_verbs features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 127087 num_examples: 1000 download_size: 420119 dataset_size: 127087 - config_name: irregular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 166979 num_examples: 1000 download_size: 460705 dataset_size: 166979 - config_name: irregular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 155238 num_examples: 1000 download_size: 453376 dataset_size: 155238 - config_name: left_branch_island_echo_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 149235 num_examples: 1000 download_size: 482617 dataset_size: 149235 - config_name: left_branch_island_simple_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 151455 num_examples: 1000 download_size: 343450 dataset_size: 151455 - config_name: matrix_question_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 154657 num_examples: 1000 download_size: 457806 dataset_size: 154657 - config_name: npi_present_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 139860 num_examples: 1000 download_size: 438013 dataset_size: 139860 - config_name: npi_present_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 129031 num_examples: 1000 download_size: 422136 dataset_size: 129031 - config_name: only_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 149911 num_examples: 1000 download_size: 459170 dataset_size: 149911 - config_name: only_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 210297 num_examples: 1000 download_size: 583720 dataset_size: 210297 - config_name: passive_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 147277 num_examples: 1000 download_size: 340272 dataset_size: 147277 - config_name: passive_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 115355 num_examples: 1000 download_size: 308350 dataset_size: 115355 - config_name: principle_A_c_command features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 189885 num_examples: 1000 download_size: 527689 dataset_size: 189885 - config_name: principle_A_case_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171793 num_examples: 1000 download_size: 477239 dataset_size: 171793 - config_name: principle_A_case_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171807 num_examples: 1000 download_size: 492973 dataset_size: 171807 - config_name: principle_A_domain_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 172565 num_examples: 1000 download_size: 499865 dataset_size: 172565 - config_name: principle_A_domain_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 166728 num_examples: 1000 download_size: 493189 dataset_size: 166728 - config_name: principle_A_domain_3 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160393 num_examples: 1000 download_size: 513886 dataset_size: 160393 - config_name: principle_A_reconstruction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153499 num_examples: 1000 download_size: 345494 dataset_size: 153499 - config_name: regular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160214 num_examples: 1000 download_size: 451850 dataset_size: 160214 - config_name: regular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 155004 num_examples: 1000 download_size: 456477 dataset_size: 155004 - config_name: sentential_negation_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173259 num_examples: 1000 download_size: 490996 dataset_size: 173259 - config_name: sentential_negation_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 233493 num_examples: 1000 download_size: 614930 dataset_size: 233493 - config_name: sentential_subject_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173827 num_examples: 1000 download_size: 365822 dataset_size: 173827 - config_name: superlative_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160685 num_examples: 1000 download_size: 381189 dataset_size: 160685 - config_name: superlative_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 160735 num_examples: 1000 download_size: 516120 dataset_size: 160735 - config_name: tough_vs_raising_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 150031 num_examples: 1000 download_size: 343026 dataset_size: 150031 - config_name: tough_vs_raising_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171079 num_examples: 1000 download_size: 364074 dataset_size: 171079 - config_name: transitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 134499 num_examples: 1000 download_size: 460291 dataset_size: 134499 - config_name: wh_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 143735 num_examples: 1000 download_size: 448630 dataset_size: 143735 - config_name: wh_questions_object_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 194440 num_examples: 1000 download_size: 387435 dataset_size: 194440 - config_name: wh_questions_subject_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 196988 num_examples: 1000 download_size: 389983 dataset_size: 196988 - config_name: wh_questions_subject_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 269665 num_examples: 1000 download_size: 462660 dataset_size: 269665 - config_name: wh_vs_that_no_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 190267 num_examples: 1000 download_size: 383262 dataset_size: 190267 - config_name: wh_vs_that_no_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 248434 num_examples: 1000 download_size: 441429 dataset_size: 248434 - config_name: wh_vs_that_with_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 174781 num_examples: 1000 download_size: 367776 dataset_size: 174781 - config_name: wh_vs_that_with_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 232990 num_examples: 1000 download_size: 425985 dataset_size: 232990 --- # Dataset Card for "blimp" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/alexwarstadt/blimp/tree/master/](https://github.com/alexwarstadt/blimp/tree/master/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 29.58 MB - **Size of the generated dataset:** 11.45 MB - **Total amount of disk used:** 41.03 MB ### Dataset Summary BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### adjunct_island - **Size of downloaded dataset files:** 0.36 MB - **Size of the generated dataset:** 0.17 MB - **Total amount of disk used:** 0.52 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_gender_agreement - **Size of downloaded dataset files:** 0.44 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_number_agreement - **Size of downloaded dataset files:** 0.45 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.59 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_passive - **Size of downloaded dataset files:** 0.46 MB - **Size of the generated dataset:** 0.15 MB - **Total amount of disk used:** 0.61 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_trans - **Size of downloaded dataset files:** 0.43 MB - **Size of the generated dataset:** 0.13 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` ### Data Fields The data fields are the same among all splits. #### adjunct_island - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_gender_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_number_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_passive - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_trans - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. ### Data Splits | name |train| |------------------------|----:| |adjunct_island | 1000| |anaphor_gender_agreement| 1000| |anaphor_number_agreement| 1000| |animate_subject_passive | 1000| |animate_subject_trans | 1000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{warstadt2019blimp, title={BLiMP: A Benchmark of Linguistic Minimal Pairs for English}, author={Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei, and Wang, Sheng-Fu and Bowman, Samuel R}, journal={arXiv preprint arXiv:1912.00582}, year={2019} } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

blog_authorship_corpus

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual paperswithcode_id: blog-authorship-corpus pretty_name: Blog Authorship Corpus size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification dataset_info: features: - name: text dtype: string - name: date dtype: string - name: gender dtype: string - name: age dtype: int32 - name: horoscope dtype: string - name: job dtype: string config_name: blog_authorship_corpus splits: - name: train num_bytes: 753833081 num_examples: 689793 - name: validation num_bytes: 41236028 num_examples: 37919 download_size: 632898892 dataset_size: 795069109 --- # Dataset Card for Blog Authorship Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 312.95 MB - **Size of the generated dataset:** 647.76 MB - **Total amount of disk used:** 960.71 MB ### Dataset Summary The Blog Authorship Corpus consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. The corpus incorporates a total of 681,288 posts and over 140 million words - or approximately 35 posts and 7250 words per person. Each blog is presented as a separate file, the name of which indicates a blogger id# and the blogger’s self-provided gender, age, industry and astrological sign. (All are labeled for gender and age but for many, industry and/or sign is marked as unknown.) All bloggers included in the corpus fall into one of three age groups: - 8240 "10s" blogs (ages 13-17), - 8086 "20s" blogs (ages 23-27), - 2994 "30s" blogs (ages 33-47). For each age group there are an equal number of male and female bloggers. Each blog in the corpus includes at least 200 occurrences of common English words. All formatting has been stripped with two exceptions. Individual posts within a single blogger are separated by the date of the following post and links within a post are denoted by the label urllink. The corpus may be freely used for non-commercial research purposes. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The language of the dataset is English (`en`). ## Dataset Structure ### Data Instances #### blog-authorship-corpus - **Size of downloaded dataset files:** 312.95 MB - **Size of the generated dataset:** 647.76 MB - **Total amount of disk used:** 960.71 MB An example of 'validation' looks as follows. ``` { "age": 23, "date": "27,July,2003", "gender": "female", "horoscope": "Scorpion", "job": "Student", "text": "This is a second test file." } ``` ### Data Fields The data fields are the same among all splits. #### blog-authorship-corpus - `text`: a `string` feature. - `date`: a `string` feature. - `gender`: a `string` feature. - `age`: a `int32` feature. - `horoscope`: a `string` feature. - `job`: a `string` feature. ### Data Splits | name |train |validation| |----------------------|-----:|---------:| |blog-authorship-corpus|532812| 31277| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The corpus may be freely used for non-commercial research purposes. ### Citation Information ``` @inproceedings{schler2006effects, title={Effects of age and gender on blogging.}, author={Schler, Jonathan and Koppel, Moshe and Argamon, Shlomo and Pennebaker, James W}, booktitle={AAAI spring symposium: Computational approaches to analyzing weblogs}, volume={6}, pages={199--205}, year={2006} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

bn_hate_speech

--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - bn license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: bengali-hate-speech pretty_name: Bengali Hate Speech Dataset tags: - hate-speech-topic-classification dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Personal '1': Political '2': Religious '3': Geopolitical '4': Gender abusive splits: - name: train num_bytes: 972635 num_examples: 3418 download_size: 974312 dataset_size: 972635 --- # Dataset Card for Bengali Hate Speech Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Bengali Hate Speech Dataset](https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset) - **Repository:** [Bengali Hate Speech Dataset](https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset) - **Paper:** [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://arxiv.org/abs/2004.07807) - **Point of Contact:** [Md. Rezaul Karim]([email protected]) ### Dataset Summary The Bengali Hate Speech Dataset is a Bengali-language dataset of news articles collected from various Bengali media sources and categorized based on the type of hate in the text. The dataset was created to provide greater support for under-resourced languages like Bengali on NLP tasks, and serves as a benchmark for multiple types of classification tasks. ### Supported Tasks and Leaderboards * `topic classification`: The dataset can be used to train a Multichannel Convolutional-LSTM for classifying different types of hate speech. The model performance can be measured by its F1 score. ### Languages The text in the dataset is in Bengali and the associated BCP-47 code is `bn`. ## Dataset Structure ### Data Instances A data instance takes the form of a news article and its associated label. 🚨 Beware that the following example contains extremely offensive content! An example looks like this: ``` {"text": "রেন্ডিয়াকে পৃথীবির মানচিএ থেকে মুচে ফেলতে হবে", "label": "Geopolitical"} ``` ### Data Fields * `text`: the text of the Bengali news article * `label`: one of `Geopolitical`, `Personal`, `Political`, `Religious`, or `Gender abusive` indicating the type of hate speech ### Data Splits The dataset has 3418 examples. ## Dataset Creation ### Curation Rationale Under-resourced languages like Bengali lack supporting resources that languages like English have. This dataset was collected from multiple Bengali news sources to provide several classification benchmarks for hate speech detection, document classification and sentiment analysis. ### Source Data #### Initial Data Collection and Normalization Bengali articles were collected from a Bengali Wikipedia dump, Bengali news articles, news dumps of TV channels, books, blogs, sports portal and social media. Emphasis was placed on Facebook pages and newspaper sources because they have about 50 million followers and is a common source of opinion and hate speech. The full dataset consists of 250 million articles and is currently being prepared. This is a subset of the full dataset. #### Who are the source language producers? The source language producers are Bengali authors and users who interact with these various forms of Bengali media. ### Annotations #### Annotation process The data was annotated by manually identifying freqently occurring terms in texts containing hate speech and references to specific entities. The authors also prepared normalized frequency vectors of 175 abusive terms that are commonly used to express hate in Bengali. A hate label is assigned if at least one of these terms exists in the text. Annotator's were provided with unbiased text only contents to make the decision. Non-hate statements were removed from the list and the category of hate was further divided into political, personal, gender abusive, geopolitical and religious. To reduce possible bias, each label was assigned based on a majority voting on the annotator's opinions and Cohen's Kappa was computed to measure inter-annotator agreement. #### Who are the annotators? Three native Bengali speakers and two linguists annotated the dataset which was then reviewed and validated by three experts (one South Asian linguist and two native speakers). ### Personal and Sensitive Information The dataset contains very sensitive and highly offensive comments in a religious, political and gendered context. Some of the comments are directed towards contemporary public figures like politicians, religious leaders, celebrities and athletes. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of the dataset is to improve hate speech detection in Bengali. The growth of social media has enabled people to express hate freely online and there has been a lot of focus on detecting hate speech for highly resourced languages like English. The use of hate speech is pervasive, like any other major language, which can have serious and deadly consequences. Failure to react to hate speech renders targeted minorities more vulnerable to attack and it can also create indifference towards their treatment from majority populations. ### Discussion of Biases The dataset was collected using a bootstrapping approach. An initial search was made for specific types of texts, articles and tweets containing common harassment directed at targeting characteristics. As a result, this dataset contains **extremely** offensive content that is disturbing. In addition, Facebook pages and newspaper sources were emphasized because they are well-known for having hate and harassment issues. ### Other Known Limitations The dataset contains racist, sexist, homophobic and offensive comments. It is collected and annotated for research related purposes only. ## Additional Information ### Dataset Curators The dataset was curated by Md. Rezaul Karim, Sumon Kanti Dey, Bharathi Raja Chakravarthi, John McCrae and Michael Cochez. ### Licensing Information This dataset is licensed under the MIT License. ### Citation Information ``` @inproceedings{karim2020BengaliNLP, title={Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network}, author={Karim, Md. Rezaul and Chakravarti, Bharathi Raja and P. McCrae, John and Cochez, Michael}, booktitle={7th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA,2020)}, publisher={IEEE}, year={2020} } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.

bnl_newspapers

--- annotations_creators: - no-annotation language_creators: - found language: - ar - da - de - fi - fr - lb - nl - pt license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling pretty_name: BnL Historical Newspapers dataset_info: features: - name: id dtype: string - name: source dtype: string - name: url dtype: string - name: title dtype: string - name: ispartof dtype: string - name: text dtype: string - name: pub_date dtype: timestamp[s] - name: publisher dtype: string - name: language dtype: string - name: article_type dtype: class_label: names: '0': ADVERTISEMENT_SECTION '1': BIBLIOGRAPHY '2': CHAPTER '3': INDEX '4': CONTRIBUTION '5': TABLE_OF_CONTENTS '6': WEATHER '7': SHIPPING '8': SECTION '9': ARTICLE '10': TITLE_SECTION '11': DEATH_NOTICE '12': SUPPLEMENT '13': TABLE '14': ADVERTISEMENT '15': CHART_DIAGRAM '16': ILLUSTRATION '17': ISSUE - name: extent dtype: int32 config_name: processed splits: - name: train num_bytes: 1611620156 num_examples: 537558 download_size: 1224029060 dataset_size: 1611620156 --- # Dataset Card for BnL Historical Newspapers ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://data.bnl.lu/data/historical-newspapers/ - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [email protected] ### Dataset Summary The BnL has digitised over 800.000 pages of Luxembourg newspapers. This dataset currently has one configuration covering a subset of these newspapers, which sit under the "Processed Datasets" collection. The BNL: > processed all newspapers and monographs that are in the public domain and extracted the full text and associated meta data of every single article, section, advertisement… The result is a large number of small, easy to use XML files formatted using Dublin Core. [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure The dataset currently contains a single configuration. ### Data Instances An example instance from the datasets: ``` python {'id': 'https://persist.lu/ark:/70795/wx8r4c/articles/DTL47', 'article_type': 8, 'extent': 49, 'ispartof': 'Luxemburger Wort', 'pub_date': datetime.datetime(1853, 3, 23, 0, 0), 'publisher': 'Verl. der St-Paulus-Druckerei', 'source': 'newspaper/luxwort/1853-03-23', 'text': 'Asien. Eine neue Nedcrland-Post ist angekommen mil Nachrichten aus Calcutta bis zum 5. Febr.; Vom» vay, 12. Febr. ; Nangun und HongKong, 13. Jan. Die durch die letzte Post gebrachle Nachricht, der König von Ava sei durch seinen Bruder enlhronl worden, wird bestätigt. (K. Z.) Verantwortl. Herausgeber, F. Schümann.', 'title': 'Asien.', 'url': 'http://www.eluxemburgensia.lu/webclient/DeliveryManager?pid=209701#panel:pp|issue:209701|article:DTL47', 'language': 'de' } ``` ### Data Fields - 'id': This is a unique and persistent identifier using ARK. - 'article_type': The type of the exported data, possible values ('ADVERTISEMENT_SECTION', 'BIBLIOGRAPHY', 'CHAPTER', 'INDEX', 'CONTRIBUTION', 'TABLE_OF_CONTENTS', 'WEATHER', 'SHIPPING', 'SECTION', 'ARTICLE', 'TITLE_SECTION', 'DEATH_NOTICE', 'SUPPLEMENT', 'TABLE', 'ADVERTISEMENT', 'CHART_DIAGRAM', 'ILLUSTRATION', 'ISSUE') - 'extent': The number of words in the text field - 'ispartof: The complete title of the source document e.g. “Luxemburger Wort”. - 'pub_date': The publishing date of the document e.g “1848-12-15” - 'publisher':The publisher of the document e.g. “Verl. der St-Paulus-Druckerei”. - 'source': Describes the source of the document. For example <dc:source>newspaper/luxwort/1848-12-15</dc:source> means that this article comes from the newspaper “luxwort” (ID for Luxemburger Wort) issued on 15.12.1848. - 'text': The full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines. - 'title': The main title of the article, section, advertisement, etc. - 'url': The link to the BnLViewer on eluxemburgensia.lu to view the resource online. - 'language': The language of the text, possible values ('ar', 'da', 'de', 'fi', 'fr', 'lb', 'nl', 'pt') ### Data Splits This dataset contains a single split `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{bnl_newspapers, title={Historical Newspapers}, url={https://data.bnl.lu/data/historical-newspapers/}, author={ Bibliothèque nationale du Luxembourg}, ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

bookcorpus

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: BookCorpus size_categories: - 10M<n<100M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: bookcorpus dataset_info: features: - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 4853859824 num_examples: 74004228 download_size: 1179510242 dataset_size: 4853859824 --- # Dataset Card for BookCorpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://yknzhu.wixsite.com/mbweb](https://yknzhu.wixsite.com/mbweb) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.18 GB - **Size of the generated dataset:** 4.85 GB - **Total amount of disk used:** 6.03 GB ### Dataset Summary Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 1.18 GB - **Size of the generated dataset:** 4.85 GB - **Total amount of disk used:** 6.03 GB An example of 'train' looks as follows. ``` { "text": "But I traded all my life for some lovin' and some gold" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits | name | train | |----------|-------:| |plain_text|74004228| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The books have been crawled from https://www.smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information. A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241). ### Citation Information ``` @InProceedings{Zhu_2015_ICCV, title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books}, author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@richarddwang](https://github.com/richarddwang), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

bookcorpusopen

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: BookCorpusOpen size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: bookcorpus dataset_info: features: - name: title dtype: string - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 6643435392 num_examples: 17868 download_size: 2404269430 dataset_size: 6643435392 --- # Dataset Card for BookCorpusOpen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/soskek/bookcorpus/issues/27](https://github.com/soskek/bookcorpus/issues/27) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.40 GB - **Size of the generated dataset:** 6.64 GB - **Total amount of disk used:** 9.05 GB ### Dataset Summary Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This version of bookcorpus has 17868 dataset items (books). Each item contains two fields: title and text. The title is the name of the book (just the file name) while text contains unprocessed book text. The bookcorpus has been prepared by Shawn Presser and is generously hosted by The-Eye. The-Eye is a non-profit, community driven platform dedicated to the archiving and long-term preservation of any and all data including but by no means limited to... websites, books, games, software, video, audio, other digital-obscura and ideas. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 2.40 GB - **Size of the generated dataset:** 6.64 GB - **Total amount of disk used:** 9.05 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\"\\n\\nzONE\\n\\n## The end and the beginning\\n\\nby\\n\\nPhilip F. Blood\\n\\nSMASHWORDS EDITION\\n\\nVersion 3.55\\n\\nPUBLISHED BY:\\n\\nPhi...", "title": "zone-the-end-and-the-beginning.epub.txt" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `title`: a `string` feature. - `text`: a `string` feature. ### Data Splits | name |train| |----------|----:| |plain_text|17868| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The books have been crawled from smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information. A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241) ### Citation Information ``` @InProceedings{Zhu_2015_ICCV, title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books}, author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} } ``` ### Contributions Thanks to [@vblagoje](https://github.com/vblagoje) for adding this dataset.

boolq

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: boolq pretty_name: BoolQ dataset_info: features: - name: question dtype: string - name: answer dtype: bool - name: passage dtype: string splits: - name: train num_bytes: 5829592 num_examples: 9427 - name: validation num_bytes: 1998190 num_examples: 3270 download_size: 8764539 dataset_size: 7827782 --- # Dataset Card for Boolq ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/google-research-datasets/boolean-questions](https://github.com/google-research-datasets/boolean-questions) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 8.77 MB - **Size of the generated dataset:** 7.83 MB - **Total amount of disk used:** 16.59 MB ### Dataset Summary BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 8.77 MB - **Size of the generated dataset:** 7.83 MB - **Total amount of disk used:** 16.59 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answer": false, "passage": "\"All biomass goes through at least some of these steps: it needs to be grown, collected, dried, fermented, distilled, and burned...", "question": "does ethanol take more energy make that produces" } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `answer`: a `bool` feature. - `passage`: a `string` feature. ### Data Splits | name |train|validation| |-------|----:|---------:| |default| 9427| 3270| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information BoolQ is released under the [Creative Commons Share-Alike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @inproceedings{clark2019boolq, title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions}, author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina}, booktitle = {NAACL}, year = {2019}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

bprec

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - pl license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-retrieval task_ids: - entity-linking-retrieval pretty_name: bprec dataset_info: - config_name: default features: - name: id dtype: int32 - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: tele num_bytes: 2739015 num_examples: 2391 - name: electro num_bytes: 125999 num_examples: 382 - name: cosmetics num_bytes: 1565263 num_examples: 2384 - name: banking num_bytes: 446944 num_examples: 561 download_size: 8006167 dataset_size: 4877221 - config_name: all features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 4937658 num_examples: 5718 download_size: 8006167 dataset_size: 4937658 - config_name: tele features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 2758147 num_examples: 2391 download_size: 4569708 dataset_size: 2758147 - config_name: electro features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 130205 num_examples: 382 download_size: 269917 dataset_size: 130205 - config_name: cosmetics features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 1596259 num_examples: 2384 download_size: 2417388 dataset_size: 1596259 - config_name: banking features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 453119 num_examples: 561 download_size: 749154 dataset_size: 453119 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [bprec homepage](https://clarin-pl.eu/dspace/handle/11321/736) - **Repository:** [bprec repository](https://gitlab.clarin-pl.eu/team-semantics/semrel-extraction) - **Paper:** [bprec paper](https://www.aclweb.org/anthology/2020.lrec-1.233.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Brand-Product Relation Extraction Corpora in Polish ### Supported Tasks and Leaderboards NER, Entity linking ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - id: int identifier of a text - text: string text, for example a consumer comment on the social media - ner: extracted entities and their relationship - source and target: a pair of entities identified in the text - from: int value representing starting character of the entity - text: string value with the entity text - to: int value representing end character of the entity - type: one of pre-identified entity types: - PRODUCT_NAME - PRODUCT_NAME_IMP - PRODUCT_NO_BRAND - BRAND_NAME - BRAND_NAME_IMP - VERSION - PRODUCT_ADJ - BRAND_ADJ - LOCATION - LOCATION_IMP ### Data Splits No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts: - tele - electro - cosmetics - banking ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{inproceedings, author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka}, year = {2020}, month = {05}, pages = {}, title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations} } ``` ### Contributions Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset.

break_data

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: - open-domain-abstractive-qa paperswithcode_id: break pretty_name: BREAK dataset_info: - config_name: QDMR-high-level features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string splits: - name: test num_bytes: 482339 num_examples: 3195 - name: train num_bytes: 5148086 num_examples: 17503 - name: validation num_bytes: 914780 num_examples: 3130 download_size: 15971078 dataset_size: 6545205 - config_name: QDMR-high-level-lexicon features: - name: source dtype: string - name: allowed_tokens dtype: string splits: - name: test num_bytes: 4240755 num_examples: 3195 - name: train num_bytes: 23234518 num_examples: 17503 - name: validation num_bytes: 4158679 num_examples: 3130 download_size: 15971078 dataset_size: 31633952 - config_name: QDMR features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string splits: - name: test num_bytes: 900632 num_examples: 8069 - name: train num_bytes: 12790466 num_examples: 44321 - name: validation num_bytes: 2237472 num_examples: 7760 download_size: 15971078 dataset_size: 15928570 - config_name: QDMR-lexicon features: - name: source dtype: string - name: allowed_tokens dtype: string splits: - name: test num_bytes: 10331822 num_examples: 8069 - name: train num_bytes: 56913064 num_examples: 44321 - name: validation num_bytes: 9936933 num_examples: 7760 download_size: 15971078 dataset_size: 77181819 - config_name: logical-forms features: - name: question_id dtype: string - name: question_text dtype: string - name: decomposition dtype: string - name: operators dtype: string - name: split dtype: string - name: program dtype: string splits: - name: test num_bytes: 927038 num_examples: 8006 - name: train num_bytes: 19821676 num_examples: 44098 - name: validation num_bytes: 3504893 num_examples: 7719 download_size: 15971078 dataset_size: 24253607 --- # Dataset Card for "break_data" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/allenai/Break](https://github.com/allenai/Break) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 79.86 MB - **Size of the generated dataset:** 155.55 MB - **Total amount of disk used:** 235.39 MB ### Dataset Summary Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations (QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases. This repository contains the Break dataset along with information on the exact data format. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### QDMR - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 15.93 MB - **Total amount of disk used:** 31.90 MB An example of 'validation' looks as follows. ``` { "decomposition": "return flights ;return #1 from denver ;return #2 to philadelphia ;return #3 if available", "operators": "['select', 'filter', 'filter', 'filter']", "question_id": "ATIS_dev_0", "question_text": "what flights are available tomorrow from denver to philadelphia ", "split": "dev" } ``` #### QDMR-high-level - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 6.54 MB - **Total amount of disk used:** 22.51 MB An example of 'train' looks as follows. ``` { "decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4", "operators": "['select', 'filter', 'filter', 'filter', 'project']", "question_id": "ATIS_dev_102", "question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ", "split": "dev" } ``` #### QDMR-high-level-lexicon - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 31.64 MB - **Total amount of disk used:** 47.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'he', 'distinct', 'House', 'two', 'at least', 'or ', 'date', 'o...", "source": "What office, also held by a member of the Maine House of Representatives, did James K. Polk hold before he was president?" } ``` #### QDMR-lexicon - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 77.19 MB - **Total amount of disk used:** 93.16 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'distinct', 'two', 'at least', 'or ', 'date', 'on ', '@@14@@', ...", "source": "what flights are available tomorrow from denver to philadelphia " } ``` #### logical-forms - **Size of downloaded dataset files:** 15.97 MB - **Size of the generated dataset:** 24.25 MB - **Total amount of disk used:** 40.22 MB An example of 'train' looks as follows. ``` { "decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4", "operators": "['select', 'filter', 'filter', 'filter', 'project']", "program": "some program", "question_id": "ATIS_dev_102", "question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ", "split": "dev" } ``` ### Data Fields The data fields are the same among all splits. #### QDMR - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. #### QDMR-high-level - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. #### QDMR-high-level-lexicon - `source`: a `string` feature. - `allowed_tokens`: a `string` feature. #### QDMR-lexicon - `source`: a `string` feature. - `allowed_tokens`: a `string` feature. #### logical-forms - `question_id`: a `string` feature. - `question_text`: a `string` feature. - `decomposition`: a `string` feature. - `operators`: a `string` feature. - `split`: a `string` feature. - `program`: a `string` feature. ### Data Splits | name |train|validation|test| |-----------------------|----:|---------:|---:| |QDMR |44321| 7760|8069| |QDMR-high-level |17503| 3130|3195| |QDMR-high-level-lexicon|17503| 3130|3195| |QDMR-lexicon |44321| 7760|8069| |logical-forms |44098| 7719|8006| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Wolfson2020Break, title={Break It Down: A Question Understanding Benchmark}, author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan}, journal={Transactions of the Association for Computational Linguistics}, year={2020}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

brwac

--- annotations_creators: - no-annotation language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: brwac pretty_name: BrWaC dataset_info: features: - name: doc_id dtype: string - name: title dtype: string - name: uri dtype: string - name: text sequence: - name: paragraphs sequence: string splits: - name: train num_bytes: 18828421452 num_examples: 3530796 download_size: 0 dataset_size: 18828421452 --- # Dataset Card for BrWaC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [BrWaC homepage](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC) - **Repository:** [BrWaC repository](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC) - **Paper:** [The brWaC Corpus: A New Open Resource for Brazilian Portuguese](https://www.aclweb.org/anthology/L18-1686/) - **Point of Contact:** [Jorge A. Wagner Filho](mailto:[email protected]) ### Dataset Summary The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework, which was made public for research purposes. The current corpus version, released in January 2017, is composed by 3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available solely for academic research purposes, and you agreed not to use it for any commercial applications. Manually download at https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Portuguese ## Dataset Structure ### Data Instances An example from the BrWaC dataset looks as follows: ``` { "doc_id": "netg-1afc73", "text": { "paragraphs": [ [ "Conteúdo recente" ], [ "ESPUMA MARROM CHAMADA \"NINGUÉM MERECE\"" ], [ "31 de Agosto de 2015, 7:07 , por paulo soavinski - | No one following this article yet." ], [ "Visualizado 202 vezes" ], [ "JORNAL ELETRÔNICO DA ILHA DO MEL" ], [ "Uma espuma marrom escuro tem aparecido com frequência na Praia de Fora.", "Na faixa de areia ela aparece disseminada e não chama muito a atenção.", "No Buraco do Aipo, com muitas pedras, ela aparece concentrada.", "É fácil saber que esta espuma estranha está lá, quando venta.", "Pequenos algodões de espuma começam a flutuar no espaço, pertinho da Praia do Saquinho.", "Quem pode ajudar na coleta deste material, envio a laboratório renomado e pagamento de análises, favor entrar em contato com o site." ] ] }, "title": "ESPUMA MARROM CHAMADA ‟NINGUÃÂM MERECE‟ - paulo soavinski", "uri": "http://blogoosfero.cc/ilhadomel/pousadasilhadomel.com.br/espuma-marrom-chamada-ninguem-merece" } ``` ### Data Fields - `doc_id`: The document ID - `title`: The document title - `uri`: URI where the document was extracted from - `text`: A list of document paragraphs (with a list of sentences in it as a list of strings) ### Data Splits The data is only split into train set with size of 3530796 samples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{wagner2018brwac, title={The brwac corpus: A new open resource for brazilian portuguese}, author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.

bsd_ja_en

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - ja license: - cc-by-nc-sa-4.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: business-scene-dialogue pretty_name: Business Scene Dialogue tags: - business-conversations-translation dataset_info: features: - name: id dtype: string - name: tag dtype: string - name: title dtype: string - name: original_language dtype: string - name: 'no' dtype: int32 - name: en_speaker dtype: string - name: ja_speaker dtype: string - name: en_sentence dtype: string - name: ja_sentence dtype: string splits: - name: train num_bytes: 4778409 num_examples: 20000 - name: test num_bytes: 493038 num_examples: 2120 - name: validation num_bytes: 477964 num_examples: 2051 download_size: 8135045 dataset_size: 5749411 --- # Dataset Card for Business Scene Dialogue ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/) - **Repository:** [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/) - **Paper:** [Rikters et al., 2019](https://www.aclweb.org/anthology/D19-5204) - **Leaderboard:** - **Point of Contact:** Matīss Rikters ### Dataset Summary This is the Business Scene Dialogue (BSD) dataset, a Japanese-English parallel corpus containing written conversations in various business scenarios. The dataset was constructed in 3 steps: 1) selecting business scenes, 2) writing monolingual conversation scenarios according to the selected scenes, and 3) translating the scenarios into the other language. Half of the monolingual scenarios were written in Japanese and the other half were written in English. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English, Japanese. ## Dataset Structure ### Data Instances Each instance contains a conversation identifier, a sentence number that indicates its position within the conversation, speaker name in English and Japanese, text in English and Japanese, original language, scene of the scenario (tag), and title of the scenario (title). ```python { "id": "190315_E004_13", "no": 14, "speaker": "Mr. Sam Lee", "ja_speaker": "サムリーさん", "en_sentence": "Would you guys consider a different scheme?", "ja_sentence": "別の事業案も考慮されますか？", "original_language": "en", "tag": "phone call", "title": "Phone: Review spec and scheme" } ``` ### Data Fields - id: dialogue identifier - no: sentence pair number within a dialogue - en_speaker: speaker name in English - ja_speaker: speaker name in Japanese - en_sentence: sentence in English - ja_sentence: sentence in Japanese - original_language: language in which monolingual scenario was written - tag: scenario - title: scenario title ### Data Splits - There are a total of 24171 sentences / 808 business scenarios. - Train: 20000 sentences / 670 scenarios - Dev: 2051 sentences / 69 scenarios - Test: 2120 sentences / 69 scenarios ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This dataset was released under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. ### Citation Information ``` @inproceedings{rikters-etal-2019-designing, title = "Designing the Business Conversation Corpus", author = "Rikters, Mat{\=\i}ss and Ri, Ryokan and Li, Tong and Nakazawa, Toshiaki", booktitle = "Proceedings of the 6th Workshop on Asian Translation", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5204", doi = "10.18653/v1/D19-5204", pages = "54--61" } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.

bswac

--- annotations_creators: - no-annotation language_creators: - found language: - bs license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: BsWac dataset_info: features: - name: sentence dtype: string config_name: bswac splits: - name: train num_bytes: 9156258478 num_examples: 354581267 download_size: 1988514951 dataset_size: 9156258478 --- # Dataset Card for BsWac ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://nlp.ffzg.hr/resources/corpora/bswac/ - **Repository:** https://www.clarin.si/repository/xmlui/handle/11356/1062 - **Paper:** http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic14-bs.pdf - **Leaderboard:** - **Point of Contact:** [Nikola Ljubešič](mailto:[email protected]) ### Dataset Summary The Bosnian web corpus bsWaC was built by crawling the .ba top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Bosnian vs. Croatian vs. Serbian). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Dataset is monolingual in Bosnian language. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @misc{11356/1062, title = {Bosnian web corpus {bsWaC} 1.1}, author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip}, url = {http://hdl.handle.net/11356/1062}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)}, year = {2016} } ``` ### Contributions Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset.

c3

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - zh license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: c3 pretty_name: C3 dataset_info: - config_name: mixed features: - name: documents sequence: string - name: document_id dtype: string - name: questions sequence: - name: question dtype: string - name: answer dtype: string - name: choice sequence: string splits: - name: train num_bytes: 2710513 num_examples: 3138 - name: test num_bytes: 891619 num_examples: 1045 - name: validation num_bytes: 910799 num_examples: 1046 download_size: 5481785 dataset_size: 4512931 - config_name: dialog features: - name: documents sequence: string - name: document_id dtype: string - name: questions sequence: - name: question dtype: string - name: answer dtype: string - name: choice sequence: string splits: - name: train num_bytes: 2039819 num_examples: 4885 - name: test num_bytes: 646995 num_examples: 1627 - name: validation num_bytes: 611146 num_examples: 1628 download_size: 4352392 dataset_size: 3297960 --- # Dataset Card for C3 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** []() - **Repository:** [link]() - **Paper:** []() - **Leaderboard:** []() - **Point of Contact:** []() ### Dataset Summary Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @article{sun2019investigating, title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension}, author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire}, journal={Transactions of the Association for Computational Linguistics}, year={2020}, url={https://arxiv.org/abs/1904.09679v3} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.

c4

--- pretty_name: C4 annotations_creators: - no-annotation language_creators: - found language: - en license: - odc-by multilinguality: - multilingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: c4 dataset_info: - config_name: en features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 828589180707 num_examples: 364868892 - name: validation num_bytes: 825767266 num_examples: 364608 download_size: 326778635540 dataset_size: 1657178361414 - config_name: en.noblocklist features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 1029628201361 num_examples: 393391519 - name: validation num_bytes: 1025606012 num_examples: 393226 download_size: 406611392434 dataset_size: 2059256402722 - config_name: realnewslike features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 38165657946 num_examples: 13799838 - name: validation num_bytes: 37875873 num_examples: 13863 download_size: 15419740744 dataset_size: 76331315892 - config_name: en.noclean features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 6715509699938 num_examples: 1063805381 - name: validation num_bytes: 6706356913 num_examples: 1065029 download_size: 2430376268625 dataset_size: 6722216056851 --- # Dataset Card for C4 ## Table of Contents - [Dataset Card for C4](#dataset-card-for-c4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://huggingface.co/datasets/allenai/c4 - **Paper:** https://arxiv.org/abs/1910.10683 ### Dataset Summary A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4 It comes in four variants: - `en`: 305GB in JSON format - `en.noblocklist`: 380GB in JSON format - `en.noclean`: 2.3TB in JSON format - `realnewslike`: 15GB in JSON format The `en.noblocklist` variant is exactly the same as the `en` variant, except we turned off the so-called "badwords filter", which removes all documents that contain words from the lists at https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words. ### Supported Tasks and Leaderboards C4 is mainly intended to pretrain language models and word representations. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances An example form the `en` config is: ``` { 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/', 'text': 'Beginners BBQ Class Taking Place in Missoula!\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z' } ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp as a string ### Data Splits | name | train |validation| |----------------|--------:|---------:| | en |364868892| 364608| | en.noblocklist |393391519| 393226| | en.noclean | ?| ?| | realnewslike | 13799838| 13863| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization C4 dataset is a collection of about 750GB of English-language text sourced from the public Common Crawl web scrape. It includes heuristics to extract only natural language (as opposed to boilerplate and other gibberish) in addition to extensive deduplication. You can find the code that has been used to build this dataset in [c4.py](https://github.com/tensorflow/datasets/blob/5952d3d60d60e1727786fa7a9a23d24bb463d4d6/tensorflow_datasets/text/c4.py) by Tensorflow Datasets. The dataset was explicitly designed to be English only: any page that was not given a probability of at least 99% of being English by [langdetect](https://github.com/Mimino666/langdetect) was discarded. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

cail2018

--- annotations_creators: - found language_creators: - found language: - zh license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: chinese-ai-and-law-cail-2018 pretty_name: CAIL 2018 tags: - judgement-prediction dataset_info: features: - name: fact dtype: string - name: relevant_articles sequence: int32 - name: accusation sequence: string - name: punish_of_money dtype: float32 - name: criminals sequence: string - name: death_penalty dtype: bool - name: imprisonment dtype: float32 - name: life_imprisonment dtype: bool splits: - name: exercise_contest_train num_bytes: 220112732 num_examples: 154592 - name: exercise_contest_valid num_bytes: 21702157 num_examples: 17131 - name: exercise_contest_test num_bytes: 41057634 num_examples: 32508 - name: first_stage_train num_bytes: 1779657510 num_examples: 1710856 - name: first_stage_test num_bytes: 244335194 num_examples: 217016 - name: final_test num_bytes: 44194707 num_examples: 35922 download_size: 984551626 dataset_size: 2351059934 --- --- # Dataset Card for CAIL 2018 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/thunlp/CAIL/blob/master/README_en.md) - **Repository:** [Github](https://github.com/thunlp/CAIL) - **Paper:** [Arxiv](https://arxiv.org/abs/1807.02478) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.

caner

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ar license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: CANER dataset_info: features: - name: token dtype: string - name: ner_tag dtype: class_label: names: '0': Allah '1': Book '2': Clan '3': Crime '4': Date '5': Day '6': Hell '7': Loc '8': Meas '9': Mon '10': Month '11': NatOb '12': Number '13': O '14': Org '15': Para '16': Pers '17': Prophet '18': Rlig '19': Sect '20': Time splits: - name: train num_bytes: 5095721 num_examples: 258240 download_size: 17063406 dataset_size: 5095721 --- # Dataset Card for CANER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [Classical-Arabic-Named-Entity-Recognition-Corpus](https://github.com/RamziSalah) - **Paper:** [Researchgate](https://www.researchgate.net/publication/330075080_BUILDING_THE_CLASSICAL_ARABIC_NAMED_ENTITY_RECOGNITION_CORPUS_CANERCORPUS) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Classical Arabic Named Entity Recognition corpus is a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities. ### Supported Tasks and Leaderboards - Named Entity Recognition ### Languages Classical Arabic ## Dataset Structure ### Data Instances An example from the dataset: ``` {'ner_tag': 1, 'token': 'الجامع'} ``` Where 1 stands for "Book" ### Data Fields - `id`: id of the sample - `token`: the tokens of the example text - `ner_tag`: the NER tags of each token The NER tags correspond to this list: ``` "Allah", "Book", "Clan", "Crime", "Date", "Day", "Hell", "Loc", "Meas", "Mon", "Month", "NatOb", "Number", "O", "Org", "Para", "Pers", "Prophet", "Rlig", "Sect", "Time" ``` ### Data Splits Training splits only ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Ramzi Salah and Lailatul Qadri Zakaria ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information @article{article, author = {Salah, Ramzi and Zakaria, Lailatul}, year = {2018}, month = {12}, pages = {}, title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)}, volume = {96}, journal = {Journal of Theoretical and Applied Information Technology} } ### Contributions Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset.

capes

--- annotations_creators: - found language_creators: - found language: - en - pt license: - unknown multilinguality: - multilingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: capes pretty_name: CAPES tags: - dissertation-abstracts-translation - theses-translation dataset_info: features: - name: translation dtype: translation: languages: - en - pt config_name: en-pt splits: - name: train num_bytes: 472484364 num_examples: 1157610 download_size: 162229298 dataset_size: 472484364 --- # Dataset Card for CAPES ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES](https://sites.google.com/view/felipe-soares/datasets#h.p_kxOR6EhHm2a6) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil. The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were collected and aligned using the Hunalign algorithm. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{soares2018parallel, title={A Parallel Corpus of Theses and Dissertations Abstracts}, author={Soares, Felipe and Yamashita, Gabrielli Harumi and Anzanello, Michel Jose}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={345--352}, year={2018}, organization={Springer} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

casino

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-generation - fill-mask task_ids: - dialogue-modeling pretty_name: Campsite Negotiation Dialogues paperswithcode_id: casino dataset_info: features: - name: chat_logs list: - name: text dtype: string - name: task_data struct: - name: data dtype: string - name: issue2youget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: issue2theyget struct: - name: Firewood dtype: string - name: Water dtype: string - name: Food dtype: string - name: id dtype: string - name: participant_info struct: - name: mturk_agent_1 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: mturk_agent_2 struct: - name: value2issue struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: value2reason struct: - name: Low dtype: string - name: Medium dtype: string - name: High dtype: string - name: outcomes struct: - name: points_scored dtype: int32 - name: satisfaction dtype: string - name: opponent_likeness dtype: string - name: demographics struct: - name: age dtype: int32 - name: gender dtype: string - name: ethnicity dtype: string - name: education dtype: string - name: personality struct: - name: svo dtype: string - name: big-five struct: - name: extraversion dtype: float32 - name: agreeableness dtype: float32 - name: conscientiousness dtype: float32 - name: emotional-stability dtype: float32 - name: openness-to-experiences dtype: float32 - name: annotations list: list: string splits: - name: train num_bytes: 3211555 num_examples: 1030 download_size: 4300019 dataset_size: 3211555 --- # Dataset Card for Casino ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Github: Kushal Chawla CaSiNo](https://github.com/kushalchawla/CaSiNo) - **Paper:** [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://aclanthology.org/2021.naacl-main.254.pdf) - **Point of Contact:** [Kushal Chawla]([email protected]) ### Dataset Summary We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations. This helps to overcome the limitations of prior negotiation datasets such as Deal or No Deal and Craigslist Bargain. Each dialogue consists of rich meta-data including participant demographics, personality, and their subjective evaluation of the negotiation in terms of satisfaction and opponent likeness. ### Supported Tasks and Leaderboards Train end-to-end models for negotiation ### Languages English ## Dataset Structure ### Data Instances ``` { "chat_logs": [ { "text": "Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "task_data": {}, "id": "mturk_agent_1" }, ... ], "participant_info": { "mturk_agent_1": { "value2issue": ... "value2reason": ... "outcomes": ... "demographics": ... "personality": ... }, "mturk_agent_2": ... }, "annotations": [ ["Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "promote-coordination,elicit-pref"], ... ] } ``` ### Data Fields - `chat_logs`: The negotiation dialogue between two participants - `text`: The dialogue utterance - `task_data`: Meta-data associated with the utterance such as the deal submitted by a participant - `id`: The ID of the participant who typed this utterance - `participant_info`: Meta-data about the two participants in this conversation - `mturk_agent_1`: For the first participant (Note that 'first' is just for reference. There is no order between the participants and any participant can start the conversation) - `value2issue`: The priority order of this participant among Food, Water, Firewood - `value2reason`: The personal arguments given by the participants themselves, consistent with the above preference order. This preference order and these arguments were submitted before the negotiation began. - `outcomes`: The negotiation outcomes for this participant including objective and subjective assessment. - `demographics`: Demographic attributes of the participant in terms of age, gender, ethnicity, and education. - `personality`: Personality attributes for this participant, in terms of Big-5 and Social Value Orientation - `mturk_agent_2`: For the second participant; follows the same structure as above - `annotations`: Strategy annotations for each utterance in the dialogue, wherever available. The first element represents the utterance and the second represents a comma-separated list of all strategies present in that utterance. ### Data Splits No default data split has been provided. Hence, all 1030 data points are under the 'train' split. | | Train | | ----- | ----- | | total dialogues | 1030 | | annotated dialogues | 396 | ## Dataset Creation ### Curation Rationale The dataset was collected to address the limitations in prior negotiation datasets from the perspective of downstream applications in pedagogy and conversational AI. Please refer to the original paper published at NAACL 2021 for details about the rationale and data curation steps ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Source Data #### Initial Data Collection and Normalization The dialogues were crowdsourced on Amazon Mechanical Turk. The strategy annotations were performed by expert annotators (first three authors of the paper). Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). #### Who are the source language producers? The primary producers are Turkers on Amazon Mechanical Turk platform. Two turkers were randomly paired with each other to engage in a negotiation via a chat interface. Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Annotations #### Annotation process From the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for this dataset: >Three expert annotators independently annotated 396 dialogues containing 4615 utterances. The annotation guidelines were iterated over a subset of 5 dialogues, while the reliability scores were computed on a different subset of 10 dialogues. We use the nominal form of Krippendorff’s alpha (Krippendorff, 2018) to measure the inter-annotator agreement. We provide the annotation statistics in Table 2. Although we release all the annotations, we skip Coordination and Empathy for our analysis in this work, due to higher subjectivity resulting in relatively lower reliability scores. #### Who are the annotators? Three expert annotators (first three authors of the paper). ### Personal and Sensitive Information All personally identifiable information about the participants such as MTurk Ids or HIT Ids was removed before releasing the data. ## Considerations for Using the Data ### Social Impact of Dataset Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Discussion of Biases Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Other Known Limitations Please refer to Section 7 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ## Additional Information ### Dataset Curators Corresponding Author: Kushal Chawla (`[email protected]`)\ Affiliation: University of Southern California\ Please refer to the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for the complete author list. ### Licensing Information The project is licensed under CC-by-4.0 ### Citation Information ``` @inproceedings{chawla2021casino, title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems}, author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan}, booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, pages={3167--3185}, year={2021} } ``` ### Contributions Thanks to [Kushal Chawla](https://kushalchawla.github.io/) for adding this dataset.

catalonia_independence

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - ca - es license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: cic pretty_name: Catalonia Independence Corpus configs: - catalan - spanish tags: - stance-detection dataset_info: - config_name: catalan features: - name: id_str dtype: string - name: TWEET dtype: string - name: LABEL dtype: class_label: names: '0': AGAINST '1': FAVOR '2': NEUTRAL splits: - name: train num_bytes: 1406250 num_examples: 6028 - name: test num_bytes: 469204 num_examples: 2010 - name: validation num_bytes: 473393 num_examples: 2010 download_size: 995415 dataset_size: 2348847 - config_name: spanish features: - name: id_str dtype: string - name: TWEET dtype: string - name: LABEL dtype: class_label: names: '0': AGAINST '1': FAVOR '2': NEUTRAL splits: - name: train num_bytes: 1507388 num_examples: 6046 - name: test num_bytes: 501783 num_examples: 2016 - name: validation num_bytes: 505092 num_examples: 2015 download_size: 1070281 dataset_size: 2514263 --- # Dataset Card for Catalonia Independence Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/ixa-ehu/catalonia-independence-corpus - **Repository:** https://github.com/ixa-ehu/catalonia-independence-corpus - **Paper:** [Multilingual Stance Detection: The Catalonia Independence Corpus](https://www.aclweb.org/anthology/2020.lrec-1.171/) - **Leaderboard:** - **Point of Contact:** [Rodrigo Agerri](https://github.com/ragerri) (corpus creator) ### Dataset Summary This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia. Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Spanish and Catalan ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.

cats_vs_dogs

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: cats-vs-dogs pretty_name: Cats Vs. Dogs dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 4219400 num_examples: 23410 download_size: 824887076 dataset_size: 4219400 --- # Dataset Card for Cats Vs. Dogs ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765) - **Repository:** - **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf) - **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats) - **Point of Contact:** ### Dataset Summary A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page: > The Asirra data set > > Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords. > > Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface: > > Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs). ### Languages English. ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>, 'labels': 0 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits | | train | |---------------|------:| | # of examples | 23410 | ## Dataset Creation ### Curation Rationale This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA: From the competition page: > Image recognition attacks > > While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. ### Source Data #### Initial Data Collection and Normalization This dataset is a subset of the Asirra dataset. From the competition page: > Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. #### Who are the source language producers? The users of [Petfinder.com](https://www.petfinder.com/). ### Annotations #### Annotation process The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/). #### Who are the annotators? The users of [Petfinder.com](https://www.petfinder.com/). ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.” ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, year = {2007}, month = {October}, publisher = {Association for Computing Machinery, Inc.}, url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

cawac

--- annotations_creators: - no-annotation language_creators: - found language: - ca license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cawac pretty_name: caWaC dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 3987238444 num_examples: 24745986 download_size: 1620361999 dataset_size: 3987238444 --- # Dataset Card for caWaC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://nlp.ffzg.hr/resources/corpora/cawac/ - **Repository:** http://nlp.ffzg.hr/data/corpora/cawac.uniq.sortr.gz - **Paper:** http://www.lrec-conf.org/proceedings/lrec2014/pdf/841_Paper.pdf - **Leaderboard:** - **Point of Contact:** [Nikola Ljubešič](mailto:[email protected]) ### Dataset Summary caWaC is a 780-million-token web corpus of Catalan built from the .cat top-level-domain in late 2013. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Dataset is monolingual in Catalan language. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @inproceedings{DBLP:conf/lrec/LjubesicT14, author = {Nikola Ljubesic and Antonio Toral}, editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asunci{\'{o}}n Moreno and Jan Odijk and Stelios Piperidis}, title = {caWaC - {A} web corpus of Catalan and its application to language modeling and machine translation}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation, {LREC} 2014, Reykjavik, Iceland, May 26-31, 2014}, pages = {1728--1732}, publisher = {European Language Resources Association {(ELRA)}}, year = {2014}, url = {http://www.lrec-conf.org/proceedings/lrec2014/summaries/841.html}, timestamp = {Mon, 19 Aug 2019 15:23:35 +0200}, biburl = {https://dblp.org/rec/conf/lrec/LjubesicT14.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

cbt

--- pretty_name: Children’s Book Test (CBT) annotations_creators: - machine-generated language_creators: - found language: - en license: - gfdl multilinguality: - monolingual size_categories: - 100K<n<1M - n<1K source_datasets: - original task_categories: - other - question-answering task_ids: - multiple-choice-qa paperswithcode_id: cbt configs: - CN - NE - P - V - raw dataset_info: - config_name: raw features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 25741580 num_examples: 98 - name: test num_bytes: 1528704 num_examples: 5 - name: validation num_bytes: 1182657 num_examples: 5 download_size: 120547669 dataset_size: 28452941 - config_name: V features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 252177649 num_examples: 105825 - name: test num_bytes: 5806625 num_examples: 2500 - name: validation num_bytes: 4556425 num_examples: 2000 download_size: 120547669 dataset_size: 262540699 - config_name: P features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 852852601 num_examples: 334030 - name: test num_bytes: 6078048 num_examples: 2500 - name: validation num_bytes: 4776981 num_examples: 2000 download_size: 120547669 dataset_size: 863707630 - config_name: NE features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 253551931 num_examples: 108719 - name: test num_bytes: 5707734 num_examples: 2500 - name: validation num_bytes: 4424316 num_examples: 2000 download_size: 120547669 dataset_size: 263683981 - config_name: CN features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 301730151 num_examples: 120769 - name: test num_bytes: 6138376 num_examples: 2500 - name: validation num_bytes: 4737257 num_examples: 2000 download_size: 120547669 dataset_size: 312605784 --- # Dataset Card for CBT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[The bAbI project](https://research.fb.com/downloads/babi/) - **Repository:** - **Paper:** [arXiv Paper](https://arxiv.org/pdf/1511.02301.pdf) - **Leaderboard:** - **Point of Contact:** [Felix Hill](mailto:[email protected]) or [Antoine Bordes](mailto:[email protected]). ### Dataset Summary The Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available. This dataset contains four different configurations: - `V`: where the answers to the questions are verbs. - `P`: where the answers to the questions are pronouns. - `NE`: where the answers to the questions are named entities. - `CN`: where the answers to the questions are common nouns. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The data is present in English language as written by authors Lucy Maud Montgomery, Charles Dickens,Andrew Lang, etc. in story books for children. ## Dataset Structure ### Data Instances An instance from the `V` config: ``` {'answer': 'said', 'options': ['christening', 'existed', 'hear', 'knows', 'read', 'remarked', 'said', 'sitting', 'talking', 'wearing'], 'question': "`` They are very kind old ladies in their way , '' XXXXX the king ; `` and were nice to me when I was a boy . ''", 'sentences': ['This vexed the king even more than the queen , who was very clever and learned , and who had hated dolls when she was a child .', 'However , she , too in spite of all the books she read and all the pictures she painted , would have been glad enough to be the mother of a little prince .', 'The king was anxious to consult the fairies , but the queen would not hear of such a thing .', 'She did not believe in fairies : she said that they had never existed ; and that she maintained , though The History of the Royal Family was full of chapters about nothing else .', 'Well , at long and at last they had a little boy , who was generally regarded as the finest baby that had ever been seen .', 'Even her majesty herself remarked that , though she could never believe all the courtiers told her , yet he certainly was a fine child -- a very fine child .', 'Now , the time drew near for the christening party , and the king and queen were sitting at breakfast in their summer parlour talking over it .', 'It was a splendid room , hung with portraits of the royal ancestors .', 'There was Cinderella , the grandmother of the reigning monarch , with her little foot in her glass slipper thrust out before her .', 'There was the Marquis de Carabas , who , as everyone knows , was raised to the throne as prince consort after his marriage with the daughter of the king of the period .', 'On the arm of the throne was seated his celebrated cat , wearing boots .', 'There , too , was a portrait of a beautiful lady , sound asleep : this was Madame La Belle au Bois-dormant , also an ancestress of the royal family .', 'Many other pictures of celebrated persons were hanging on the walls .', "`` You have asked all the right people , my dear ? ''", 'said the king .', "`` Everyone who should be asked , '' answered the queen .", "`` People are so touchy on these occasions , '' said his majesty .", "`` You have not forgotten any of our aunts ? ''", "`` No ; the old cats ! ''", "replied the queen ; for the king 's aunts were old-fashioned , and did not approve of her , and she knew it ."]} ``` ### Data Fields For the `raw` config, the data fields are: - `title`: a `string` feature containing the title of the book present in the dataset. - `content`: a `string` feature containing the content of the book present in the dataset. For all other configs, the data fields are: - `sentences`: a `list` of `string` features containing 20 sentences from a book. - `question`: a `string` feature containing a question with blank marked as `XXXX` which is to be filled with one of the options. - `answer`: a `string` feature containing the answer. - `options`: a `list` of `string` features containing the options for the question. ### Data Splits The splits and corresponding sizes are: | |train |test |validation| |:--|------:|----:|---------:| |raw|98 |5 |5 | |V |105825 |2500 |2000 | |P |334030 |2500 |2000 | |CN |120769 |2500 |2000 | |NE |108719 |2500 |2000 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Children's Book Authors ### Annotations #### Annotation process From the [homepage](https://research.fb.com/downloads/babi/): >After allocating books to either training, validation or test sets, we formed example ‘questions’ from chapters in the book by enumerating 21 consecutive sentences. In each question, the first 20 sentences form the context, and a word is removed from the 21st sentence, which becomes the query. Models must identify the answer word among a selection of 10 candidate answers appearing in the context sentences and the query. For finer-grained analyses, we evaluated four classes of question by removing distinct types of word: Named Entities, (Common) Nouns, Verbs and Prepositions. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information ``` GNU Free Documentation License v1.3 ``` ### Citation Information ``` @misc{hill2016goldilocks, title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations}, author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston}, year={2016}, eprint={1511.02301}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.

cc100

--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu language_bcp47: - bn-Latn - hi-Latn - my-x-zawgyi - ta-Latn - te-Latn - ur-Latn - zh-Hans - zh-Hant license: - unknown multilinguality: - multilingual size_categories: - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc100 pretty_name: CC100 configs: - am - sr dataset_info: - config_name: am features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 935440775 num_examples: 3124561 download_size: 138821056 dataset_size: 935440775 - config_name: sr features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10299427460 num_examples: 35747957 download_size: 1578989320 dataset_size: 10299427460 - config_name: ka features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10228918845 num_examples: 31708119 download_size: 1100446372 dataset_size: 10228918845 --- # Dataset Card for CC100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://data.statmt.org/cc-100/ - **Repository:** None - **Paper:** https://www.aclweb.org/anthology/2020.acl-main.747.pdf, https://www.aclweb.org/anthology/2020.lrec-1.494.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. ### Supported Tasks and Leaderboards CC-100 is mainly inteded to pretrain language models and word represantations. ### Languages To load a language which isn't part of the config, all you need to do is specify the language code in the config. You can find the valid languages in Homepage section of Dataset Description: https://data.statmt.org/cc-100/ E.g. `dataset = load_dataset("cc100", lang="en")` ## Dataset Structure ### Data Instances An example from the `am` configuration: ``` {'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'} ``` Each data point is a paragraph of text. The paragraphs are presented in the original (unshuffled) order. Documents are separated by a data point consisting of a single newline character. ### Data Fields The data fields are: - id: id of the example - text: content as a string ### Data Splits Sizes of some configurations: | name |train| |----------|----:| |am|3124561| |sr|35747957| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Being constructed from Common Crawl, personal and sensitive information might be present. This **must** be considered before training deep learning models with CC-100, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was prepared by [Statistical Machine Translation at the University of Edinburgh](https://www.statmt.org/ued/) using the [CC-Net](https://github.com/facebookresearch/cc_net) toolkit by Facebook Research. ### Licensing Information Statistical Machine Translation at the University of Edinburgh makes no claims of intellectual property on the work of preparation of the corpus. By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset. ### Citation Information ```bibtex @inproceedings{conneau-etal-2020-unsupervised, title = "Unsupervised Cross-lingual Representation Learning at Scale", author = "Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.747", doi = "10.18653/v1/2020.acl-main.747", pages = "8440--8451", abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.", } ``` ```bibtex @inproceedings{wenzek-etal-2020-ccnet, title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data", author = "Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, Edouard", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.494", pages = "4003--4012", abstract = "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

cc_news

--- pretty_name: CC-News annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc-news dataset_info: features: - name: title dtype: string - name: text dtype: string - name: domain dtype: string - name: date dtype: string - name: description dtype: string - name: url dtype: string - name: image_url dtype: string config_name: plain_text splits: - name: train num_bytes: 2016418133 num_examples: 708241 download_size: 845131146 dataset_size: 2016418133 --- # Dataset Card for CC-News ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CC-News homepage](https://commoncrawl.org/2016/10/news-dataset-available/) - **Point of Contact:** [Vladimir Blagojevic](mailto:[email protected]) ### Dataset Summary CC-News dataset contains news articles from news sites all over the world. The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has been prepared using [news-please](https://github.com/fhamborg/news-please) - an integrated web crawler and information extractor for news. It contains 708241 English language news articles published between Jan 2017 and December 2019. It represents a small portion of the English language subset of the CC-News dataset. ### Supported Tasks and Leaderboards CC-News has been mostly used for language model training. ### Languages The text in the dataset is in the English language. ## Dataset Structure ### Data Instances Dataset instance contains an article itself and the relevant article fields. An example from the Cc-New train set looks as follows: ``` { 'date': '2017-08-14 00:00:00', 'description': '"The spirit of Green Day has always been about rising above oppression."', 'domain': '1041jackfm.cbslocal.com', 'image_url': 'https://cbs1041jackfm.files.wordpress.com/2017/08/billie-joe-armstrong-theo-wargo-getty-images.jpg?w=946', 'text': 'By Abby Hassler\nGreen Day’s Billie Joe Armstrong has always been outspoken about his political beliefs. Following the tragedy in Charlottesville, Virgina, over the weekend, Armstrong felt the need to speak out against the white supremacists who caused much of the violence.\nRelated: Billie Joe Armstrong Wins #TBT with Childhood Studio Photo\n“My heart feels heavy. I feel like what happened in Charlottesville goes beyond the point of anger,” Armstrong wrote on Facebook. “It makes me sad and desperate. shocked. I f—— hate racism more than anything.”\n“The spirit of Green Day has always been about rising above oppression. and sticking up for what you believe in and singing it at the top of your lungs,” Armstrong continued. “We grew up fearing nuclear holocaust because of the cold war. those days are feeling way too relevant these days. these issues are our ugly past.. and now it’s coming to haunt us. always resist these doomsday politicians. and in the words of our punk forefathers .. Nazi punks f— off.”', 'title': 'Green Day’s Billie Joe Armstrong Rails Against White Nationalists', 'url': 'http://1041jackfm.cbslocal.com/2017/08/14/billie-joe-armstrong-white-nationalists/' } ``` ### Data Fields - `date`: date of publication - `description`: description or a summary of the article - `domain`: source domain of the article (i.e. www.nytimes.com) - `image_url`: URL of the article's image - `text`: the actual article text in raw form - `title`: title of the article - `url`: article URL, the original URL where it was scraped. ### Data Splits CC-News dataset has only the training set, i.e. it has to be loaded with `train` split specified: `cc_news = load_dataset('cc_news', split="train")` ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization CC-News dataset has been proposed, created, and maintained by Sebastian Nagel. The data is publicly available on AWS S3 Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has been prepared using [news-please](https://github.com/fhamborg/news-please) - an integrated web crawler and information extractor for news. It contains 708241 English language news articles published between Jan 2017 and December 2019. Although news-please tags each news article with an appropriate language tag, these tags are somewhat unreliable. To strictly isolate English language articles an additional check has been performed using [Spacy langdetect pipeline](https://spacy.io/universe/project/spacy-langdetect). We selected articles with text fields scores of 80% probability or more of being English. There are no strict guarantees that each article has all the relevant fields. For example, 527595 articles have a valid description field. All articles have what appears to be a valid image URL, but they have not been verified. #### Who are the source language producers? The news websites throughout the World. ### Annotations #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information As one can imagine, data contains contemporary public figures or individuals who appeared in the news. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help language model researchers develop better language models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{Hamborg2017, author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela}, title = {news-please: A Generic News Crawler and Extractor}, year = {2017}, booktitle = {Proceedings of the 15th International Symposium of Information Science}, location = {Berlin}, doi = {10.5281/zenodo.4120316}, pages = {218--223}, month = {March} } ``` ### Contributions Thanks to [@vblagoje](https://github.com/vblagoje) for adding this dataset.

ccaligned_multilingual

--- annotations_creators: - no-annotation language_creators: - found language: - af - ak - am - ar - as - ay - az - be - bg - bm - bn - br - bs - ca - ceb - ckb - cs - cy - de - dv - el - eo - es - fa - ff - fi - fo - fr - fy - ga - gl - gn - gu - he - hi - hr - hu - id - ig - is - it - iu - ja - ka - kac - kg - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - nso - ny - om - or - pa - pl - ps - pt - rm - ro - ru - rw - sc - sd - se - shn - si - sk - sl - sn - so - sq - sr - ss - st - su - sv - sw - syc - szl - ta - te - tg - th - ti - tl - tn - tr - ts - tt - ug - uk - ur - uz - ve - vi - war - wo - xh - yi - yo - zgh - zh - zu - zza license: - unknown multilinguality: - translation size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M - 10M<n<100M source_datasets: - original task_categories: - other paperswithcode_id: ccaligned pretty_name: CCAligned dataset_info: - config_name: documents-zz_TR features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - zz_TR splits: - name: train num_bytes: 641412 num_examples: 41 download_size: 125488 dataset_size: 641412 - config_name: sentences-zz_TR features: - name: translation dtype: translation: languages: - en_XX - zz_TR - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 4056 num_examples: 34 download_size: 1428 dataset_size: 4056 - config_name: documents-tz_MA features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - tz_MA splits: - name: train num_bytes: 51782 num_examples: 4 download_size: 11996 dataset_size: 51782 - config_name: sentences-tz_MA features: - name: translation dtype: translation: languages: - en_XX - tz_MA - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 6256 num_examples: 33 download_size: 2420 dataset_size: 6256 - config_name: documents-ak_GH features: - name: Domain dtype: string - name: Source_URL dtype: string - name: Target_URL dtype: string - name: translation dtype: translation: languages: - en_XX - ak_GH splits: - name: train num_bytes: 10738312 num_examples: 249 download_size: 399236 dataset_size: 10738312 - config_name: sentences-ak_GH features: - name: translation dtype: translation: languages: - en_XX - ak_GH - name: LASER_similarity dtype: float32 splits: - name: train num_bytes: 50110 num_examples: 478 download_size: 17636 dataset_size: 50110 --- # Dataset Card for ccaligned_multilingual ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://www.statmt.org/cc-aligned/ - **Repository:** [Needs More Information] - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). This corpus was created from 68 Commoncrawl Snapshots. To load a language which isn't part of the config, all you need to do is specify the language code. You can find the valid languages in http://www.statmt.org/cc-aligned/ E.g. ``` dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="documents") ``` or ``` dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="sentences") ``` ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in (137) multiple languages aligned with english. ## Dataset Structure ### Data Instances An instance of `documents` type for language `ak_GH`: ``` {'Domain': 'islamhouse.com', 'Source_URL': 'https://islamhouse.com/en/audios/373088/', 'Target_URL': 'https://islamhouse.com/ak/audios/373088/', 'translation': {'ak_GH': "Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah - Arab kasa|Islamhouse.com|Follow us:|facebook|twitter|taepe|Titles All|Fie wibesite|kasa nyina|Buukuu edi adanse ma prente|Nhyehyɛmu|Nyim/sua Islam|Curriculums|Nyina ndeɛma|Nyina ndeɛma (295)|Buukuu/ nwoma (2)|sini / muuvi (31)|ɔdio (262)|Aɛn websideNew!|Kɔ wura kramosom mu seisei|Ebio|figa/kaasɛ|Farebae|AKAkan|Kratafa titriw|kasa interface( anyimu) : Akan|Kasa ma no mu-nsɛm : Arab kasa|ɔdio|Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah|play|pause|stop|mute|unmute|max volume|Kasakyerɛ ni :|Farebae:|17 / 11 / 1432 , 15/10/2011|Nhyehyɛmu:|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Mmira ma Hajj na Umrah|nkyerɛmu|kasamu /sɛntɛns ma te ase na Umrah wɔ ... mu no hann ma no Quran na Sunnah na te ase ma no nana na no kasamu /sɛntɛns ma bi ma no emerging yi adu obusuani|Akenkane we ye di ko kasa bi su (36)|Afar - Qafár afa|Akan|Amhari ne - አማርኛ|Arab kasa - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldive - ދިވެހި|Greek - Ελληνικά|English ( brofo kasa) - English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|Kurdish - كوردی سۆرانی|Uganda ne - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Sango|Sinhalese - සිංහල|Somali - Soomaali|Albania ne - Shqip|Swahili - Kiswahili|Telugu - తెలుగు ప్రజలు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbeck ne - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chine ne - 中文|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Soma kɔ ne kɔ hom adamfo|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Nsɔwso fael (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|Enoumah ebatahu|Rituals/Esom ajomadie ewu Hajji mmire .. 1434 AH [01] no fapemso Enum|Fiidbak/ Ye hiya wu jun kyiri|Lenke de yɛe|kɔntakt yɛn|Aɛn webside|Qura'an Kro kronkrom|Balagh|wɔ mfinimfin Dowload faele|Yɛ atuu bra Islam mu afei|Tsin de yɛe ewu|Anaa bomu/combine hɛn melin liste|© Islamhouse Website/ Islam dan webi site|×|×|Yi mu kasa|", 'en_XX': 'SUMMARY in the jurisprudence of Umrah - Arabic - Abdul Aziz Bin Marzooq Al-Turaifi|Islamhouse.com|Follow us:|facebook|twitter|QuranEnc.com|HadeethEnc.com|Type|Titles All|Home Page|All Languages|Categories|Know about Islam|All items|All items (4057)|Books (701)|Articles (548)|Fatawa (370)|Videos (1853)|Audios (416)|Posters (98)|Greeting cards (22)|Favorites (25)|Applications (21)|Desktop Applications (3)|To convert to Islam now !|More|Figures|Sources|Curriculums|Our Services|QuranEnc.com|HadeethEnc.com|ENEnglish|Main Page|Interface Language : English|Language of the content : Arabic|Audios|تعريب عنوان المادة|SUMMARY in the jurisprudence of Umrah|play|pause|stop|mute|unmute|max volume|Lecturer : Abdul Aziz Bin Marzooq Al-Turaifi|Sources:|AlRaya Islamic Recoding in Riyadh|17 / 11 / 1432 , 15/10/2011|Categories:|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Pilgrimage and Umrah|Description|SUMMARY in jurisprudence of Umrah: A statement of jurisprudence and Umrah in the light of the Quran and Sunnah and understanding of the Ancestors and the statement of some of the emerging issues related to them.|This page translated into (36)|Afar - Qafár afa|Akane - Akan|Amharic - አማርኛ|Arabic - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldivi - ދިވެހި|Greek - Ελληνικά|English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|kurdish - كوردی سۆرانی|Ugandan - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Yanga ti Sango|Sinhalese - සිංහල|Somali - Soomaali|Albanian - Shqip|Swahili - Kiswahili|Telugu - తెలుగు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbek - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chinese - 中文|Send a comment to Webmaster|Send to a friend?|Send a comment to Webmaster|Attachments (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|The relevant Material|The rituals of the pilgrimage season .. 1434 AH [ 01] the fifth pillar|The Quality of the Accepted Hajj (Piligrimage) and Its Limitations|Easy Path to the Rules of the Rites of Hajj|A Call to the Pilgrims of the Scared House of Allah|More|feedback|Important links|Contact us|Privacy policy|Islam Q&A|Learning Arabic Language|About Us|Convert To Islam|Noble Quran encyclopedia|IslamHouse.com Reader|Encyclopedia of Translated Prophetic Hadiths|Our Services|The Quran|Balagh|Center for downloading files|To embrace Islam now...|Follow us through|Or join our mailing list.|© Islamhouse Website|×|×|Choose language|'}} ``` An instance of `sentences` type for language `ak_GH`: ``` {'LASER_similarity': 1.4549942016601562, 'translation': {'ak_GH': 'Salah (nyamefere) ye Mmerebeia', 'en_XX': 'What he dislikes when fasting (10)'}} ``` ### Data Fields For `documents` type: - `Domain`: a `string` feature containing the domain. - `Source_URL`: a `string` feature containing the source URL. - `Target_URL`: a `string` feature containing the target URL. - `translation`: a `dictionary` feature with two keys : - `en_XX`: a `string` feature containing the content in English. - <language_code>: a `string` feature containing the content in the `language_code` specified. For `sentences` type: - `LASER_similarity`: a `float32` feature representing the LASER similarity score. - `translation`: a `dictionary` feature with two keys : - `en_XX`: a `string` feature containing the content in English. - <language_code>: a `string` feature containing the content in the `language_code` specified. ### Data Splits Split sizes of some small configurations: | name |train| |----------|----:| |documents-zz_TR|41| |sentences-zz_TR|34| |documents-tz_MA|4| |sentences-tz_MA|33| |documents-ak_GH|249| |sentences-ak_GH|478| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{elkishky_ccaligned_2020, author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}, month = {November}, title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs}, year = {2020} address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.480", doi = "10.18653/v1/2020.emnlp-main.480", pages = "5960--5969" } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.

cdsc

--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: polish-cdscorpus pretty_name: Polish CDSCorpus tags: - sentences entailment and relatedness dataset_info: - config_name: cdsc-e features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: entailment_judgment dtype: class_label: names: '0': NEUTRAL '1': CONTRADICTION '2': ENTAILMENT splits: - name: train num_bytes: 1381902 num_examples: 8000 - name: test num_bytes: 179400 num_examples: 1000 - name: validation num_bytes: 174662 num_examples: 1000 download_size: 376079 dataset_size: 1735964 - config_name: cdsc-r features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: relatedness_score dtype: float32 splits: - name: train num_bytes: 1349902 num_examples: 8000 - name: test num_bytes: 175400 num_examples: 1000 - name: validation num_bytes: 170662 num_examples: 1000 download_size: 381525 dataset_size: 1695964 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://zil.ipipan.waw.pl/Scwad/CDSCorpus - **Repository:** - **Paper:** @inproceedings{wroblewska2017polish, title={Polish evaluation dataset for compositional distributional semantics models}, author={Wr{\'o}blewska, Alina and Krasnowska-Kiera{\'s}, Katarzyna}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={784--792}, year={2017} } - **Leaderboard:** https://klejbenchmark.com/leaderboard/ - **Point of Contact:** [email protected] ### Dataset Summary Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - pair_ID: id of sentences pairs - sentence_A: first sentence - sentence_B: second sentence for cdsc-e domain: - entailment_judgment: either 'NEUTRAL', 'CONTRADICTION' or 'ENTAILMENT' for cdsc-r domain: - relatedness_score: float representing a reletedness ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-NC-SA 4.0 ### Citation Information [More Information Needed] ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.

cdt

--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - bsd-3-clause multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: cdt dataset_info: features: - name: sentence dtype: string - name: target dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1104322 num_examples: 10041 - name: test num_bytes: 109681 num_examples: 1000 download_size: 375476 dataset_size: 1214003 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://2019.poleval.pl/index.php/tasks/ - **Repository:** https://github.com/ptaszynski/cyberbullying-Polish - **Paper:** - **Leaderboard:** https://klejbenchmark.com/leaderboard/ - **Point of Contact:** ### Dataset Summary The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sentence: an anonymized tweet in polish - target: 1 if tweet is described as bullying, 0 otherwise. The test set doesn't have labels so -1 is used instead. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information BSD 3-Clause ### Citation Information [More Information Needed] ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.

cedr

--- annotations_creators: - crowdsourced language_creators: - found language: - ru license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - multi-label-classification pretty_name: The Corpus for Emotions Detecting in Russian-language text sentences (CEDR) tags: - emotion-classification dataset_info: - config_name: main features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string splits: - name: train num_bytes: 1418355 num_examples: 7528 - name: test num_bytes: 350275 num_examples: 1882 download_size: 693026 dataset_size: 1768630 - config_name: enriched features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string - name: sentences list: list: - name: forma dtype: string - name: lemma dtype: string splits: - name: train num_bytes: 4792366 num_examples: 7528 - name: test num_bytes: 1182343 num_examples: 1882 download_size: 1822522 dataset_size: 5974709 --- # Dataset Card for [cedr] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [GitHub](https://github.com/sag111/CEDR) - **Repository:** [GitHub](https://github.com/sag111/CEDR) - **Paper:** [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S1877050921013247) - **Leaderboard:** - **Point of Contact:** [@sag111](mailto:[email protected]) ### Dataset Summary The Corpus for Emotions Detecting in Russian-language text sentences of different social sources (CEDR) contains 9410 comments labeled for 5 emotion categories (joy, sadness, surprise, fear, and anger). Here are 2 dataset configurations: - "main" - contains "text", "labels", and "source" features; - "enriched" - includes all "main" features and "sentences". Dataset with predefined train/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a text sentence in Russian from several sources with one or more emotion annotations (or no emotion at all). An example for an instance from the dataset is shown below: ``` { 'text': 'Забавно как люди в возрасте удивляются входящим звонкам на мобильник)', 'labels': [0], 'source': 'twitter', 'sentences': [ [ {'forma': 'Забавно', 'lemma': 'Забавно'}, {'forma': 'как', 'lemma': 'как'}, {'forma': 'люди', 'lemma': 'человек'}, {'forma': 'в', 'lemma': 'в'}, {'forma': 'возрасте', 'lemma': 'возраст'}, {'forma': 'удивляются', 'lemma': 'удивляться'}, {'forma': 'входящим', 'lemma': 'входить'}, {'forma': 'звонкам', 'lemma': 'звонок'}, {'forma': 'на', 'lemma': 'на'}, {'forma': 'мобильник', 'lemma': 'мобильник'}, {'forma': ')', 'lemma': ')'} ] ] } ``` Emotion label codes: {0: "joy", 1: "sadness", 2: "surprise", 3: "fear", 4: "anger"} ### Data Fields The main configuration includes: - text: the text of the sentence; - labels: the emotion annotations; - source: the tag name of the corresponding source In addition to the above, the raw data includes: - sentences: text tokenized and lemmatized with [udpipe](https://ufal.mff.cuni.cz/udpipe) - 'forma': the original word form; - 'lemma': the lemma of this word ### Data Splits The dataset includes a set of train/test splits. with 7528, and 1882 examples respectively. ## Dataset Creation ### Curation Rationale The formed dataset of examples consists of sentences in Russian from several sources (blogs, microblogs, news), which allows creating methods to analyse various types of texts. The created methodology for building the dataset based on applying a crowdsourcing service can be used to expand the number of examples to improve the accuracy of supervised classifiers. ### Source Data #### Initial Data Collection and Normalization Data was collected from several sources: posts of the Live Journal social network, texts of the online news agency Lenta.ru, and Twitter microblog posts. Only those sentences were selected that contained marker words from the dictionary of [the emotive vocabulary of the Russian language](http://lexrus.ru/default.aspx?p=2876). The authors manually formed a list of marker words for each emotion by choosing words from different categories of the dictionary. In total, 3069 sentences were selected from LiveJournal posts, 2851 sentences from Lenta.Ru, and 3490 sentencesfrom Twitter. After selection, sentences were offered to annotators for labeling. #### Who are the source language producers? Russian-speaking LiveJournal and Tweeter users, and authors of news articles on the site lenta.ru. ### Annotations #### Annotation process Annotating sentences with labels of their emotions was performed with the help of [a crowdsourcing platform](https://yandex.ru/support/toloka/index.html?lang=en). The annotators’ task was: “What emotions did the author express in the sentence?”. The annotators were allowed to put an arbitrary number of the following emotion labels: "joy", "sadness", "anger", "fear", and "surprise". If the accuracy of an annotator on the control sentences (including the trial run) became less than 70%, or if the accuracy was less than 66% over the last six control samples, the annotator was dismissed. Sentences were split into tasks and assigned to annotators so that each sentence was annotated at least three times. A label of a specific emotion was assigned to a sentence if put by more than half of the annotators. #### Who are the annotators? Only those of the 30% of the best-performing active users (by the platform’s internal rating) who spoke Russian and were over 18 years old were allowed into the annotation process. Moreover, before a platform user could be employed as an annotator, they underwent a training task, after which they were to mark 25 trial samples with more than 80% agreement compared to the annotation that the authors had performed themselves. ### Personal and Sensitive Information The text of the sentences may contain profanity. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Researchers at AI technology lab at NRC "Kurchatov Institute". See the author [list](https://www.sciencedirect.com/science/article/pii/S1877050921013247). ### Licensing Information The GitHub repository which houses this dataset has an Apache License 2.0. ### Citation Information If you have found our results helpful in your work, feel free to cite our publication. This is an updated version of the dataset, the collection and preparation of which is described here: ``` @article{sboev2021data, title={Data-Driven Model for Emotion Detection in Russian Texts}, author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman}, journal={Procedia Computer Science}, volume={190}, pages={637--642}, year={2021}, publisher={Elsevier} } ``` ### Contributions Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.

cfq

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Compositional Freebase Questions size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering - other task_ids: - open-domain-qa - closed-domain-qa paperswithcode_id: cfq tags: - compositionality dataset_info: - config_name: mcd1 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 37408806 num_examples: 95743 - name: test num_bytes: 5446503 num_examples: 11968 download_size: 267599061 dataset_size: 42855309 - config_name: mcd2 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39424657 num_examples: 95743 - name: test num_bytes: 5314019 num_examples: 11968 download_size: 267599061 dataset_size: 44738676 - config_name: mcd3 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 38316345 num_examples: 95743 - name: test num_bytes: 5244503 num_examples: 11968 download_size: 267599061 dataset_size: 43560848 - config_name: question_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39989433 num_examples: 98999 - name: test num_bytes: 5781561 num_examples: 10340 download_size: 267599061 dataset_size: 45770994 - config_name: question_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41217350 num_examples: 95654 - name: test num_bytes: 5179936 num_examples: 11909 download_size: 267599061 dataset_size: 46397286 - config_name: query_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40270175 num_examples: 100654 - name: test num_bytes: 5634924 num_examples: 9512 download_size: 267599061 dataset_size: 45905099 - config_name: query_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40811284 num_examples: 94600 - name: test num_bytes: 5268358 num_examples: 12589 download_size: 267599061 dataset_size: 46079642 - config_name: random_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41279218 num_examples: 95744 - name: test num_bytes: 5164923 num_examples: 11967 download_size: 267599061 dataset_size: 46444141 --- # Dataset Card for "cfq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/google-research/google-research/tree/master/cfq](https://github.com/google-research/google-research/tree/master/cfq) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** https://arxiv.org/abs/1912.09713 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.14 GB - **Size of the generated dataset:** 362.07 MB - **Total amount of disk used:** 2.50 GB ### Dataset Summary The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can also be used for semantic parsing. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English (`en`). ## Dataset Structure ### Data Instances #### mcd1 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 42.90 MB - **Total amount of disk used:** 310.49 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count(*) WHERE {\n?x0 a ns:people.person .\n?x0 ns:influence.influence_node.influenced M1 .\n?x0 ns:influence.influence_node.influenced M2 .\n?x0 ns:people.person.spouse_s/ns:people.marriage.spouse|ns:fictional_universe.fictional_character.married_to/ns:fictional_universe.marriage_of_fictional_characters.spouses ?x1 .\n?x1 a ns:film.cinematographer .\nFILTER ( ?x0 != ?x1 )\n}', 'question': 'Did a person marry a cinematographer , influence M1 , and influence M2' } ``` #### mcd2 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 44.77 MB - **Total amount of disk used:** 312.38 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count(*) WHERE {\n?x0 ns:people.person.parents|ns:fictional_universe.fictional_character.parents|ns:organization.organization.parent/ns:organization.organization_relationship.parent ?x1 .\n?x1 a ns:people.person .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M4 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M4\n}', 'question': "Did M1 and M5 employ M2 , M3 , and M4 and employ a person 's child" } ``` #### mcd3 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 43.60 MB - **Total amount of disk used:** 311.20 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_complexity_split - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 45.95 MB - **Total amount of disk used:** 313.55 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_pattern_split - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 46.12 MB - **Total amount of disk used:** 313.72 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` ### Data Fields The data fields are the same among all splits and configurations: - `question`: a `string` feature. - `query`: a `string` feature. ### Data Splits | name | train | test | |---------------------------|-------:|------:| | mcd1 | 95743 | 11968 | | mcd2 | 95743 | 11968 | | mcd3 | 95743 | 11968 | | query_complexity_split | 100654 | 9512 | | query_pattern_split | 94600 | 12589 | | question_complexity_split | 98999 | 10340 | | question_pattern_split | 95654 | 11909 | | random_split | 95744 | 11967 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Keysers2020, title={Measuring Compositional Generalization: A Comprehensive Method on Realistic Data}, author={Daniel Keysers and Nathanael Sch"{a}rli and Nathan Scales and Hylke Buisman and Daniel Furrer and Sergii Kashubin and Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and Olivier Bousquet}, booktitle={ICLR}, year={2020}, url={https://arxiv.org/abs/1912.09713.pdf}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@brainshawn](https://github.com/brainshawn) for adding this dataset.

chr_en

--- annotations_creators: - expert-generated - found - no-annotation language_creators: - found language: - chr - en license: - other multilinguality: - monolingual - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - fill-mask - text-generation - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: chren configs: - monolingual - monolingual_raw - parallel - parallel_raw dataset_info: - config_name: monolingual_raw features: - name: text_sentence dtype: string - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 1210828 num_examples: 5210 download_size: 28899321 dataset_size: 1210828 - config_name: parallel_raw features: - name: line_number dtype: string - name: sentence_pair dtype: translation: languages: - en - chr - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 5012923 num_examples: 14151 download_size: 28899321 dataset_size: 5012923 - config_name: monolingual features: - name: sentence dtype: string splits: - name: chr num_bytes: 882848 num_examples: 5210 - name: en5000 num_bytes: 615295 num_examples: 5000 - name: en10000 num_bytes: 1211645 num_examples: 10000 - name: en20000 num_bytes: 2432378 num_examples: 20000 - name: en50000 num_bytes: 6065780 num_examples: 49999 - name: en100000 num_bytes: 12130564 num_examples: 100000 download_size: 28899321 dataset_size: 23338510 - config_name: parallel features: - name: sentence_pair dtype: translation: languages: - en - chr splits: - name: train num_bytes: 3089658 num_examples: 11639 - name: dev num_bytes: 260409 num_examples: 1000 - name: out_dev num_bytes: 78134 num_examples: 256 - name: test num_bytes: 264603 num_examples: 1000 - name: out_test num_bytes: 80967 num_examples: 256 download_size: 28899321 dataset_size: 3773771 --- # Dataset Card for ChrEn ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn) - **Paper:** [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791) - **Point of Contact:** [[email protected]]([email protected]) ### Dataset Summary ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. ### Supported Tasks and Leaderboards The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`). ### Languages The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC). ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill. ### Licensing Information The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions). ### Citation Information ``` @inproceedings{zhang2020chren, title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization}, author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit}, booktitle={EMNLP2020}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

cifar10

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-10 pretty_name: Cifar10 dataset_info: features: - name: img dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck config_name: plain_text splits: - name: train num_bytes: 113854600 num_examples: 50000 - name: test num_bytes: 22772838 num_examples: 10000 download_size: 170498071 dataset_size: 136627438 --- # Dataset Card for CIFAR-10 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.cs.toronto.edu/~kriz/cifar.html - **Repository:** - **Paper:** Learning Multiple Layers of Features from Tiny Images by Alex Krizhevsky - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-10). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x201FA6EE748>, 'label': 0 } ``` ### Data Fields - img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - label: 0-9 with the following correspondence 0 airplane 1 automobile 2 bird 3 cat 4 deer 5 dog 6 frog 7 horse 8 ship 9 truck ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.

cifar100

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-100 pretty_name: Cifar100 dataset_info: features: - name: img dtype: image - name: fine_label dtype: class_label: names: '0': apple '1': aquarium_fish '2': baby '3': bear '4': beaver '5': bed '6': bee '7': beetle '8': bicycle '9': bottle '10': bowl '11': boy '12': bridge '13': bus '14': butterfly '15': camel '16': can '17': castle '18': caterpillar '19': cattle '20': chair '21': chimpanzee '22': clock '23': cloud '24': cockroach '25': couch '26': cra '27': crocodile '28': cup '29': dinosaur '30': dolphin '31': elephant '32': flatfish '33': forest '34': fox '35': girl '36': hamster '37': house '38': kangaroo '39': keyboard '40': lamp '41': lawn_mower '42': leopard '43': lion '44': lizard '45': lobster '46': man '47': maple_tree '48': motorcycle '49': mountain '50': mouse '51': mushroom '52': oak_tree '53': orange '54': orchid '55': otter '56': palm_tree '57': pear '58': pickup_truck '59': pine_tree '60': plain '61': plate '62': poppy '63': porcupine '64': possum '65': rabbit '66': raccoon '67': ray '68': road '69': rocket '70': rose '71': sea '72': seal '73': shark '74': shrew '75': skunk '76': skyscraper '77': snail '78': snake '79': spider '80': squirrel '81': streetcar '82': sunflower '83': sweet_pepper '84': table '85': tank '86': telephone '87': television '88': tiger '89': tractor '90': train '91': trout '92': tulip '93': turtle '94': wardrobe '95': whale '96': willow_tree '97': wolf '98': woman '99': worm - name: coarse_label dtype: class_label: names: '0': aquatic_mammals '1': fish '2': flowers '3': food_containers '4': fruit_and_vegetables '5': household_electrical_devices '6': household_furniture '7': insects '8': large_carnivores '9': large_man-made_outdoor_things '10': large_natural_outdoor_scenes '11': large_omnivores_and_herbivores '12': medium_mammals '13': non-insect_invertebrates '14': people '15': reptiles '16': small_mammals '17': trees '18': vehicles_1 '19': vehicles_2 config_name: cifar100 splits: - name: train num_bytes: 112751396 num_examples: 50000 - name: test num_bytes: 22605519 num_examples: 10000 download_size: 169001437 dataset_size: 135356915 --- # Dataset Card for CIFAR-100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html) - **Repository:** - **Paper:** [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19, 'coarse_label': 11 } ``` ### Data Fields - `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `fine_label`: an `int` classification label with the following mapping: `0`: apple `1`: aquarium_fish `2`: baby `3`: bear `4`: beaver `5`: bed `6`: bee `7`: beetle `8`: bicycle `9`: bottle `10`: bowl `11`: boy `12`: bridge `13`: bus `14`: butterfly `15`: camel `16`: can `17`: castle `18`: caterpillar `19`: cattle `20`: chair `21`: chimpanzee `22`: clock `23`: cloud `24`: cockroach `25`: couch `26`: cra `27`: crocodile `28`: cup `29`: dinosaur `30`: dolphin `31`: elephant `32`: flatfish `33`: forest `34`: fox `35`: girl `36`: hamster `37`: house `38`: kangaroo `39`: keyboard `40`: lamp `41`: lawn_mower `42`: leopard `43`: lion `44`: lizard `45`: lobster `46`: man `47`: maple_tree `48`: motorcycle `49`: mountain `50`: mouse `51`: mushroom `52`: oak_tree `53`: orange `54`: orchid `55`: otter `56`: palm_tree `57`: pear `58`: pickup_truck `59`: pine_tree `60`: plain `61`: plate `62`: poppy `63`: porcupine `64`: possum `65`: rabbit `66`: raccoon `67`: ray `68`: road `69`: rocket `70`: rose `71`: sea `72`: seal `73`: shark `74`: shrew `75`: skunk `76`: skyscraper `77`: snail `78`: snake `79`: spider `80`: squirrel `81`: streetcar `82`: sunflower `83`: sweet_pepper `84`: table `85`: tank `86`: telephone `87`: television `88`: tiger `89`: tractor `90`: train `91`: trout `92`: tulip `93`: turtle `94`: wardrobe `95`: whale `96`: willow_tree `97`: wolf `98`: woman `99`: worm - `coarse_label`: an `int` coarse classification label with following mapping: `0`: aquatic_mammals `1`: fish `2`: flowers `3`: food_containers `4`: fruit_and_vegetables `5`: household_electrical_devices `6`: household_furniture `7`: insects `8`: large_carnivores `9`: large_man-made_outdoor_things `10`: large_natural_outdoor_scenes `11`: large_omnivores_and_herbivores `12`: medium_mammals `13`: non-insect_invertebrates `14`: people `15`: reptiles `16`: small_mammals `17`: trees `18`: vehicles_1 `19`: vehicles_2 ### Data Splits | name |train|test| |----------|----:|---------:| |cifar100|50000| 10000| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.

circa

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: circa pretty_name: CIRCA tags: - question-answer-pair-classification dataset_info: features: - name: context dtype: string - name: question-X dtype: string - name: canquestion-X dtype: string - name: answer-Y dtype: string - name: judgements dtype: string - name: goldstandard1 dtype: class_label: names: '0': 'Yes' '1': 'No' '2': In the middle, neither yes nor no '3': Probably yes / sometimes yes '4': Probably no '5': Yes, subject to some conditions '6': Other '7': I am not sure how X will interpret Y’s answer - name: goldstandard2 dtype: class_label: names: '0': 'Yes' '1': 'No' '2': In the middle, neither yes nor no '3': Yes, subject to some conditions '4': Other splits: - name: train num_bytes: 8149489 num_examples: 34268 download_size: 7766077 dataset_size: 8149489 --- # Dataset Card for CIRCA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CIRCA homepage](https://github.com/google-research-datasets/circa) - **Repository:** [CIRCA repository](https://github.com/google-research-datasets/circa) - **Paper:** ["I’d rather just go to bed”: Understanding Indirect Answers](https://arxiv.org/abs/2010.03450) - **Point of Contact:** [Circa team, Google]([email protected]) ### Dataset Summary The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions. The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend). The following are the situational contexts for the dialogs in the data. ``` 1. X wants to know about Y’s food preferences 2. X wants to know what activities Y likes to do during weekends. 3. X wants to know what sorts of books Y likes to read. 4. Y has just moved into a neighbourhood and meets his/her new neighbour X. 5. X and Y are colleagues who are leaving work on a Friday at the same time. 6. X wants to know about Y's music preferences. 7. Y has just travelled from a different city to meet X. 8. X and Y are childhood neighbours who unexpectedly run into each other at a cafe. 9. Y has just told X that he/she is thinking of buying a flat in New York. 10. Y has just told X that he/she is considering switching his/her job. ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances The columns indicate: ``` 1. id : unique id for the question-answer pair 2. context : the social situation for the dialogue. One of 10 situations (see next section). Each situation is a dialogue between a person who poses the question (X) and the person who answers (Y). 3. question-X : the question posed by X 4. canquestion-X : a (automatically) rewritten version of question into declarative form Eg. Do you like Italian? --> I like Italian. See the paper for details. 5. answer-Y : the answer given by Y to X 6. judgements : the interpretations for the QA pair from 5 annotators. The value is a list of 5 strings, separated by the token ‘#’ 7. goldstandard1 : a gold standard majority judgement from the annotators. The value is the most common interpretation and picked by at least 3 (out of 5 annotators). When a majority judgement was not reached by the above criteria, the value is ‘NA’ 8. goldstandard2 : Here the labels ‘Probably yes / sometimes yes’, ‘Probably no', and 'I am not sure how X will interpret Y’s answer' are mapped respectively to ‘Yes’, ‘No’, and 'In the middle, neither yes nor no’ before computing the majority. Still the label must be given at least 3 times to become the majority choice. This method represents a less strict way of analyzing the interpretations. ``` ### Data Fields ``` id : 1 context : X wants to know about Y's food preferences. question-X : Are you vegan? canquestion-X : I am vegan. answer-Y : I love burgers too much. judgements : no#no#no#no#no goldstandard1 : no (label(s) used for the classification task) goldstandard2 : no (label(s) used for the classification task) ``` ### Data Splits There are no explicit train/val/test splits in this dataset. ## Dataset Creation ### Curation Rationale They revisited a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret ‘I’m starving.’ in response to ‘Hungry?’, even without direct cue words such as ‘yes’ and ‘no’. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today’s systems are only as sensitive to these pragmatic moves as their language model allows. They create and release the first large-scale English language corpus ‘Circa’ with 34,268 (polar question, indirect answer) pairs to enable progress on this task. ### Source Data #### Initial Data Collection and Normalization The QA pairs and judgements were collected using crowd annotations in three phases. They recruited English native speakers. The full descriptions of the data collection and quality control are present in [EMNLP 2020 paper](https://arxiv.org/pdf/2010.03450.pdf). Below is a brief overview only. Phase 1: In the first phase, they collected questions only. They designed 10 imaginary social situations which give the annotator a context for the conversation. Examples are: ``` ‘asking a friend for food preferences’ ‘meeting your childhood neighbour’ ‘your friend wants to buy a flat in New York’ ``` Annotators were asked to suggest questions which could be asked in each situation, such that each question only requires a ‘yes’ or ‘no’ answer. 100 annotators produced 5 questions each for the 10 situations, resulting in 5000 questions. Phase 2: Here they focused on eliciting answers to the questions. They sampled 3500 questions from our previous set. For each question, They collected possible answers from 10 different annotators. The annotators were instructed to provide a natural phrase or a sentence as the answer and to avoid the use of explicit ‘yes’ and ‘no’ words. Phase 3: Finally the QA pairs (34,268) were given to a third set of annotators who were asked how the question seeker would likely interpret a particular answer. These annotators had the following options to choose from: ``` * 'Yes' * 'Probably yes' / 'sometimes yes' * 'Yes, subject to some conditions' * 'No' * 'Probably no' * 'In the middle, neither yes nor no' * 'I am not sure how X will interpret Y's answer' ``` #### Who are the source language producers? The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset is the work of Annie Louis, Dan Roth, and Filip Radlinski from Google LLC. ### Licensing Information This dataset was made available under the Creative Commons Attribution 4.0 License. A full copy of the license can be found at https://creativecommons.org/licenses/by-sa/4.0/e and link to the license webpage if available. ### Citation Information ``` @InProceedings{louis_emnlp2020, author = "Annie Louis and Dan Roth and Filip Radlinski", title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", year = "2020", } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.

civil_comments

--- language: - en paperswithcode_id: null pretty_name: CivilComments dataset_info: features: - name: text dtype: string - name: toxicity dtype: float32 - name: severe_toxicity dtype: float32 - name: obscene dtype: float32 - name: threat dtype: float32 - name: insult dtype: float32 - name: identity_attack dtype: float32 - name: sexual_explicit dtype: float32 splits: - name: test num_bytes: 32073013 num_examples: 97320 - name: train num_bytes: 596835730 num_examples: 1804874 - name: validation num_bytes: 32326369 num_examples: 97320 download_size: 414947977 dataset_size: 661235112 --- # Dataset Card for "civil_comments" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 414.95 MB - **Size of the generated dataset:** 661.23 MB - **Total amount of disk used:** 1.08 GB ### Dataset Summary The comments in this dataset come from an archive of the Civil Comments platform, a commenting plugin for independent news sites. These public comments were created from 2015 - 2017 and appeared on approximately 50 English-language news sites across the world. When Civil Comments shut down in 2017, they chose to make the public comments available in a lasting open archive to enable future research. The original data, published on figshare, includes the public comment text, some associated metadata such as article IDs, timestamps and commenter-generated "civility" labels, but does not include user ids. Jigsaw extended this dataset by adding additional labels for toxicity and identity mentions. This data set is an exact replica of the data released for the Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This dataset is released under CC0, as is the underlying comment text. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 414.95 MB - **Size of the generated dataset:** 661.23 MB - **Total amount of disk used:** 1.08 GB An example of 'validation' looks as follows. ``` { "identity_attack": 0.0, "insult": 0.0, "obscene": 0.0, "severe_toxicity": 0.0, "sexual_explicit": 0.0, "text": "The public test.", "threat": 0.0, "toxicity": 0.0 } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `toxicity`: a `float32` feature. - `severe_toxicity`: a `float32` feature. - `obscene`: a `float32` feature. - `threat`: a `float32` feature. - `insult`: a `float32` feature. - `identity_attack`: a `float32` feature. - `sexual_explicit`: a `float32` feature. ### Data Splits | name | train |validation|test | |-------|------:|---------:|----:| |default|1804874| 97320|97320| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{DBLP:journals/corr/abs-1903-04561, author = {Daniel Borkan and Lucas Dixon and Jeffrey Sorensen and Nithum Thain and Lucy Vasserman}, title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification}, journal = {CoRR}, volume = {abs/1903.04561}, year = {2019}, url = {http://arxiv.org/abs/1903.04561}, archivePrefix = {arXiv}, eprint = {1903.04561}, timestamp = {Sun, 31 Mar 2019 19:01:24 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

clickbait_news_bg

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - bg license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking pretty_name: Clickbait/Fake News in Bulgarian dataset_info: features: - name: fake_news_score dtype: class_label: names: '0': legitimate '1': fake - name: click_bait_score dtype: class_label: names: '0': normal '1': clickbait - name: content_title dtype: string - name: content_url dtype: string - name: content_published_time dtype: string - name: content dtype: string splits: - name: train num_bytes: 24480402 num_examples: 2815 - name: validation num_bytes: 6752242 num_examples: 761 download_size: 8569575 dataset_size: 31232644 --- # Dataset Card for Clickbait/Fake News in Bulgarian ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Data Science Society / Case Fake News](https://gitlab.com/datasciencesociety/case_fake_news) - **Repository:** [Data Science Society / Case Fake News / Data](https://gitlab.com/datasciencesociety/case_fake_news/-/tree/master/data) - **Paper:** [This paper uses the dataset.](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This is a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned. The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks. The dataset was prepared for the Hack the Fake News hackathon. It was provided by the [Bulgarian Association of PR Agencies](http://www.bapra.bg/) and is available in [Gitlab](https://gitlab.com/datasciencesociety/). The corpus was automatically collected, and then annotated by students of journalism. The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; There are 761 testing examples. There is 98% correlation between fake news and clickbaits. One important aspect about the training dataset is that it contains many repetitions. This should not be surprising as it attempts to represent a natural distribution of factual vs. fake news on-line over a period of time. As publishers of fake news often have a group of websites that feature the same deceiving content, we should expect some repetition. In particular, the training dataset contains 434 unique articles with duplicates. These articles have three reposts each on average, with the most reposted article appearing 45 times. If we take into account the labels of the reposted articles, we can see that if an article is reposted, it is more likely to be fake news. The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322. (The dataset description is from the following [paper](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf).) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Bulgarian ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each entry in the dataset consists of the following elements: * `fake_news_score` - a label indicating whether the article is fake or not * `click_bait_score` - another label indicating whether it is a click-bait * `content_title` - article heading * `content_url` - URL of the original article * `content_published_time` - date of publication * `content` - article content ### Data Splits The **training dataset** contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; The **validation dataset** contains 761 testing examples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@tsvm](https://github.com/tsvm), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

climate_fever

--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|wikipedia - original task_categories: - text-classification - text-retrieval task_ids: - text-scoring - fact-checking - fact-checking-retrieval - semantic-similarity-scoring - multi-input-text-classification paperswithcode_id: climate-fever pretty_name: ClimateFever dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: claim_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO '3': DISPUTED - name: evidences list: - name: evidence_id dtype: string - name: evidence_label dtype: class_label: names: '0': SUPPORTS '1': REFUTES '2': NOT_ENOUGH_INFO - name: article dtype: string - name: evidence dtype: string - name: entropy dtype: float32 - name: votes list: string splits: - name: test num_bytes: 2429272 num_examples: 1535 download_size: 687133 dataset_size: 2429272 --- # Dataset Card for ClimateFever ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CLIMATE-FEVER homepage](http://climatefever.ai) - **Repository:** [CLIMATE-FEVER repository](https://github.com/tdiggelm/climate-fever-dataset) - **Paper:** [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://arxiv.org/abs/2012.00614) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Thomas Diggelmann](mailto:[email protected]) ### Dataset Summary A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in English, as found in real-world claims about climate-change on the Internet. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances ``` { "claim_id": "0", "claim": "Global warming is driving polar bears toward extinction", "claim_label": 0, # "SUPPORTS" "evidences": [ { "evidence_id": "Extinction risk from global warming:170", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Extinction risk from global warming", "evidence": "\"Recent Research Shows Human Activity Driving Earth Towards Global Extinction Event\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Global warming:14", "evidence_label": 0, # "SUPPORTS" "article": "Global warming", "evidence": "Environmental impacts include the extinction or relocation of many species as their ecosystems change, most immediately the environments of coral reefs, mountains, and the Arctic.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Global warming:178", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Global warming", "evidence": "Rising temperatures push bees to their physiological limits, and could cause the extinction of bee populations.", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] }, { "evidence_id": "Habitat destruction:61", "evidence_label": 0, # "SUPPORTS" "article": "Habitat destruction", "evidence": "Rising global temperatures, caused by the greenhouse effect, contribute to habitat destruction, endangering various species, such as the polar bear.", "entropy": 0.0, "votes": [ "SUPPORTS", "SUPPORTS", null, null, null ] }, { "evidence_id": "Polar bear:1328", "evidence_label": 2, # "NOT_ENOUGH_INFO" "article": "Polar bear", "evidence": "\"Bear hunting caught in global warming debate\".", "entropy": 0.6931471805599453, "votes": [ "SUPPORTS", "NOT_ENOUGH_INFO", null, null, null ] } ] } ``` ### Data Fields - `claim_id`: a `string` feature, unique claim identifier. - `claim`: a `string` feature, claim text. - `claim_label`: a `int` feature, overall label assigned to claim (based on evidence majority vote). The label correspond to 0: "supports", 1: "refutes", 2: "not enough info" and 3: "disputed". - `evidences`: a list of evidences with fields: - `evidence_id`: a `string` feature, unique evidence identifier. - `evidence_label`: a `int` feature, micro-verdict label. The label correspond to 0: "supports", 1: "refutes" and 2: "not enough info". - `article`: a `string` feature, title of source article (Wikipedia page). - `evidence`: a `string` feature, evidence sentence. - `entropy`: a `float32` feature, entropy reflecting uncertainty of `evidence_label`. - `votes`: a `list` of `string` features, corresponding to individual votes. ### Data Splits This benchmark dataset currently consists of a single data split `test` that consists of 1,535 claims or 7,675 claim-evidence pairs. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ```bibtex @misc{diggelmann2020climatefever, title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, year={2020}, eprint={2012.00614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@tdiggelm](https://github.com/tdiggelm) for adding this dataset.

clinc_oos

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: clinc150 pretty_name: CLINC150 dataset_info: - config_name: small features: - name: text dtype: string - name: intent dtype: class_label: names: '0': restaurant_reviews '1': nutrition_info '2': account_blocked '3': oil_change_how '4': time '5': weather '6': redeem_rewards '7': interest_rate '8': gas_type '9': accept_reservations '10': smart_home '11': user_name '12': report_lost_card '13': repeat '14': whisper_mode '15': what_are_your_hobbies '16': order '17': jump_start '18': schedule_meeting '19': meeting_schedule '20': freeze_account '21': what_song '22': meaning_of_life '23': restaurant_reservation '24': traffic '25': make_call '26': text '27': bill_balance '28': improve_credit_score '29': change_language '30': 'no' '31': measurement_conversion '32': timer '33': flip_coin '34': do_you_have_pets '35': balance '36': tell_joke '37': last_maintenance '38': exchange_rate '39': uber '40': car_rental '41': credit_limit '42': oos '43': shopping_list '44': expiration_date '45': routing '46': meal_suggestion '47': tire_change '48': todo_list '49': card_declined '50': rewards_balance '51': change_accent '52': vaccines '53': reminder_update '54': food_last '55': change_ai_name '56': bill_due '57': who_do_you_work_for '58': share_location '59': international_visa '60': calendar '61': translate '62': carry_on '63': book_flight '64': insurance_change '65': todo_list_update '66': timezone '67': cancel_reservation '68': transactions '69': credit_score '70': report_fraud '71': spending_history '72': directions '73': spelling '74': insurance '75': what_is_your_name '76': reminder '77': where_are_you_from '78': distance '79': payday '80': flight_status '81': find_phone '82': greeting '83': alarm '84': order_status '85': confirm_reservation '86': cook_time '87': damaged_card '88': reset_settings '89': pin_change '90': replacement_card_duration '91': new_card '92': roll_dice '93': income '94': taxes '95': date '96': who_made_you '97': pto_request '98': tire_pressure '99': how_old_are_you '100': rollover_401k '101': pto_request_status '102': how_busy '103': application_status '104': recipe '105': calendar_update '106': play_music '107': 'yes' '108': direct_deposit '109': credit_limit_change '110': gas '111': pay_bill '112': ingredients_list '113': lost_luggage '114': goodbye '115': what_can_i_ask_you '116': book_hotel '117': are_you_a_bot '118': next_song '119': change_speed '120': plug_type '121': maybe '122': w2 '123': oil_change_when '124': thank_you '125': shopping_list_update '126': pto_balance '127': order_checks '128': travel_alert '129': fun_fact '130': sync_device '131': schedule_maintenance '132': apr '133': transfer '134': ingredient_substitution '135': calories '136': current_location '137': international_fees '138': calculator '139': definition '140': next_holiday '141': update_playlist '142': mpg '143': min_payment '144': change_user_name '145': restaurant_suggestion '146': travel_notification '147': cancel '148': pto_used '149': travel_suggestion '150': change_volume splits: - name: train num_bytes: 394128 num_examples: 7600 - name: validation num_bytes: 160302 num_examples: 3100 - name: test num_bytes: 286970 num_examples: 5500 download_size: 1702451 dataset_size: 841400 - config_name: imbalanced features: - name: text dtype: string - name: intent dtype: class_label: names: '0': restaurant_reviews '1': nutrition_info '2': account_blocked '3': oil_change_how '4': time '5': weather '6': redeem_rewards '7': interest_rate '8': gas_type '9': accept_reservations '10': smart_home '11': user_name '12': report_lost_card '13': repeat '14': whisper_mode '15': what_are_your_hobbies '16': order '17': jump_start '18': schedule_meeting '19': meeting_schedule '20': freeze_account '21': what_song '22': meaning_of_life '23': restaurant_reservation '24': traffic '25': make_call '26': text '27': bill_balance '28': improve_credit_score '29': change_language '30': 'no' '31': measurement_conversion '32': timer '33': flip_coin '34': do_you_have_pets '35': balance '36': tell_joke '37': last_maintenance '38': exchange_rate '39': uber '40': car_rental '41': credit_limit '42': oos '43': shopping_list '44': expiration_date '45': routing '46': meal_suggestion '47': tire_change '48': todo_list '49': card_declined '50': rewards_balance '51': change_accent '52': vaccines '53': reminder_update '54': food_last '55': change_ai_name '56': bill_due '57': who_do_you_work_for '58': share_location '59': international_visa '60': calendar '61': translate '62': carry_on '63': book_flight '64': insurance_change '65': todo_list_update '66': timezone '67': cancel_reservation '68': transactions '69': credit_score '70': report_fraud '71': spending_history '72': directions '73': spelling '74': insurance '75': what_is_your_name '76': reminder '77': where_are_you_from '78': distance '79': payday '80': flight_status '81': find_phone '82': greeting '83': alarm '84': order_status '85': confirm_reservation '86': cook_time '87': damaged_card '88': reset_settings '89': pin_change '90': replacement_card_duration '91': new_card '92': roll_dice '93': income '94': taxes '95': date '96': who_made_you '97': pto_request '98': tire_pressure '99': how_old_are_you '100': rollover_401k '101': pto_request_status '102': how_busy '103': application_status '104': recipe '105': calendar_update '106': play_music '107': 'yes' '108': direct_deposit '109': credit_limit_change '110': gas '111': pay_bill '112': ingredients_list '113': lost_luggage '114': goodbye '115': what_can_i_ask_you '116': book_hotel '117': are_you_a_bot '118': next_song '119': change_speed '120': plug_type '121': maybe '122': w2 '123': oil_change_when '124': thank_you '125': shopping_list_update '126': pto_balance '127': order_checks '128': travel_alert '129': fun_fact '130': sync_device '131': schedule_maintenance '132': apr '133': transfer '134': ingredient_substitution '135': calories '136': current_location '137': international_fees '138': calculator '139': definition '140': next_holiday '141': update_playlist '142': mpg '143': min_payment '144': change_user_name '145': restaurant_suggestion '146': travel_notification '147': cancel '148': pto_used '149': travel_suggestion '150': change_volume splits: - name: train num_bytes: 546909 num_examples: 10625 - name: validation num_bytes: 160302 num_examples: 3100 - name: test num_bytes: 286970 num_examples: 5500 download_size: 2016773 dataset_size: 994181 - config_name: plus features: - name: text dtype: string - name: intent dtype: class_label: names: '0': restaurant_reviews '1': nutrition_info '2': account_blocked '3': oil_change_how '4': time '5': weather '6': redeem_rewards '7': interest_rate '8': gas_type '9': accept_reservations '10': smart_home '11': user_name '12': report_lost_card '13': repeat '14': whisper_mode '15': what_are_your_hobbies '16': order '17': jump_start '18': schedule_meeting '19': meeting_schedule '20': freeze_account '21': what_song '22': meaning_of_life '23': restaurant_reservation '24': traffic '25': make_call '26': text '27': bill_balance '28': improve_credit_score '29': change_language '30': 'no' '31': measurement_conversion '32': timer '33': flip_coin '34': do_you_have_pets '35': balance '36': tell_joke '37': last_maintenance '38': exchange_rate '39': uber '40': car_rental '41': credit_limit '42': oos '43': shopping_list '44': expiration_date '45': routing '46': meal_suggestion '47': tire_change '48': todo_list '49': card_declined '50': rewards_balance '51': change_accent '52': vaccines '53': reminder_update '54': food_last '55': change_ai_name '56': bill_due '57': who_do_you_work_for '58': share_location '59': international_visa '60': calendar '61': translate '62': carry_on '63': book_flight '64': insurance_change '65': todo_list_update '66': timezone '67': cancel_reservation '68': transactions '69': credit_score '70': report_fraud '71': spending_history '72': directions '73': spelling '74': insurance '75': what_is_your_name '76': reminder '77': where_are_you_from '78': distance '79': payday '80': flight_status '81': find_phone '82': greeting '83': alarm '84': order_status '85': confirm_reservation '86': cook_time '87': damaged_card '88': reset_settings '89': pin_change '90': replacement_card_duration '91': new_card '92': roll_dice '93': income '94': taxes '95': date '96': who_made_you '97': pto_request '98': tire_pressure '99': how_old_are_you '100': rollover_401k '101': pto_request_status '102': how_busy '103': application_status '104': recipe '105': calendar_update '106': play_music '107': 'yes' '108': direct_deposit '109': credit_limit_change '110': gas '111': pay_bill '112': ingredients_list '113': lost_luggage '114': goodbye '115': what_can_i_ask_you '116': book_hotel '117': are_you_a_bot '118': next_song '119': change_speed '120': plug_type '121': maybe '122': w2 '123': oil_change_when '124': thank_you '125': shopping_list_update '126': pto_balance '127': order_checks '128': travel_alert '129': fun_fact '130': sync_device '131': schedule_maintenance '132': apr '133': transfer '134': ingredient_substitution '135': calories '136': current_location '137': international_fees '138': calculator '139': definition '140': next_holiday '141': update_playlist '142': mpg '143': min_payment '144': change_user_name '145': restaurant_suggestion '146': travel_notification '147': cancel '148': pto_used '149': travel_suggestion '150': change_volume splits: - name: train num_bytes: 791255 num_examples: 15250 - name: validation num_bytes: 160302 num_examples: 3100 - name: test num_bytes: 286970 num_examples: 5500 download_size: 2509789 dataset_size: 1238527 --- # Dataset Card for CLINC150 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/clinc/oos-eval/) - **Repository:** [Github](https://github.com/clinc/oos-eval/) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/D19-1131) - **Leaderboard:** [PapersWithCode](https://paperswithcode.com/sota/text-classification-on-clinc-oos) - **Point of Contact:** ### Dataset Summary Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope (OOS), i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. It offers a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems. ### Supported Tasks and Leaderboards - `intent-classification`: This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data. [here](https://paperswithcode.com/sota/text-classification-on-clinc-oos). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'text' : 'can you walk me through setting up direct deposits to my bank of internet savings account', 'label' : 108 } ``` ### Data Fields - text : Textual data - label : 150 intent classes over 10 domains, the dataset contains one label for 'out-of-scope' intent. The Label Id to Label Name map is mentioned in the table below: | **Label Id** | **Label name** | |--- |--- | | 0 | restaurant_reviews | | 1 | nutrition_info | | 2 | account_blocked | | 3 | oil_change_how | | 4 | time | | 5 | weather | | 6 | redeem_rewards | | 7 | interest_rate | | 8 | gas_type | | 9 | accept_reservations | | 10 | smart_home | | 11 | user_name | | 12 | report_lost_card | | 13 | repeat | | 14 | whisper_mode | | 15 | what_are_your_hobbies | | 16 | order | | 17 | jump_start | | 18 | schedule_meeting | | 19 | meeting_schedule | | 20 | freeze_account | | 21 | what_song | | 22 | meaning_of_life | | 23 | restaurant_reservation | | 24 | traffic | | 25 | make_call | | 26 | text | | 27 | bill_balance | | 28 | improve_credit_score | | 29 | change_language | | 30 | no | | 31 | measurement_conversion | | 32 | timer | | 33 | flip_coin | | 34 | do_you_have_pets | | 35 | balance | | 36 | tell_joke | | 37 | last_maintenance | | 38 | exchange_rate | | 39 | uber | | 40 | car_rental | | 41 | credit_limit | | 42 | oos | | 43 | shopping_list | | 44 | expiration_date | | 45 | routing | | 46 | meal_suggestion | | 47 | tire_change | | 48 | todo_list | | 49 | card_declined | | 50 | rewards_balance | | 51 | change_accent | | 52 | vaccines | | 53 | reminder_update | | 54 | food_last | | 55 | change_ai_name | | 56 | bill_due | | 57 | who_do_you_work_for | | 58 | share_location | | 59 | international_visa | | 60 | calendar | | 61 | translate | | 62 | carry_on | | 63 | book_flight | | 64 | insurance_change | | 65 | todo_list_update | | 66 | timezone | | 67 | cancel_reservation | | 68 | transactions | | 69 | credit_score | | 70 | report_fraud | | 71 | spending_history | | 72 | directions | | 73 | spelling | | 74 | insurance | | 75 | what_is_your_name | | 76 | reminder | | 77 | where_are_you_from | | 78 | distance | | 79 | payday | | 80 | flight_status | | 81 | find_phone | | 82 | greeting | | 83 | alarm | | 84 | order_status | | 85 | confirm_reservation | | 86 | cook_time | | 87 | damaged_card | | 88 | reset_settings | | 89 | pin_change | | 90 | replacement_card_duration | | 91 | new_card | | 92 | roll_dice | | 93 | income | | 94 | taxes | | 95 | date | | 96 | who_made_you | | 97 | pto_request | | 98 | tire_pressure | | 99 | how_old_are_you | | 100 | rollover_401k | | 101 | pto_request_status | | 102 | how_busy | | 103 | application_status | | 104 | recipe | | 105 | calendar_update | | 106 | play_music | | 107 | yes | | 108 | direct_deposit | | 109 | credit_limit_change | | 110 | gas | | 111 | pay_bill | | 112 | ingredients_list | | 113 | lost_luggage | | 114 | goodbye | | 115 | what_can_i_ask_you | | 116 | book_hotel | | 117 | are_you_a_bot | | 118 | next_song | | 119 | change_speed | | 120 | plug_type | | 121 | maybe | | 122 | w2 | | 123 | oil_change_when | | 124 | thank_you | | 125 | shopping_list_update | | 126 | pto_balance | | 127 | order_checks | | 128 | travel_alert | | 129 | fun_fact | | 130 | sync_device | | 131 | schedule_maintenance | | 132 | apr | | 133 | transfer | | 134 | ingredient_substitution | | 135 | calories | | 136 | current_location | | 137 | international_fees | | 138 | calculator | | 139 | definition | | 140 | next_holiday | | 141 | update_playlist | | 142 | mpg | | 143 | min_payment | | 144 | change_user_name | | 145 | restaurant_suggestion | | 146 | travel_notification | | 147 | cancel | | 148 | pto_used | | 149 | travel_suggestion | | 150 | change_volume | ### Data Splits The dataset comes in different subsets: - `small` : Small, in which there are only 50 training queries per each in-scope intent - `imbalanced` : Imbalanced, in which intents have either 25, 50, 75, or 100 training queries. - `plus`: OOS+, in which there are 250 out-of-scope training examples, rather than 100. | name |train|validation|test| |----------|----:|---------:|---:| |small|7600| 3100| 5500 | |imbalanced|10625| 3100| 5500| |plus|15250| 3100| 5500| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{larson-etal-2019-evaluation, title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction", author = "Larson, Stefan and Mahendran, Anish and Peper, Joseph J. and Clarke, Christopher and Lee, Andrew and Hill, Parker and Kummerfeld, Jonathan K. and Leach, Kevin and Laurenzano, Michael A. and Tang, Lingjia and Mars, Jason", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", year = "2019", url = "https://www.aclweb.org/anthology/D19-1131" } ``` ### Contributions Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.

clue

--- annotations_creators: - other language_creators: - other language: - zh license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification - multiple-choice task_ids: - topic-classification - semantic-similarity-scoring - natural-language-inference - multiple-choice-qa paperswithcode_id: clue pretty_name: 'CLUE: Chinese Language Understanding Evaluation benchmark' tags: - coreference-nli - qa-nli dataset_info: - config_name: afqmc features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' - name: idx dtype: int32 splits: - name: test num_bytes: 378726 num_examples: 3861 - name: train num_bytes: 3396535 num_examples: 34334 - name: validation num_bytes: 426293 num_examples: 4316 download_size: 1195044 dataset_size: 4201554 - config_name: tnews features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': '100' '1': '101' '2': '102' '3': '103' '4': '104' '5': '106' '6': '107' '7': '108' '8': '109' '9': '110' '10': '112' '11': '113' '12': '114' '13': '115' '14': '116' - name: idx dtype: int32 splits: - name: test num_bytes: 810974 num_examples: 10000 - name: train num_bytes: 4245701 num_examples: 53360 - name: validation num_bytes: 797926 num_examples: 10000 download_size: 5123575 dataset_size: 5854601 - config_name: iflytek features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' '10': '10' '11': '11' '12': '12' '13': '13' '14': '14' '15': '15' '16': '16' '17': '17' '18': '18' '19': '19' '20': '20' '21': '21' '22': '22' '23': '23' '24': '24' '25': '25' '26': '26' '27': '27' '28': '28' '29': '29' '30': '30' '31': '31' '32': '32' '33': '33' '34': '34' '35': '35' '36': '36' '37': '37' '38': '38' '39': '39' '40': '40' '41': '41' '42': '42' '43': '43' '44': '44' '45': '45' '46': '46' '47': '47' '48': '48' '49': '49' '50': '50' '51': '51' '52': '52' '53': '53' '54': '54' '55': '55' '56': '56' '57': '57' '58': '58' '59': '59' '60': '60' '61': '61' '62': '62' '63': '63' '64': '64' '65': '65' '66': '66' '67': '67' '68': '68' '69': '69' '70': '70' '71': '71' '72': '72' '73': '73' '74': '74' '75': '75' '76': '76' '77': '77' '78': '78' '79': '79' '80': '80' '81': '81' '82': '82' '83': '83' '84': '84' '85': '85' '86': '86' '87': '87' '88': '88' '89': '89' '90': '90' '91': '91' '92': '92' '93': '93' '94': '94' '95': '95' '96': '96' '97': '97' '98': '98' '99': '99' '100': '100' '101': '101' '102': '102' '103': '103' '104': '104' '105': '105' '106': '106' '107': '107' '108': '108' '109': '109' '110': '110' '111': '111' '112': '112' '113': '113' '114': '114' '115': '115' '116': '116' '117': '117' '118': '118' - name: idx dtype: int32 splits: - name: test num_bytes: 2105688 num_examples: 2600 - name: train num_bytes: 10028613 num_examples: 12133 - name: validation num_bytes: 2157123 num_examples: 2599 download_size: 6505938 dataset_size: 14291424 - config_name: cmnli features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 2386837 num_examples: 13880 - name: train num_bytes: 67685309 num_examples: 391783 - name: validation num_bytes: 2051845 num_examples: 12241 download_size: 31404066 dataset_size: 72123991 - config_name: cluewsc2020 features: - name: idx dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': 'true' '1': 'false' - name: target struct: - name: span1_text dtype: string - name: span2_text dtype: string - name: span1_index dtype: int32 - name: span2_index dtype: int32 splits: - name: test num_bytes: 645649 num_examples: 2574 - name: train num_bytes: 288828 num_examples: 1244 - name: validation num_bytes: 72682 num_examples: 304 download_size: 281384 dataset_size: 1007159 - config_name: csl features: - name: idx dtype: int32 - name: corpus_id dtype: int32 - name: abst dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' - name: keyword sequence: string splits: - name: test num_bytes: 2463740 num_examples: 3000 - name: train num_bytes: 16478914 num_examples: 20000 - name: validation num_bytes: 2464575 num_examples: 3000 download_size: 3234594 dataset_size: 21407229 - config_name: cmrc2018 features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 3112066 num_examples: 2000 - name: train num_bytes: 15508110 num_examples: 10142 - name: validation num_bytes: 5183809 num_examples: 3219 - name: trial num_bytes: 1606931 num_examples: 1002 download_size: 3405146 dataset_size: 25410916 - config_name: drcd features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 4982402 num_examples: 3493 - name: train num_bytes: 37443458 num_examples: 26936 - name: validation num_bytes: 5222753 num_examples: 3524 download_size: 7264200 dataset_size: 47648613 - config_name: chid features: - name: idx dtype: int32 - name: candidates sequence: string - name: content sequence: string - name: answers sequence: - name: text dtype: string - name: candidate_id dtype: int32 splits: - name: test num_bytes: 11480463 num_examples: 3447 - name: train num_bytes: 252478178 num_examples: 84709 - name: validation num_bytes: 10117789 num_examples: 3218 download_size: 139199202 dataset_size: 274076430 - config_name: c3 features: - name: id dtype: int32 - name: context sequence: string - name: question dtype: string - name: choice sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1600166 num_examples: 1625 - name: train num_bytes: 9672787 num_examples: 11869 - name: validation num_bytes: 2990967 num_examples: 3816 download_size: 3495930 dataset_size: 14263920 - config_name: ocnli features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 376066 num_examples: 3000 - name: train num_bytes: 6187190 num_examples: 50437 - name: validation num_bytes: 366235 num_examples: 2950 download_size: 4359754 dataset_size: 6929491 - config_name: diagnostics features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 42400 num_examples: 514 download_size: 12062 dataset_size: 42400 --- # Dataset Card for "clue" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.cluebenchmarks.com - **Repository:** https://github.com/CLUEbenchmark/CLUE - **Paper:** [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://aclanthology.org/2020.coling-main.419/) - **Point of Contact:** [Zhenzhong Lan](mailto:[email protected]) - **Size of downloaded dataset files:** 198.68 MB - **Size of the generated dataset:** 486.34 MB - **Total amount of disk used:** 685.02 MB ### Dataset Summary CLUE, A Chinese Language Understanding Evaluation Benchmark (https://www.cluebenchmarks.com/) is a collection of resources for training, evaluating, and analyzing Chinese language understanding systems. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### afqmc - **Size of downloaded dataset files:** 1.20 MB - **Size of the generated dataset:** 4.20 MB - **Total amount of disk used:** 5.40 MB An example of 'validation' looks as follows. ``` { "idx": 0, "label": 0, "sentence1": "双十一花呗提额在哪", "sentence2": "里可以提花呗额度" } ``` #### c3 - **Size of downloaded dataset files:** 3.20 MB - **Size of the generated dataset:** 15.69 MB - **Total amount of disk used:** 18.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "比人的灵敏", "choice": ["没有人的灵敏", "和人的差不多", "和人的一样好", "比人的灵敏"], "context": "[\"许多动物的某些器官感觉特别灵敏，它们能比人类提前知道一些灾害事件的发生，例如，海洋中的水母能预报风暴，老鼠能事先躲避矿井崩塌或有害气体，等等。地震往往能使一些动物的某些感觉器官受到刺激而发生异常反应。如一个地区的重力发生变异，某些动物可能通过它们的平衡...", "id": 1, "question": "动物的器官感觉与人的相比有什么不同?" } ``` #### chid - **Size of downloaded dataset files:** 139.20 MB - **Size of the generated dataset:** 274.08 MB - **Total amount of disk used:** 413.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": { "candidate_id": [3, 5, 6, 1, 7, 4, 0], "text": ["碌碌无为", "无所作为", "苦口婆心", "得过且过", "未雨绸缪", "软硬兼施", "传宗接代"] }, "candidates": "[\"传宗接代\", \"得过且过\", \"咄咄逼人\", \"碌碌无为\", \"软硬兼施\", \"无所作为\", \"苦口婆心\", \"未雨绸缪\", \"和衷共济\", \"人老珠黄\"]...", "content": "[\"谈到巴萨目前的成就，瓜迪奥拉用了“坚持”两个字来形容。自从上世纪90年代克鲁伊夫带队以来，巴萨就坚持每年都有拉玛西亚球员进入一队的传统。即便是范加尔时代，巴萨强力推出的“巴萨五鹰”德拉·佩纳、哈维、莫雷罗、罗杰·加西亚和贝拉乌桑几乎#idiom0000...", "idx": 0 } ``` #### cluewsc2020 - **Size of downloaded dataset files:** 0.28 MB - **Size of the generated dataset:** 1.03 MB - **Total amount of disk used:** 1.29 MB An example of 'train' looks as follows. ``` { "idx": 0, "label": 1, "target": { "span1_index": 3, "span1_text": "伤口", "span2_index": 27, "span2_text": "它们" }, "text": "裂开的伤口涂满尘土，里面有碎石子和木头刺，我小心翼翼把它们剔除出去。" } ``` #### cmnli - **Size of downloaded dataset files:** 31.40 MB - **Size of the generated dataset:** 72.12 MB - **Total amount of disk used:** 103.53 MB An example of 'train' looks as follows. ``` { "idx": 0, "label": 0, "sentence1": "从概念上讲，奶油略读有两个基本维度-产品和地理。", "sentence2": "产品和地理位置是使奶油撇油起作用的原因。" } ``` ### Data Fields The data fields are the same among all splits. #### afqmc - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `0` (0), `1` (1). - `idx`: a `int32` feature. #### c3 - `id`: a `int32` feature. - `context`: a `list` of `string` features. - `question`: a `string` feature. - `choice`: a `list` of `string` features. - `answer`: a `string` feature. #### chid - `idx`: a `int32` feature. - `candidates`: a `list` of `string` features. - `content`: a `list` of `string` features. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `candidate_id`: a `int32` feature. #### cluewsc2020 - `idx`: a `int32` feature. - `text`: a `string` feature. - `label`: a classification label, with possible values including `true` (0), `false` (1). - `span1_text`: a `string` feature. - `span2_text`: a `string` feature. - `span1_index`: a `int32` feature. - `span2_index`: a `int32` feature. #### cmnli - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `neutral` (0), `entailment` (1), `contradiction` (2). - `idx`: a `int32` feature. ### Data Splits | name |train |validation|test | |-----------|-----:|---------:|----:| |afqmc | 34334| 4316| 3861| |c3 | 11869| 3816| 3892| |chid | 84709| 3218| 3231| |cluewsc2020| 1244| 304| 290| |cmnli |391783| 12241|13880| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{xu-etal-2020-clue, title = "{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark", author = "Xu, Liang and Hu, Hai and Zhang, Xuanwei and Li, Lu and Cao, Chenjie and Li, Yudong and Xu, Yechen and Sun, Kai and Yu, Dian and Yu, Cong and Tian, Yin and Dong, Qianqian and Liu, Weitang and Shi, Bo and Cui, Yiming and Li, Junyi and Zeng, Jun and Wang, Rongzhao and Xie, Weijian and Li, Yanting and Patterson, Yina and Tian, Zuoyu and Zhang, Yiwen and Zhou, He and Liu, Shaoweihua and Zhao, Zhe and Zhao, Qipeng and Yue, Cong and Zhang, Xinrui and Yang, Zhengliang and Richardson, Kyle and Lan, Zhenzhong", booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2020.coling-main.419", doi = "10.18653/v1/2020.coling-main.419", pages = "4762--4772", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@JetRunner](https://github.com/JetRunner) for adding this dataset.

cmrc2018

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - zh license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: cmrc-2018 pretty_name: Chinese Machine Reading Comprehension 2018 dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 15508110 num_examples: 10142 - name: validation num_bytes: 5183809 num_examples: 3219 - name: test num_bytes: 1606931 num_examples: 1002 download_size: 11508117 dataset_size: 22298850 --- # Dataset Card for "cmrc2018" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/ymcui/cmrc2018](https://github.com/ymcui/cmrc2018) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 11.50 MB - **Size of the generated dataset:** 22.31 MB - **Total amount of disk used:** 33.83 MB ### Dataset Summary A Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 11.50 MB - **Size of the generated dataset:** 22.31 MB - **Total amount of disk used:** 33.83 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [11, 11], "text": ["光荣和ω-force", "光荣和ω-force"] }, "context": "\"《战国无双3》（）是由光荣和ω-force开发的战国无双系列的正统第三续作。本作以三大故事为主轴，分别是以武田信玄等人为主的《关东三国志》，织田信长等人为主的《战国三杰》，石田三成等人为主的《关原的年轻武者》，丰富游戏内的剧情。此部份专门介绍角色，欲知武...", "id": "DEV_0_QUERY_0", "question": "《战国无双3》是由哪两个公司合作开发的？" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | validation | test | | ------- | ----: | ---------: | ---: | | default | 10142 | 3219 | 1002 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{cui-emnlp2019-cmrc2018, title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension", author = "Cui, Yiming and Liu, Ting and Che, Wanxiang and Xiao, Li and Chen, Zhipeng and Ma, Wentao and Wang, Shijin and Hu, Guoping", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1600", doi = "10.18653/v1/D19-1600", pages = "5886--5891", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

cmu_hinglish_dog

--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - en - hi license: - cc-by-sa-3.0 - gfdl multilinguality: - multilingual - translation pretty_name: CMU Document Grounded Conversations size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] dataset_info: features: - name: date dtype: string - name: docIdx dtype: int64 - name: translation dtype: translation: languages: - en - hi_en - name: uid dtype: string - name: utcTimestamp dtype: string - name: rating dtype: int64 - name: status dtype: int64 - name: uid1LogInTime dtype: string - name: uid1LogOutTime dtype: string - name: uid1response struct: - name: response sequence: int64 - name: type dtype: string - name: uid2response struct: - name: response sequence: int64 - name: type dtype: string - name: user2_id dtype: string - name: whoSawDoc sequence: string - name: wikiDocumentIdx dtype: int64 splits: - name: train num_bytes: 3142398 num_examples: 8060 - name: test num_bytes: 379521 num_examples: 960 - name: validation num_bytes: 368726 num_examples: 942 download_size: 8749685 dataset_size: 3890645 --- # Dataset Card for CMU Document Grounded Conversations ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CMU Hinglish DoG](http://festvox.org/cedar/data/notyet/) - **Repository:** [CMU Document Grounded Conversations (English version)](https://github.com/festvox/datasets-CMU_DoG) - **Paper:** [CMU Document Grounded Conversations (English version)](https://arxiv.org/pdf/1809.07358.pdf) - **Point of Contact:** ### Dataset Summary This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English versions. Can be used for Translating between the two. The dataset has been provided by Prof. Alan Black's group from CMU. ### Supported Tasks and Leaderboards - `abstractive-mt` ### Languages ## Dataset Structure ### Data Instances A typical data point comprises a Hinglish text, with key `hi_en` and its English version with key `en`. The `docIdx` contains the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document. The `uid` has the user id of this utterance. An example from the CMU_Hinglish_DoG train set looks as follows: ``` {'rating': 2, 'wikiDocumentIdx': 13, 'utcTimestamp': '2018-03-16T17:48:22.037Z', 'uid': 'user2', 'date': '2018-03-16T17:47:21.964Z', 'uid2response': {'response': [1, 2, 3, 5], 'type': 'finish'}, 'uid1LogInTime': '2018-03-16T17:47:21.964Z', 'user2_id': 'USR664', 'uid1LogOutTime': '2018-03-16T18:02:29.072Z', 'whoSawDoc': ['user1', 'user2'], 'status': 1, 'docIdx': 0, 'uid1response': {'response': [1, 2, 3, 4], 'type': 'finish'}, 'translation': {'en': 'The director is Zack Snyder, 27% Rotten Tomatoes, 4.9/10.', 'hi_en': 'Zack Snyder director hai, 27% Rotten Tomatoes, 4.9/10.'}} ``` ### Data Fields - `date`: the time the file is created, as a string - `docIdx`: the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document. - `translation`: - `hi_en`: The text in Hinglish - `en`: The text in English - `uid`: the user id of this utterance. - `utcTimestamp`: the server utc timestamp of this utterance, as a string - `rating`: A number from 1 or 2 or 3. A larger number means the quality of the conversation is better. - `status`: status as an integer - `uid1LogInTime`: optional login time of user 1, as a string - `uid1LogOutTime`: optional logout time of user 1, as a string - `uid1response`: a json object contains the status and response of user after finishing the conversation. Fields in the object includes: - `type`: should be one of ['finish', 'abandon','abandonWithouAnsweringFeedbackQuestion']. 'finish' means the user successfully finishes the conversation, either by completing 12 or 15 turns or in the way that the other user leaves the conversation first. 'abandon' means the user abandons the conversation in the middle, but entering the feedback page. 'abandonWithouAnsweringFeedbackQuestion' means the user just disconnects or closes the web page without providing the feedback. - `response`: the answer to the post-conversation questions. The worker can choose multiple of them. The options presented to the user are as follows: For type 'finish' 1: The conversation is understandable. 2: The other user is actively responding me. 3: The conversation goes smoothly. For type 'abandon' 1: The other user is too rude. 2: I don't know how to proceed with the conversation. 3: The other user is not responding to me. For users given the document 4: I have watched the movie before. 5: I have not watched the movie before. For the users without the document 4: I will watch the movie after the other user's introduction. 5: I will not watch the movie after the other user's introduction. - `uid2response`: same as uid1response - `user2_id`: the generated user id of user 2 - `whoSawDoc`: Should be one of ['user1'], ['user2'], ['user1', 'user2']. Indicating which user read the document. - `wikiDocumentId`: the index of the wiki document. ### Data Splits | name |train|validation|test| |----------|----:|---------:|---:| |CMU DOG | 8060| 942| 960| ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data The Hinglish dataset is derived from the original CMU DoG (Document Grounded Conversations Dataset). More info about that can be found in the [repo](https://github.com/festvox/datasets-CMU_DoG) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was initially created by Prof Alan W Black's group at CMU ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @inproceedings{ cmu_dog_emnlp18, title={A Dataset for Document Grounded Conversations}, author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W}, year={2018}, booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing} } ``` ### Contributions Thanks to [@Ishan-Kumar2](https://github.com/Ishan-Kumar2) for adding this dataset.

cnn_dailymail

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: cnn-daily-mail-1 pretty_name: CNN / Daily Mail train-eval-index: - config: 3.0.0 task: summarization task_id: summarization splits: eval_split: test col_mapping: article: text highlights: target dataset_info: - config_name: 3.0.0 features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string splits: - name: train num_bytes: 1261704133 num_examples: 287113 - name: validation num_bytes: 57732436 num_examples: 13368 - name: test num_bytes: 49925756 num_examples: 11490 download_size: 585439472 dataset_size: 1369362325 - config_name: 1.0.0 features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string splits: - name: train num_bytes: 1261704133 num_examples: 287113 - name: validation num_bytes: 57732436 num_examples: 13368 - name: test num_bytes: 49925756 num_examples: 11490 download_size: 585439472 dataset_size: 1369362325 - config_name: 2.0.0 features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string splits: - name: train num_bytes: 1261704133 num_examples: 287113 - name: validation num_bytes: 57732436 num_examples: 13368 - name: test num_bytes: 49925756 num_examples: 11490 download_size: 585439472 dataset_size: 1369362325 --- # Dataset Card for CNN Dailymail Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [CNN / DailyMail Dataset repository](https://github.com/abisee/cnn-dailymail) - **Paper:** [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), [Get To The Point: Summarization with Pointer-Generator Networks](https://www.aclweb.org/anthology/K16-1028.pdf) - **Leaderboard:** [Papers with Code leaderboard for CNN / Dailymail Dataset](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) - **Point of Contact:** [Abigail See](mailto:[email protected]) ### Dataset Summary The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering. ### Supported Tasks and Leaderboards - 'summarization': [Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models. ### Languages The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data. ## Dataset Structure ### Data Instances For each instance, there is a string for the article, a string for the highlights, and a string for the id. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=cnn_dailymail&config=3.0.0) to explore more examples. ``` {'id': '0054d6d30dbcad772e20b22771153a2a9cbeaf62', 'article': '(CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.' 'highlights': 'The elderly woman suffered from diabetes and hypertension, ship's doctors say .\nPreviously, 86 passengers had fallen ill on the ship, Agencia Brasil says .'} ``` The average token count for the articles and the highlights are provided below: | Feature | Mean Token Count | | ---------- | ---------------- | | Article | 781 | | Highlights | 56 | ### Data Fields - `id`: a string containing the heximal formated SHA1 hash of the url where the story was retrieved from - `article`: a string containing the body of the news article - `highlights`: a string containing the highlight of the article as written by the article author ### Data Splits The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset. | Dataset Split | Number of Instances in Split | | ------------- | ------------------------------------------- | | Train | 287,113 | | Validation | 13,368 | | Test | 11,490 | ## Dataset Creation ### Curation Rationale Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels. ### Source Data #### Initial Data Collection and Normalization The data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015. The code for the original data collection is available at <https://github.com/deepmind/rc-data>. The articles were downloaded using archives of <www.cnn.com> and <www.dailymail.co.uk> on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <https://cs.nyu.edu/~kcho/DMQA/>. An updated version of the code that does not anonymize the data is available at <https://github.com/abisee/cnn-dailymail>. Hermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them. #### Who are the source language producers? The text was written by journalists at CNN and the Daily Mail. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Version 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences. This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated. ### Discussion of Biases [Bordia and Bowman (2019)](https://www.aclweb.org/anthology/N19-3002.pdf) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'. Because the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published. ### Other Known Limitations News articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article [(Kryściński et al, 2019)](https://www.aclweb.org/anthology/D19-1051.pdf). [Chen et al (2016)](https://www.aclweb.org/anthology/P16-1223.pdf) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors. It should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles. ## Additional Information ### Dataset Curators The data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format. Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions. The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040. ### Licensing Information The CNN / Daily Mail dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @inproceedings{see-etal-2017-get, title = "Get To The Point: Summarization with Pointer-Generator Networks", author = "See, Abigail and Liu, Peter J. and Manning, Christopher D.", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-1099", doi = "10.18653/v1/P17-1099", pages = "1073--1083", abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.", } ``` ``` @inproceedings{DBLP:conf/nips/HermannKGEKSB15, author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom}, title={Teaching Machines to Read and Comprehend}, year={2015}, cdate={1420070400000}, pages={1693-1701}, url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend}, booktitle={NIPS}, crossref={conf/nips/2015} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.

coached_conv_pref

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - other - text-generation - fill-mask - token-classification task_ids: - dialogue-modeling - parsing paperswithcode_id: coached-conversational-preference-elicitation pretty_name: Coached Conversational Preference Elicitation tags: - Conversational Recommendation dataset_info: features: - name: conversationId dtype: string - name: utterances sequence: - name: index dtype: int32 - name: speaker dtype: class_label: names: '0': USER '1': ASSISTANT - name: text dtype: string - name: segments sequence: - name: startIndex dtype: int32 - name: endIndex dtype: int32 - name: text dtype: string - name: annotations sequence: - name: annotationType dtype: class_label: names: '0': ENTITY_NAME '1': ENTITY_PREFERENCE '2': ENTITY_DESCRIPTION '3': ENTITY_OTHER - name: entityType dtype: class_label: names: '0': MOVIE_GENRE_OR_CATEGORY '1': MOVIE_OR_SERIES '2': PERSON '3': SOMETHING_ELSE config_name: coached_conv_pref splits: - name: train num_bytes: 2295579 num_examples: 502 download_size: 5191959 dataset_size: 2295579 --- # Dataset Card for Coached Conversational Preference Elicitation ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Coached Conversational Preference Elicitation Homepage](https://research.google/tools/datasets/coached-conversational-preference-elicitation/) - **Repository:** [Coached Conversational Preference Elicitation Repository](https://github.com/google-research-datasets/ccpe) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/W19-5941/) ### Dataset Summary A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities. ### Supported Tasks and Leaderboards * `other-other-Conversational Recommendation`: The dataset can be used to train a model for Conversational recommendation, which consists in Coached Conversation Preference Elicitation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A typical data point comprises of a series of utterances between the 'assistant' and the 'user'. Each such utterance is annotated into categories mentioned in data fields. An example from the Coached Conversational Preference Elicitation dataset looks as follows: ``` {'conversationId': 'CCPE-6faee', 'utterances': {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'segments': [{'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [0], 'entityType': [0]}, {'annotationType': [1], 'entityType': [0]}], 'endIndex': [20, 27], 'startIndex': [14, 0], 'text': ['comedy', 'I really like comedy movies']}, {'annotations': [{'annotationType': [0], 'entityType': [0]}], 'endIndex': [24], 'startIndex': [16], 'text': ['comedies']}, {'annotations': [{'annotationType': [1], 'entityType': [0]}], 'endIndex': [15], 'startIndex': [0], 'text': ['I love to laugh']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [0], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [21, 21], 'startIndex': [8, 0], 'text': ['Step Brothers', 'I liked Step Brothers']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [1], 'entityType': [1]}], 'endIndex': [32], 'startIndex': [0], 'text': ['Had some amazing one-liners that']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [0], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [15, 15], 'startIndex': [13, 0], 'text': ['RV', "I don't like RV"]}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [1], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [48, 66], 'startIndex': [18, 50], 'text': ['It was just so slow and boring', "I didn't like it"]}, {'annotations': [{'annotationType': [0], 'entityType': [1]}], 'endIndex': [63], 'startIndex': [33], 'text': ['Jurassic World: Fallen Kingdom']}, {'annotations': [{'annotationType': [0], 'entityType': [1]}, {'annotationType': [3], 'entityType': [1]}], 'endIndex': [52, 52], 'startIndex': [22, 0], 'text': ['Jurassic World: Fallen Kingdom', 'I have seen the movie Jurassic World: Fallen Kingdom']}, {'annotations': [{'annotationType': [], 'entityType': []}], 'endIndex': [0], 'startIndex': [0], 'text': ['']}, {'annotations': [{'annotationType': [1], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}, {'annotationType': [1], 'entityType': [1]}], 'endIndex': [24, 125, 161], 'startIndex': [0, 95, 135], 'text': ['I really like the actors', 'I just really like the scenery', 'the dinosaurs were awesome']}], 'speaker': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], 'text': ['What kinds of movies do you like?', 'I really like comedy movies.', 'Why do you like comedies?', "I love to laugh and comedy movies, that's their whole purpose. Make you laugh.", 'Alright, how about a movie you liked?', 'I liked Step Brothers.', 'Why did you like that movie?', 'Had some amazing one-liners that still get used today even though the movie was made awhile ago.', 'Well, is there a movie you did not like?', "I don't like RV.", 'Why not?', "And I just didn't It was just so slow and boring. I didn't like it.", 'Ok, then have you seen the movie Jurassic World: Fallen Kingdom', 'I have seen the movie Jurassic World: Fallen Kingdom.', 'What is it about these kinds of movies that you like or dislike?', 'I really like the actors. I feel like they were doing their best to make the movie better. And I just really like the scenery, and the the dinosaurs were awesome.']}} ``` ### Data Fields Each conversation has the following fields: * `conversationId`: A unique random ID for the conversation. The ID has no meaning. * `utterances`: An array of utterances by the workers. Each utterance has the following fields: * `index`: A 0-based index indicating the order of the utterances in the conversation. * `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance. * `text`: The raw text as written by the ASSISTANT, or transcribed from the spoken recording of USER. * `segments`: An array of semantic annotations of spans in the text. Each semantic annotation segment has the following fields: * `startIndex`: The position of the start of the annotation in the utterance text. * `endIndex`: The position of the end of the annotation in the utterance text. * `text`: The raw text that has been annotated. * `annotations`: An array of annotation details for this segment. Each annotation has two fields: * `annotationType`: The class of annotation (see ontology below). * `entityType`: The class of the entity to which the text refers (see ontology below). **EXPLANATION OF ONTOLOGY** In the corpus, preferences and the entities that these preferences refer to are annotated with an annotation type as well as an entity type. Annotation types fall into four categories: * `ENTITY_NAME` (0): These mark the names of relevant entities mentioned. * `ENTITY_PREFERENCE` (1): These are defined as statements indicating that the dialog participant does or does not like the relevant entity in general, or that they do or do not like some aspect of the entity. This may also be thought of the participant having some sentiment about what is being discussed. * `ENTITY_DESCRIPTION` (2): Neutral descriptions that describe an entity but do not convey an explicit liking or disliking. * `ENTITY_OTHER` (3): Other relevant statements about an entity that convey relevant information of how the participant relates to the entity but do not provide a sentiment. Most often, these relate to whether a participant has seen a particular movie, or knows a lot about a given entity. Entity types are marked as belonging to one of four categories: * `MOVIE_GENRE_OR_CATEGORY` (0): For genres or general descriptions that capture a particular type or style of movie. * `MOVIE_OR_SERIES` (1): For the full or partial name of a movie or series of movies. * `PERSON` (2): For the full or partial name of an actual person. * `SOMETHING_ELSE ` (3): For other important proper nouns, such as the names of characters or locations. ### Data Splits There is a single split of the dataset named 'train' which contains the whole datset. | | Train | | ------------------- | ----- | | Input Conversations | 502 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{radlinski-etal-2019-ccpe, title = {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences}, author = {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi}, booktitle = {Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue ({SIGDIAL})}, year = 2019 } ``` ### Contributions Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset.