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huggingface-datasets-embeddings

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5baf6e169b7ad2c6ef04ff6c5491feeca09bb544

# Dataset Card for "jeopardy" ## 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.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/](https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/) - **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:** 12.72 MB - **Size of the generated dataset:** 36.13 MB - **Total amount of disk used:** 48.85 MB ### Dataset Summary Dataset containing 216,930 Jeopardy questions, answers and other data. The json file is an unordered list of questions where each question has 'category' : the question category, e.g. "HISTORY" 'value' : integer $ value of the question as string, e.g. "200" Note: This is "None" for Final Jeopardy! and Tiebreaker questions 'question' : text of question Note: This sometimes contains hyperlinks and other things messy text such as when there's a picture or video question 'answer' : text of answer 'round' : one of "Jeopardy!","Double Jeopardy!","Final Jeopardy!" or "Tiebreaker" Note: Tiebreaker questions do happen but they're very rare (like once every 20 years) 'show_number' : int of show number, e.g '4680' 'air_date' : string of the show air date in format YYYY-MM-DD ### 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:** 12.72 MB - **Size of the generated dataset:** 36.13 MB - **Total amount of disk used:** 48.85 MB An example of 'train' looks as follows. ``` { "air_date": "2004-12-31", "answer": "Hattie McDaniel (for her role in Gone with the Wind)", "category": "EPITAPHS & TRIBUTES", "question": "'1939 Oscar winner: \"...you are a credit to your craft, your race and to your family\"'", "round": "Jeopardy!", "show_number": 4680, "value": 2000 } ``` ### Data Fields The data fields are the same among all splits. #### default - `category`: a `string` feature. - `air_date`: a `string` feature. - `question`: a `string` feature. - `value`: a `int32` feature. - `answer`: a `string` feature. - `round`: a `string` feature. - `show_number`: a `int32` feature. ### Data Splits | name |train | |-------|-----:| |default|216930| ## 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 ``` ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

jeopardy

2022-03-02T23:29:22+00:00

{"language": ["en"], "pretty_name": "jeopardy", "dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "air_date", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "value", "dtype": "int32"}, {"name": "answer", "dtype": "string"}, {"name": "round", "dtype": "string"}, {"name": "show_number", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 35916080, "num_examples": 216930}], "download_size": 55554625, "dataset_size": 35916080}}

2024-01-18T11:06:50+00:00

TAGS #language-English #region-us

Dataset Card for "jeopardy" =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 12.72 MB * Size of the generated dataset: 36.13 MB * Total amount of disk used: 48.85 MB ### Dataset Summary Dataset containing 216,930 Jeopardy questions, answers and other data. The json file is an unordered list of questions where each question has 'category' : the question category, e.g. "HISTORY" 'value' : integer $ value of the question as string, e.g. "200" Note: This is "None" for Final Jeopardy! and Tiebreaker questions 'question' : text of question Note: This sometimes contains hyperlinks and other things messy text such as when there's a picture or video question 'answer' : text of answer 'round' : one of "Jeopardy!","Double Jeopardy!","Final Jeopardy!" or "Tiebreaker" Note: Tiebreaker questions do happen but they're very rare (like once every 20 years) 'show\_number' : int of show number, e.g '4680' 'air\_date' : string of the show air date in format YYYY-MM-DD ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 12.72 MB * Size of the generated dataset: 36.13 MB * Total amount of disk used: 48.85 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'category': a 'string' feature. * 'air\_date': a 'string' feature. * 'question': a 'string' feature. * 'value': a 'int32' feature. * 'answer': a 'string' feature. * 'round': a 'string' feature. * 'show\_number': a 'int32' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.

8b9fb0e6b3456ed57c9ba5524ef9e26b01177750

# Dataset Card for JFLEG ## 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/keisks/jfleg) - **Repository:** [Github](https://github.com/keisks/jfleg) - **Paper:** [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/) - **Leaderboard:** [Leaderboard](https://github.com/keisks/jfleg#leader-board-published-results) - **Point of Contact:** Courtney Napoles, Keisuke Sakaguchi ### Dataset Summary JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC systems with respect to fluency (extent to which a text is native-sounding) as well as grammaticality. For each source document, there are four human-written corrections. ### Supported Tasks and Leaderboards Grammatical error correction. ### Languages English (native as well as L2 writers) ## Dataset Structure ### Data Instances Each instance contains a source sentence and four corrections. For example: ```python { 'sentence': "They are moved by solar energy ." 'corrections': [ "They are moving by solar energy .", "They are moved by solar energy .", "They are moved by solar energy .", "They are propelled by solar energy ." ] } ``` ### Data Fields - sentence: original sentence written by an English learner - corrections: corrected versions by human annotators. The order of the annotations are consistent (eg first sentence will always be written by annotator "ref0"). ### Data Splits - This dataset contains 1511 examples in total and comprise a dev and test split. - There are 754 and 747 source sentences for dev and test, respectively. - Each sentence has 4 corresponding corrected versions. ## 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 This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information This benchmark was proposed by [Napoles et al., 2020](https://arxiv.org/abs/1702.04066). ``` @InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort, author = {Napoles, Courtney and Sakaguchi, Keisuke and Tetreault, Joel}, title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction}, booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers}, month = {April}, year = {2017}, address = {Valencia, Spain}, publisher = {Association for Computational Linguistics}, pages = {229--234}, url = {http://www.aclweb.org/anthology/E17-2037} } @InProceedings{heilman-EtAl:2014:P14-2, author = {Heilman, Michael and Cahill, Aoife and Madnani, Nitin and Lopez, Melissa and Mulholland, Matthew and Tetreault, Joel}, title = {Predicting Grammaticality on an Ordinal Scale}, booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, month = {June}, year = {2014}, address = {Baltimore, Maryland}, publisher = {Association for Computational Linguistics}, pages = {174--180}, url = {http://www.aclweb.org/anthology/P14-2029} } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.

jhu-clsp/jfleg

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual", "other-language-learner"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|other-GUG-grammaticality-judgements"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "jfleg", "pretty_name": "JHU FLuency-Extended GUG corpus", "tags": ["grammatical-error-correction"], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "corrections", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 379979, "num_examples": 755}, {"name": "test", "num_bytes": 379699, "num_examples": 748}], "download_size": 289093, "dataset_size": 759678}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

2024-02-06T07:47:11+00:00

TAGS #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #multilinguality-other-language-learner #size_categories-1K<n<10K #source_datasets-extended|other-GUG-grammaticality-judgements #language-English #license-cc-by-nc-sa-4.0 #grammatical-error-correction #arxiv-1702.04066 #region-us

# Dataset Card for JFLEG ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: Napoles et al., 2020 - Leaderboard: Leaderboard - Point of Contact: Courtney Napoles, Keisuke Sakaguchi ### Dataset Summary JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC systems with respect to fluency (extent to which a text is native-sounding) as well as grammaticality. For each source document, there are four human-written corrections. ### Supported Tasks and Leaderboards Grammatical error correction. ### Languages English (native as well as L2 writers) ## Dataset Structure ### Data Instances Each instance contains a source sentence and four corrections. For example: ### Data Fields - sentence: original sentence written by an English learner - corrections: corrected versions by human annotators. The order of the annotations are consistent (eg first sentence will always be written by annotator "ref0"). ### Data Splits - This dataset contains 1511 examples in total and comprise a dev and test split. - There are 754 and 747 source sentences for dev and test, respectively. - Each sentence has 4 corresponding corrected versions. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. This benchmark was proposed by Napoles et al., 2020. ### Contributions Thanks to @j-chim for adding this dataset.

9fca5f507ac11049ff34bb13c8546b01afcedbbd

# 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:** [Jigsaw Comment Toxicity Classification Kaggle Competition](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. ### Supported Tasks and Leaderboards The dataset support multi-label classification ### Languages The comments are in English ## Dataset Structure ### Data Instances A data point consists of a comment followed by multiple labels that can be associated with it. {'id': '02141412314', 'comment_text': 'Sample comment text', 'toxic': 0, 'severe_toxic': 0, 'obscene': 0, 'threat': 0, 'insult': 0, 'identity_hate': 1, } ### Data Fields - `id`: id of the comment - `comment_text`: the text of the comment - `toxic`: value of 0(non-toxic) or 1(toxic) classifying the comment - `severe_toxic`: value of 0(non-severe_toxic) or 1(severe_toxic) classifying the comment - `obscene`: value of 0(non-obscene) or 1(obscene) classifying the comment - `threat`: value of 0(non-threat) or 1(threat) classifying the comment - `insult`: value of 0(non-insult) or 1(insult) classifying the comment - `identity_hate`: value of 0(non-identity_hate) or 1(identity_hate) classifying the comment ### Data Splits The data is split into a training and testing set. ## Dataset Creation ### Curation Rationale The dataset was created to help in efforts to identify and curb instances of toxicity online. ### Source Data #### Initial Data Collection and Normalization The dataset is a collection of Wikipedia comments. #### 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 If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The "Toxic Comment Classification" dataset is released under [CC0], with the underlying comment text being governed by Wikipedia\'s [CC-SA-3.0]. ### Citation Information No citation information. ### Contributions Thanks to [@Tigrex161](https://github.com/Tigrex161) for adding this dataset.

google/jigsaw_toxicity_pred

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["other"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "pretty_name": "JigsawToxicityPred", "dataset_info": {"features": [{"name": "comment_text", "dtype": "string"}, {"name": "toxic", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}, {"name": "severe_toxic", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}, {"name": "obscene", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}, {"name": "threat", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}, {"name": "insult", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}, {"name": "identity_hate", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 71282358, "num_examples": 159571}, {"name": "test", "num_bytes": 28241991, "num_examples": 63978}], "download_size": 0, "dataset_size": 99524349}, "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "binary_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"comment_text": "text", "toxic": "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"}}]}]}

2024-01-18T11:06:53+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-crowdsourced #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc0-1.0 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Jigsaw Comment Toxicity Classification Kaggle Competition - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. ### Supported Tasks and Leaderboards The dataset support multi-label classification ### Languages The comments are in English ## Dataset Structure ### Data Instances A data point consists of a comment followed by multiple labels that can be associated with it. {'id': '02141412314', 'comment_text': 'Sample comment text', 'toxic': 0, 'severe_toxic': 0, 'obscene': 0, 'threat': 0, 'insult': 0, 'identity_hate': 1, } ### Data Fields - 'id': id of the comment - 'comment_text': the text of the comment - 'toxic': value of 0(non-toxic) or 1(toxic) classifying the comment - 'severe_toxic': value of 0(non-severe_toxic) or 1(severe_toxic) classifying the comment - 'obscene': value of 0(non-obscene) or 1(obscene) classifying the comment - 'threat': value of 0(non-threat) or 1(threat) classifying the comment - 'insult': value of 0(non-insult) or 1(insult) classifying the comment - 'identity_hate': value of 0(non-identity_hate) or 1(identity_hate) classifying the comment ### Data Splits The data is split into a training and testing set. ## Dataset Creation ### Curation Rationale The dataset was created to help in efforts to identify and curb instances of toxicity online. ### Source Data #### Initial Data Collection and Normalization The dataset is a collection of Wikipedia comments. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups. ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The "Toxic Comment Classification" dataset is released under [CC0], with the underlying comment text being governed by Wikipedia\'s [CC-SA-3.0]. No citation information. ### Contributions Thanks to @Tigrex161 for adding this dataset.

d46022c9df7c8b74ba52876f314f81ef60fa1727

# Dataset Card for Jigsaw Unintended Bias in Toxicity Classification ## 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://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification - **Repository:** - **Paper:** - **Leaderboard:** https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard - **Point of Contact:** ### Dataset Summary The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition. Please see the original [data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data) description for more information. ### Supported Tasks and Leaderboards The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset can be used for multi-attribute prediction. See the original [leaderboard](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard) for reference. ### Languages English ## Dataset Structure ### Data Instances A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes. For instance, here's the first train example. ``` { "article_id": 2006, "asian": NaN, "atheist": NaN, "bisexual": NaN, "black": NaN, "buddhist": NaN, "christian": NaN, "comment_text": "This is so cool. It's like, 'would you want your mother to read this??' Really great idea, well done!", "created_date": "2015-09-29 10:50:41.987077+00", "disagree": 0, "female": NaN, "funny": 0, "heterosexual": NaN, "hindu": NaN, "homosexual_gay_or_lesbian": NaN, "identity_annotator_count": 0, "identity_attack": 0.0, "insult": 0.0, "intellectual_or_learning_disability": NaN, "jewish": NaN, "latino": NaN, "likes": 0, "male": NaN, "muslim": NaN, "obscene": 0.0, "other_disability": NaN, "other_gender": NaN, "other_race_or_ethnicity": NaN, "other_religion": NaN, "other_sexual_orientation": NaN, "parent_id": NaN, "physical_disability": NaN, "psychiatric_or_mental_illness": NaN, "publication_id": 2, "rating": 0, "sad": 0, "severe_toxicity": 0.0, "sexual_explicit": 0.0, "target": 0.0, "threat": 0.0, "toxicity_annotator_count": 4, "transgender": NaN, "white": NaN, "wow": 0 } ``` ### Data Fields - `id`: id of the comment - `target`: value between 0(non-toxic) and 1(toxic) classifying the comment - `comment_text`: the text of the comment - `severe_toxicity`: value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment - `obscene`: value between 0(non-obscene) and 1(obscene) classifying the comment - `identity_attack`: value between 0(non-identity_hate) or 1(identity_hate) classifying the comment - `insult`: value between 0(non-insult) or 1(insult) classifying the comment - `threat`: value between 0(non-threat) and 1(threat) classifying the comment - For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): - `male` - `female` - `transgender` - `other_gender` - `heterosexual` - `homosexual_gay_or_lesbian` - `bisexual` - `other_sexual_orientation` - `christian` - `jewish` - `muslim` - `hindu` - `buddhist` - `atheist` - `other_religion` - `black` - `white` - `asian` - `latino` - `other_race_or_ethnicity` - `physical_disability` - `intellectual_or_learning_disability` - `psychiatric_or_mental_illness` - `other_disability` - Other metadata related to the source of the comment, such as creation date, publication id, number of likes, number of annotators, etc: - `created_date` - `publication_id` - `parent_id` - `article_id` - `rating` - `funny` - `wow` - `sad` - `likes` - `disagree` - `sexual_explicit` - `identity_annotator_count` - `toxicity_annotator_count` ### Data Splits There are four splits: - train: The train dataset as released during the competition. Contains labels and identity information for a subset of rows. - test: The train dataset as released during the competition. Does not contain labels nor identity information. - test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. - test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. ## Dataset Creation ### Curation Rationale The dataset was created to help in efforts to identify and curb instances of toxicity online. ### 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 This dataset is released under CC0, as is the underlying comment text. ### Citation Information No citation is available for this dataset, though you may link to the [kaggle](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) competition ### Contributions Thanks to [@iwontbecreative](https://github.com/iwontbecreative) for adding this dataset.

google/jigsaw_unintended_bias

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["text-scoring"], "pretty_name": "Jigsaw Unintended Bias in Toxicity Classification", "tags": ["toxicity-prediction"], "dataset_info": {"features": [{"name": "target", "dtype": "float32"}, {"name": "comment_text", "dtype": "string"}, {"name": "severe_toxicity", "dtype": "float32"}, {"name": "obscene", "dtype": "float32"}, {"name": "identity_attack", "dtype": "float32"}, {"name": "insult", "dtype": "float32"}, {"name": "threat", "dtype": "float32"}, {"name": "asian", "dtype": "float32"}, {"name": "atheist", "dtype": "float32"}, {"name": "bisexual", "dtype": "float32"}, {"name": "black", "dtype": "float32"}, {"name": "buddhist", "dtype": "float32"}, {"name": "christian", "dtype": "float32"}, {"name": "female", "dtype": "float32"}, {"name": "heterosexual", "dtype": "float32"}, {"name": "hindu", "dtype": "float32"}, {"name": "homosexual_gay_or_lesbian", "dtype": "float32"}, {"name": "intellectual_or_learning_disability", "dtype": "float32"}, {"name": "jewish", "dtype": "float32"}, {"name": "latino", "dtype": "float32"}, {"name": "male", "dtype": "float32"}, {"name": "muslim", "dtype": "float32"}, {"name": "other_disability", "dtype": "float32"}, {"name": "other_gender", "dtype": "float32"}, {"name": "other_race_or_ethnicity", "dtype": "float32"}, {"name": "other_religion", "dtype": "float32"}, {"name": "other_sexual_orientation", "dtype": "float32"}, {"name": "physical_disability", "dtype": "float32"}, {"name": "psychiatric_or_mental_illness", "dtype": "float32"}, {"name": "transgender", "dtype": "float32"}, {"name": "white", "dtype": "float32"}, {"name": "created_date", "dtype": "string"}, {"name": "publication_id", "dtype": "int32"}, {"name": "parent_id", "dtype": "float32"}, {"name": "article_id", "dtype": "int32"}, {"name": "rating", "dtype": {"class_label": {"names": {"0": "rejected", "1": "approved"}}}}, {"name": "funny", "dtype": "int32"}, {"name": "wow", "dtype": "int32"}, {"name": "sad", "dtype": "int32"}, {"name": "likes", "dtype": "int32"}, {"name": "disagree", "dtype": "int32"}, {"name": "sexual_explicit", "dtype": "float32"}, {"name": "identity_annotator_count", "dtype": "int32"}, {"name": "toxicity_annotator_count", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 914264058, "num_examples": 1804874}, {"name": "test_private_leaderboard", "num_bytes": 49188921, "num_examples": 97320}, {"name": "test_public_leaderboard", "num_bytes": 49442360, "num_examples": 97320}], "download_size": 0, "dataset_size": 1012895339}}

2024-01-18T11:06:57+00:00

TAGS #task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc0-1.0 #toxicity-prediction #region-us

# Dataset Card for Jigsaw Unintended Bias in Toxicity Classification ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: URL - Point of Contact: ### Dataset Summary The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition. Please see the original data description for more information. ### Supported Tasks and Leaderboards The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset can be used for multi-attribute prediction. See the original leaderboard for reference. ### Languages English ## Dataset Structure ### Data Instances A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes. For instance, here's the first train example. ### Data Fields - 'id': id of the comment - 'target': value between 0(non-toxic) and 1(toxic) classifying the comment - 'comment_text': the text of the comment - 'severe_toxicity': value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment - 'obscene': value between 0(non-obscene) and 1(obscene) classifying the comment - 'identity_attack': value between 0(non-identity_hate) or 1(identity_hate) classifying the comment - 'insult': value between 0(non-insult) or 1(insult) classifying the comment - 'threat': value between 0(non-threat) and 1(threat) classifying the comment - For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): - 'male' - 'female' - 'transgender' - 'other_gender' - 'heterosexual' - 'homosexual_gay_or_lesbian' - 'bisexual' - 'other_sexual_orientation' - 'christian' - 'jewish' - 'muslim' - 'hindu' - 'buddhist' - 'atheist' - 'other_religion' - 'black' - 'white' - 'asian' - 'latino' - 'other_race_or_ethnicity' - 'physical_disability' - 'intellectual_or_learning_disability' - 'psychiatric_or_mental_illness' - 'other_disability' - Other metadata related to the source of the comment, such as creation date, publication id, number of likes, number of annotators, etc: - 'created_date' - 'publication_id' - 'parent_id' - 'article_id' - 'rating' - 'funny' - 'wow' - 'sad' - 'likes' - 'disagree' - 'sexual_explicit' - 'identity_annotator_count' - 'toxicity_annotator_count' ### Data Splits There are four splits: - train: The train dataset as released during the competition. Contains labels and identity information for a subset of rows. - test: The train dataset as released during the competition. Does not contain labels nor identity information. - test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. - test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. ## Dataset Creation ### Curation Rationale The dataset was created to help in efforts to identify and curb instances of toxicity online. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information This dataset is released under CC0, as is the underlying comment text. No citation is available for this dataset, though you may link to the kaggle competition ### Contributions Thanks to @iwontbecreative for adding this dataset.

282eba950020b54bfc7c29b70aed6431a0723217

# Dataset Card for JNLPBA ## 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.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004 - **Repository:** [Needs More Information] - **Paper:** https://www.aclweb.org/anthology/W04-1213.pdf - **Leaderboard:** https://paperswithcode.com/sota/named-entity-recognition-ner-on-jnlpba?p=biobert-a-pre-trained-biomedical-language - **Point of Contact:** [Needs More Information] ### Dataset Summary The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. ### Supported Tasks and Leaderboards NER ### Languages English ## Dataset Structure ### Data Instances { 'id': '1', 'tokens': ['IL-2', 'gene', 'expression', 'and', 'NF-kappa', 'B', 'activation', 'through', 'CD28', 'requires', 'reactive', 'oxygen', 'production', 'by', '5-lipoxygenase', '.'], 'ner_tags': [1, 2, 0, 0, 9, 10, 0, 0, 9, 0, 0, 0, 0, 0, 9, 0], } ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no bio-entity mentioned, `1` signals the first token of a bio-entity and `2` the subsequent bio-entity tokens. ### Data Splits Train samples: 37094 Validation samples: 7714 ## 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 @inproceedings{collier-kim-2004-introduction, title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}", author = "Collier, Nigel and Kim, Jin-Dong", booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})", month = aug # " 28th and 29th", year = "2004", address = "Geneva, Switzerland", publisher = "COLING", url = "https://aclanthology.org/W04-1213", pages = "73--78", } ### Contributions Thanks to [@edugp](https://github.com/edugp) for adding this dataset.

jnlpba

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-genia-v3.02"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "BioNLP / JNLPBA Shared Task 2004", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-DNA", "2": "I-DNA", "3": "B-RNA", "4": "I-RNA", "5": "B-cell_line", "6": "I-cell_line", "7": "B-cell_type", "8": "I-cell_type", "9": "B-protein", "10": "I-protein"}}}}], "config_name": "jnlpba", "splits": [{"name": "train", "num_bytes": 8775707, "num_examples": 18546}, {"name": "validation", "num_bytes": 1801565, "num_examples": 3856}], "download_size": 3171072, "dataset_size": 10577272}}

2024-01-18T11:07:08+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-genia-v3.02 #language-English #license-unknown #region-us

# Dataset Card for JNLPBA ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: URL - Leaderboard: URL - Point of Contact: ### Dataset Summary The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. ### Supported Tasks and Leaderboards NER ### Languages English ## Dataset Structure ### Data Instances { 'id': '1', 'tokens': ['IL-2', 'gene', 'expression', 'and', 'NF-kappa', 'B', 'activation', 'through', 'CD28', 'requires', 'reactive', 'oxygen', 'production', 'by', '5-lipoxygenase', '.'], 'ner_tags': [1, 2, 0, 0, 9, 10, 0, 0, 9, 0, 0, 0, 0, 0, 9, 0], } ### Data Fields - 'id': Sentence identifier. - 'tokens': Array of tokens composing a sentence. - 'ner_tags': Array of tags, where '0' indicates no bio-entity mentioned, '1' signals the first token of a bio-entity and '2' the subsequent bio-entity tokens. ### Data Splits Train samples: 37094 Validation samples: 7714 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information @inproceedings{collier-kim-2004-introduction, title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}", author = "Collier, Nigel and Kim, Jin-Dong", booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})", month = aug # " 28th and 29th", year = "2004", address = "Geneva, Switzerland", publisher = "COLING", url = "URL pages = "73--78", } ### Contributions Thanks to @edugp for adding this dataset.

4d764b3a538c9722b7130fe8fe09e9274cbaafe1

# Dataset Card for journalists_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:** http://qufaculty.qu.edu.qa/telsayed/datasets/ - **Repository:** [Needs More Information] - **Paper:** https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/download/13221/12856 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Maram Hasanain] [email protected] ### Dataset Summary The journalists_questions dataset supports question identification over Arabic tweets of journalists. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Arabic ## Dataset Structure ### Data Instances Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not. A question tweet is a tweet that has at least one interrogative question. Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label. Below is an example: { 'tweet_id': '493235142128074753', 'label': 'yes', 'label_confidence':0.6359 } ### Data Fields tweet_id: the Twitter assigned ID for the tweet object. label: annotation of the tweet by whether it is a question or not label_confidence: confidence score for the label given annotations of multiple annotators per tweet ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter. ### Source Data #### Initial Data Collection and Normalization To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists. #### 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 [@MaramHasanain](https://github.com/MaramHasanain) for adding this dataset.

journalists_questions

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["other"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "JournalistsQuestions", "tags": ["question-identification"], "dataset_info": {"features": [{"name": "tweet_id", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "no", "1": "yes"}}}}, {"name": "label_confidence", "dtype": "float32"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 342296, "num_examples": 10077}], "download_size": 271039, "dataset_size": 342296}}

2024-01-18T11:07:09+00:00

TAGS #task_categories-text-classification #annotations_creators-crowdsourced #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Arabic #license-unknown #question-identification #region-us

# Dataset Card for journalists_questions ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: URL - Leaderboard: - Point of Contact: [Maram Hasanain] maram.hasanain@URL ### Dataset Summary The journalists_questions dataset supports question identification over Arabic tweets of journalists. ### Supported Tasks and Leaderboards ### Languages Arabic ## Dataset Structure ### Data Instances Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not. A question tweet is a tweet that has at least one interrogative question. Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label. Below is an example: { 'tweet_id': '493235142128074753', 'label': 'yes', 'label_confidence':0.6359 } ### Data Fields tweet_id: the Twitter assigned ID for the tweet object. label: annotation of the tweet by whether it is a question or not label_confidence: confidence score for the label given annotations of multiple annotators per tweet ### Data Splits ## Dataset Creation ### Curation Rationale The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter. ### Source Data #### Initial Data Collection and Normalization To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @MaramHasanain for adding this dataset.

a65c03cc5a31c38c5a992f2a809b3f30234b7b29

# Dataset Card for KanHope ## 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:** https://zenodo.org/record/4904729 - **Repository:** [KanHope](https://github.com/adeepH/KanHope) - **Paper:** [Hope speech detection in Under-resourced Kannada langauge](https://arxiv.org/abs/2108.04616) - **Leaderboard:** [N/A] - **Point of Contact:** [Adeep Hande]([email protected]) ### Dataset Summary KanHope dataset is a code-mixed Kannada-English dataset for hope speech detection. All texts are scraped from the comments section of YouTube. The dataset consists of 6,176 user-generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope speech or Not-hope speech. ### Supported Tasks and Leaderboards This task aims to detect Hope speech content of the code-mixed dataset of comments/posts in Dravidian Languages ( Kannada-English) collected from social media. The comment/post may contain more than one sentence, but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Languages Code-mixed text in Dravidian languages (Kannada-English). ## Dataset Structure ### Data Instances An example from the Kannada dataset looks as follows: | text | label | | :------ | :----- | | ��������� ��ͭ� heartly heltidini... plz avrigella namma nimmellara supprt beku | 0 (Non_hope speech) | | Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. | 1 (Hope Speech) | ### Data Fields Kannada - `text`: Kannada-English code mixed comment. - `label`: integer from either of 0 or 1 that corresponds to these values: "Non_hope Speech", "Hope Speech" ### Data Splits | | train | validation | test | |---------|------:|-----------:|-----:| | Kannada | 4941 | 618 | 617 | ## Dataset Creation ### Curation Rationale Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums. ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? Youtube users ### 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 ``` @misc{hande2021hope, title={Hope Speech detection in under-resourced Kannada language}, author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi}, year={2021}, eprint={2108.04616}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@adeepH](https://github.com/adeepH) for adding this dataset.

kan_hope

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en", "kn"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "pretty_name": "KanHope", "language_bcp47": ["en-IN", "kn-IN"], "tags": ["hope-speech-detection"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Not-Hope", "1": "Hope"}}}}], "splits": [{"name": "train", "num_bytes": 494898, "num_examples": 4940}, {"name": "test", "num_bytes": 65722, "num_examples": 618}], "download_size": 568972, "dataset_size": 560620}}

2024-01-18T11:07:10+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-English #language-Kannada #license-cc-by-4.0 #hope-speech-detection #arxiv-2108.04616 #region-us

Dataset Card for KanHope ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information Dataset Description ------------------- * Homepage: URL * Repository: KanHope * Paper: Hope speech detection in Under-resourced Kannada langauge * Leaderboard: [N/A] * Point of Contact: Adeep Hande ### Dataset Summary KanHope dataset is a code-mixed Kannada-English dataset for hope speech detection. All texts are scraped from the comments section of YouTube. The dataset consists of 6,176 user-generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope speech or Not-hope speech. ### Supported Tasks and Leaderboards This task aims to detect Hope speech content of the code-mixed dataset of comments/posts in Dravidian Languages ( Kannada-English) collected from social media. The comment/post may contain more than one sentence, but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Languages Code-mixed text in Dravidian languages (Kannada-English). Dataset Structure ----------------- ### Data Instances An example from the Kannada dataset looks as follows: ### Data Fields Kannada * 'text': Kannada-English code mixed comment. * 'label': integer from either of 0 or 1 that corresponds to these values: "Non\_hope Speech", "Hope Speech" ### Data Splits Dataset Creation ---------------- ### Curation Rationale Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? Youtube users ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @adeepH for adding this dataset.

e690c78d74b56f26d3309acb89f496e573a42349

# Dataset Card for kannada_news 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 link](https://www.kaggle.com/disisbig/kannada-news-dataset) for kannada news headlines dataset - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** More information about the dataset and the models can be found [here](https://github.com/goru001/nlp-for-kannada) ### Dataset Summary The Kannada news dataset contains only the headlines of news article in three categories: Entertainment, Tech, and Sports. The data set contains around 6300 news article headlines which are collected from Kannada news websites. The data set has been cleaned and contains train and test set using which can be used to benchmark topic classification models in Kannada. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Kannada (kn) ## Dataset Structure ### Data Instances The data has two files. A train.csv and valid.csv. An example row of the dataset is as below: ``` { 'headline': 'ಫಿಫಾ ವಿಶ್ವಕಪ್ ಫೈನಲ್: ಅತಿರೇಕಕ್ಕೇರಿದ ಸಂಭ್ರಮಾಚರಣೆ; ಅಭಿಮಾನಿಗಳ ಹುಚ್ಚು ವರ್ತನೆಗೆ ವ್ಯಾಪಕ ಖಂಡನೆ', 'label':'sports' } ``` NOTE: The data has very few examples on the technology (class label: 'tech') topic. [More Information Needed] ### Data Fields Data has two fields: - headline: text headline in kannada (string) - label : corresponding class label which the headlines pertains to in english (string) ### Data Splits The dataset is divided into two splits. All the headlines are scraped from news websites on the internet. | | train | validation | |-----------------|--------:|-----------:| | Input Sentences | 5167 | 1293 | ## 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 There are starkingly less amount of data for South Indian languages, especially Kannada, available in digital format which can be used for NLP purposes. Though having roughly 38 million native speakers, it is a little under-represented language and will benefit from active contribution from the community. This dataset, however, can just help people get exposed to Kannada and help proceed further active participation for enabling continuous progress and development. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Gaurav Arora] (https://github.com/goru001/nlp-for-kannada). Has also got some starter models an embeddings to help get started. ### Licensing Information cc-by-sa-4.0 ### Citation Information https://www.kaggle.com/disisbig/kannada-news-dataset ### Contributions Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset.

kannada_news

2022-03-02T23:29:22+00:00

{"annotations_creators": ["other"], "language_creators": ["other"], "language": ["kn"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "pretty_name": "KannadaNews Dataset", "dataset_info": {"features": [{"name": "headline", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sports", "1": "tech", "2": "entertainment"}}}}], "splits": [{"name": "train", "num_bytes": 969216, "num_examples": 5167}, {"name": "validation", "num_bytes": 236817, "num_examples": 1293}], "download_size": 0, "dataset_size": 1206033}}

2024-01-18T11:07:12+00:00

TAGS #task_categories-text-classification #task_ids-topic-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Kannada #license-cc-by-sa-4.0 #region-us

Dataset Card for kannada\_news dataset ====================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: Kaggle link for kannada news headlines dataset * Repository: * Paper: * Leaderboard: * Point of Contact: More information about the dataset and the models can be found here ### Dataset Summary The Kannada news dataset contains only the headlines of news article in three categories: Entertainment, Tech, and Sports. The data set contains around 6300 news article headlines which are collected from Kannada news websites. The data set has been cleaned and contains train and test set using which can be used to benchmark topic classification models in Kannada. ### Supported Tasks and Leaderboards ### Languages Kannada (kn) Dataset Structure ----------------- ### Data Instances The data has two files. A URL and URL. An example row of the dataset is as below: NOTE: The data has very few examples on the technology (class label: 'tech') topic. ### Data Fields Data has two fields: * headline: text headline in kannada (string) * label : corresponding class label which the headlines pertains to in english (string) ### Data Splits The dataset is divided into two splits. All the headlines are scraped from news websites on the internet. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset There are starkingly less amount of data for South Indian languages, especially Kannada, available in digital format which can be used for NLP purposes. Though having roughly 38 million native speakers, it is a little under-represented language and will benefit from active contribution from the community. This dataset, however, can just help people get exposed to Kannada and help proceed further active participation for enabling continuous progress and development. ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators [Gaurav Arora] (URL Has also got some starter models an embeddings to help get started. ### Licensing Information cc-by-sa-4.0 URL ### Contributions Thanks to @vrindaprabhu for adding this dataset.

899d114e2c206f04ca787acf860177201103237e

# Dataset Card for KdConv ## 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/thu-coai/KdConv) - **Paper:** [{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://www.aclweb.org/anthology/2020.acl-main.635.pdf) ### Dataset Summary KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer learning and domain adaptation. ### Supported Tasks and Leaderboards This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup. ### Languages This dataset has only Chinese Language. ## Dataset Structure ### Data Instances Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking , e.g.: ``` { "messages": [ { "message": "对《我喜欢上你时的内心活动》这首歌有了解吗？" }, { "attrs": [ { "attrname": "Information", "attrvalue": "《我喜欢上你时的内心活动》是由韩寒填词，陈光荣作曲，陈绮贞演唱的歌曲，作为电影《喜欢你》的主题曲于2017年4月10日首发。2018年，该曲先后提名第37届香港电影金像奖最佳原创电影歌曲奖、第7届阿比鹿音乐奖流行单曲奖。", "name": "我喜欢上你时的内心活动" } ], "message": "有些了解，是电影《喜欢你》的主题曲。" }, ... { "attrs": [ { "attrname": "代表作品", "attrvalue": "旅行的意义", "name": "陈绮贞" }, { "attrname": "代表作品", "attrvalue": "时间的歌", "name": "陈绮贞" } ], "message": "我还知道《旅行的意义》与《时间的歌》，都算是她的代表作。" }, { "message": "好，有时间我找出来听听。" } ], "name": "我喜欢上你时的内心活动" } ``` The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity , relationship, tail entity), e.g.: ``` "忽然之间": [ [ "忽然之间", "Information", "《忽然之间》是歌手 莫文蔚演唱的歌曲，由 周耀辉， 李卓雄填词， 林健华谱曲，收录在莫文蔚1999年发行专辑《 就是莫文蔚》里。" ], [ "忽然之间", "谱曲", "林健华" ] ... ] ``` ### Data Fields Conversation data fields: - `name`: the starting topic (entity) of the conversation - `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}` - `messages`: list of all the turns in the dialogue. For each turn: - `message`: the utterance - `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet: - `name`: the head entity - `attrname`: the relation - `attrvalue`: the tail entity Knowledge Base data fields: - `head_entity`: the head entity - `kb_triplets`: list of corresponding triplets - `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}` ### Data Splits The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes: | | train | validation | test | |--------|------:|-----------:|-----:| | travel | 1200 | 1200 | 1200 | | film | 1200 | 150 | 150 | | music | 1200 | 150 | 150 | | all | 3600 | 450 | 450 | The Knowledge base dataset is having only train split with following sizes: | | train | |--------|------:| | travel | 1154 | | film | 8090 | | music | 4441 | | all | 13685 | ## 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 Apache License 2.0 ### Citation Information ``` @inproceedings{zhou-etal-2020-kdconv, title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation", author = "Zhou, Hao and Zheng, Chujie and Huang, Kaili and Huang, Minlie and Zhu, Xiaoyan", 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.635", doi = "10.18653/v1/2020.acl-main.635", pages = "7098--7108", } ``` ### Contributions Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.

kd_conv

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "machine-generated"], "language_creators": ["crowdsourced"], "language": ["zh"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["dialogue-modeling"], "paperswithcode_id": "kdconv", "pretty_name": "Knowledge-driven Conversation", "dataset_info": [{"config_name": "travel_dialogues", "features": [{"name": "messages", "sequence": [{"name": "message", "dtype": "string"}, {"name": "attrs", "sequence": [{"name": "attrname", "dtype": "string"}, {"name": "attrvalue", "dtype": "string"}, {"name": "name", "dtype": "string"}]}]}, {"name": "name", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3241550, "num_examples": 1200}, {"name": "test", "num_bytes": 793883, "num_examples": 150}, {"name": "validation", "num_bytes": 617177, "num_examples": 150}], "download_size": 11037768, "dataset_size": 4652610}, {"config_name": "travel_knowledge_base", "features": [{"name": "head_entity", "dtype": "string"}, {"name": "kb_triplets", "sequence": {"sequence": "string"}}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1517024, "num_examples": 1154}], "download_size": 11037768, "dataset_size": 1517024}, {"config_name": "music_dialogues", "features": [{"name": "messages", "sequence": [{"name": "message", "dtype": "string"}, {"name": "attrs", "sequence": [{"name": "attrname", "dtype": "string"}, {"name": "attrvalue", "dtype": "string"}, {"name": "name", "dtype": "string"}]}]}, {"name": "name", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3006192, "num_examples": 1200}, {"name": "test", "num_bytes": 801012, "num_examples": 150}, {"name": "validation", "num_bytes": 633905, "num_examples": 150}], "download_size": 11037768, "dataset_size": 4441109}, {"config_name": "music_knowledge_base", "features": [{"name": "head_entity", "dtype": "string"}, {"name": "kb_triplets", "sequence": {"sequence": "string"}}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5980643, "num_examples": 4441}], "download_size": 11037768, "dataset_size": 5980643}, {"config_name": "film_dialogues", "features": [{"name": "messages", "sequence": [{"name": "message", "dtype": "string"}, {"name": "attrs", "sequence": [{"name": "attrname", "dtype": "string"}, {"name": "attrvalue", "dtype": "string"}, {"name": "name", "dtype": "string"}]}]}, {"name": "name", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4867659, "num_examples": 1200}, {"name": "test", "num_bytes": 956995, "num_examples": 150}, {"name": "validation", "num_bytes": 884232, "num_examples": 150}], "download_size": 11037768, "dataset_size": 6708886}, {"config_name": "film_knowledge_base", "features": [{"name": "head_entity", "dtype": "string"}, {"name": "kb_triplets", "sequence": {"sequence": "string"}}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10500882, "num_examples": 8090}], "download_size": 11037768, "dataset_size": 10500882}, {"config_name": "all_dialogues", "features": [{"name": "messages", "sequence": [{"name": "message", "dtype": "string"}, {"name": "attrs", "sequence": [{"name": "attrname", "dtype": "string"}, {"name": "attrvalue", "dtype": "string"}, {"name": "name", "dtype": "string"}]}]}, {"name": "name", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11115313, "num_examples": 3600}, {"name": "test", "num_bytes": 2551802, "num_examples": 450}, {"name": "validation", "num_bytes": 2135226, "num_examples": 450}], "download_size": 11037768, "dataset_size": 15802341}, {"config_name": "all_knowledge_base", "features": [{"name": "head_entity", "dtype": "string"}, {"name": "kb_triplets", "sequence": {"sequence": "string"}}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17998529, "num_examples": 13685}], "download_size": 11037768, "dataset_size": 17998529}]}

2024-01-18T11:07:15+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Chinese #license-apache-2.0 #region-us

Dataset Card for KdConv ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: Github * Paper: {K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation ### Dataset Summary KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer learning and domain adaptation. ### Supported Tasks and Leaderboards This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup. ### Languages This dataset has only Chinese Language. Dataset Structure ----------------- ### Data Instances Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking , e.g.: The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity , relationship, tail entity), e.g.: ### Data Fields Conversation data fields: * 'name': the starting topic (entity) of the conversation * 'domain': the domain this sample belongs to. Categorical value among '{travel, film, music}' * 'messages': list of all the turns in the dialogue. For each turn: + 'message': the utterance + 'attrs': list of knowledge graph triplets referred by the utterance. For each triplet: - 'name': the head entity - 'attrname': the relation - 'attrvalue': the tail entity Knowledge Base data fields: * 'head\_entity': the head entity * 'kb\_triplets': list of corresponding triplets * 'domain': the domain this sample belongs to. Categorical value among '{travel, film, music}' ### Data Splits The conversation dataset is split into a 'train', 'validation', and 'test' split with the following sizes: The Knowledge base dataset is having only train split with following sizes: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Apache License 2.0 ### Contributions Thanks to @pacman100 for adding this dataset.

4b3b17204b61840c4b931fe3a3cb98cbe21c468e

# Dataset Card for KDE4 ## 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/KDE4.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **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/KDE4.php E.g. `dataset = load_dataset("kde4", lang1="en", lang2="nl")` ### 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.

kde4

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["af", "ar", "as", "ast", "be", "bg", "bn", "br", "ca", "crh", "cs", "csb", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gu", "ha", "he", "hi", "hne", "hr", "hsb", "hu", "hy", "id", "is", "it", "ja", "ka", "kk", "km", "kn", "ko", "ku", "lb", "lt", "lv", "mai", "mk", "ml", "mr", "ms", "mt", "nb", "nds", "ne", "nl", "nn", "nso", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "rw", "se", "si", "sk", "sl", "sr", "sv", "ta", "te", "tg", "th", "tr", "uk", "uz", "vi", "wa", "xh", "zh"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "KDE4", "language_bcp47": ["bn-IN", "en-GB", "pt-BR", "zh-CN", "zh-HK", "zh-TW"], "dataset_info": [{"config_name": "fi-nl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["fi", "nl"]}}}], "splits": [{"name": "train", "num_bytes": 8845933, "num_examples": 101593}], "download_size": 2471355, "dataset_size": 8845933}, {"config_name": "it-ro", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["it", "ro"]}}}], "splits": [{"name": "train", "num_bytes": 8827049, "num_examples": 109003}], "download_size": 2389051, "dataset_size": 8827049}, {"config_name": "nl-sv", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["nl", "sv"]}}}], "splits": [{"name": "train", "num_bytes": 22294586, "num_examples": 188454}], "download_size": 6203460, "dataset_size": 22294586}, {"config_name": "en-it", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "it"]}}}], "splits": [{"name": "train", "num_bytes": 27132585, "num_examples": 220566}], "download_size": 7622662, "dataset_size": 27132585}, {"config_name": "en-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 25650409, "num_examples": 210173}], "download_size": 7049364, "dataset_size": 25650409}]}

2024-01-18T11:07:20+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Afrikaans #language-Arabic #language-Assamese #language-Asturian #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Catalan #language-Crimean Tatar #language-Czech #language-Kashubian #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Western Frisian #language-Irish #language-Galician #language-Gujarati #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Upper Sorbian #language-Hungarian #language-Armenian #language-Indonesian #language-Icelandic #language-Italian #language-Japanese #language-Georgian #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Kurdish #language-Luxembourgish #language-Lithuanian #language-Latvian #language-Maithili #language-Macedonian #language-Malayalam #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Norwegian Bokmål #language-Low German #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Pedi #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Kinyarwanda #language-Northern Sami #language-Sinhala #language-Slovak #language-Slovenian #language-Serbian #language-Swedish #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkish #language-Ukrainian #language-Uzbek #language-Vietnamese #language-Walloon #language-Xhosa #language-Chinese #license-unknown #region-us

# Dataset Card for KDE4 ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### 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: URL E.g. 'dataset = load_dataset("kde4", lang1="en", lang2="nl")' ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

ac0b3ec25f0398732ada258d175b55a98583776b

# Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM) ## 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/KELM-corpus - **Repository:** https://github.com/google-research-datasets/KELM-corpus - **Paper:** https://arxiv.org/abs/2010.12688 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text. The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations. ### Supported Tasks and Leaderboards The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model with the tuples concatenated into a single sequence. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances Each instance consists of one KG triple paired with corresponding natural language. ### Data Fields - `triple`: Wikipedia triples of the form `<subject> <relation> <object>` where some subjects have multiple relations, e.g. `<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>`. For more details on how these relations are grouped, please refer to the paper. - `sentence`: The corresponding Wikipedia sentence. ### Data Splits The dataset includes a pre-determined train, validation, and test split. ## Dataset Creation ### Curation Rationale The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate natural text from a knowledge graph. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The data is sourced from English Wikipedia and it's associated knowledge graph. ### 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 From the paper: > Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still contain some of these biases, certain types of biases may be reduced. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This dataset has been released under the [CC BY-SA 2.0 license](https://creativecommons.org/licenses/by-sa/2.0/). ### Citation Information ``` @misc{agarwal2020large, title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training}, author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou}, year={2020}, eprint={2010.12688}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

kelm

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "paperswithcode_id": "kelm", "pretty_name": "Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)", "tags": ["data-to-text-generation"], "dataset_info": {"features": [{"name": "triple", "dtype": "string"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1343187306, "num_examples": 6371131}, {"name": "validation", "num_bytes": 167790917, "num_examples": 796471}, {"name": "test", "num_bytes": 167921750, "num_examples": 796493}], "download_size": 1631259869, "dataset_size": 1678899973}}

2024-01-18T11:07:22+00:00

TAGS #task_categories-other #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-sa-3.0 #data-to-text-generation #arxiv-2010.12688 #region-us

# Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM) ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text. The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations. ### Supported Tasks and Leaderboards The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model with the tuples concatenated into a single sequence. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances Each instance consists of one KG triple paired with corresponding natural language. ### Data Fields - 'triple': Wikipedia triples of the form '<subject> <relation> <object>' where some subjects have multiple relations, e.g. '<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>'. For more details on how these relations are grouped, please refer to the paper. - 'sentence': The corresponding Wikipedia sentence. ### Data Splits The dataset includes a pre-determined train, validation, and test split. ## Dataset Creation ### Curation Rationale The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate natural text from a knowledge graph. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? The data is sourced from English Wikipedia and it's associated knowledge graph. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases From the paper: > Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still contain some of these biases, certain types of biases may be reduced. ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information This dataset has been released under the CC BY-SA 2.0 license. ### Contributions Thanks to @joeddav for adding this dataset.

0f7881c6bf693742af91a31b4d8f827db2f41233

# Dataset Card for KILT ## 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://ai.facebook.com/tools/kilt/ - **Repository:** https://github.com/facebookresearch/KILT - **Paper:** https://arxiv.org/abs/2009.02252 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/689/leaderboard/ - **Point of Contact:** [Needs More Information] ### Dataset Summary KILT has been built from 11 datasets representing 5 types of tasks: - Fact-checking - Entity linking - Slot filling - Open domain QA - Dialog generation All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. #### Loading the KILT knowledge source and task data The original KILT [release](https://github.com/facebookresearch/KILT) only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: ```python from datasets import load_dataset # Get the pre-processed Wikipedia knowledge source for kild kilt_wiki = load_dataset("kilt_wikipedia") # Get the KILT task datasets kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only") # Most tasks in KILT already have all required data, but KILT-TriviaQA # only provides the question IDs, not the questions themselves. # Thankfully, we can get the original TriviaQA data with: trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # The KILT IDs can then be mapped to the TriviaQA questions with: triviaqa_map = {} def add_missing_data(x, trivia_qa_subset, triviaqa_map): i = triviaqa_map[x['id']] x['input'] = trivia_qa_subset[i]['question'] x['output']['original_answer'] = trivia_qa_subset[i]['answer']['value'] return x for k in ['train', 'validation', 'test']: triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])]) kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map) kilt_triviaqa[k] = kilt_triviaqa[k].map(add_missing_data, fn_kwargs=dict(trivia_qa_subset=trivia_qa[k], triviaqa_map=triviaqa_map)) ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure ### Data Instances An example of open-domain QA from the Natural Questions `nq` configuration looks as follows: ``` {'id': '-5004457603684974952', 'input': 'who is playing the halftime show at super bowl 2016', 'meta': {'left_context': '', 'mention': '', 'obj_surface': [], 'partial_evidence': [], 'right_context': '', 'sub_surface': [], 'subj_aliases': [], 'template_questions': []}, 'output': [{'answer': 'Coldplay', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 186, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 178, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Beyoncé', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 224, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 217, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Bruno Mars', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 239, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 229, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 0.9657992720603943, 'end_character': 341, 'end_paragraph_id': 1, 'meta': {'annotation_id': '2430977867500315580', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': 'NONE'}, 'section': 'Section::::Abstract.', 'start_character': 0, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': -1.0, 'end_character': -1, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who previously had headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively.', "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars", "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars,"], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': -1, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}]} ``` ### Data Fields Examples from all configurations have the following features: - `input`: a `string` feature representing the query. - `output`: a `list` of features each containing information for an answer, made up of: - `answer`: a `string` feature representing a possible answer. - `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by: - `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from. - `section`: a `string` feature, the title of the section in Wikipedia article. - `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article. - `start_character`: a `int32` feature. - `start_paragraph_id`: a `int32` feature. - `end_character`: a `int32` feature. - `end_paragraph_id`: a `int32` feature. ### Data Splits The configurations have the following splits: | | Train | Validation | Test | | ----------- | ----------- | ----------- | ----------- | | triviaqa | 61844 | 5359 | 6586 | | fever | 104966 | 10444 | 10100 | | aidayago2 | 18395 | 4784 | 4463 | | wned | | 3396 | 3376 | | cweb | | 5599 | 5543 | | trex | 2284168 | 5000 | 5000 | | structured_zeroshot | 147909 | 3724 | 4966 | | nq | 87372 | 2837 | 1444 | | hotpotqa | 88869 | 5600 | 5569 | | eli5 | 272634 | 1507 | 600 | | wow | 94577 | 3058 | 2944 | ## 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 Cite as: ``` @inproceedings{kilt_tasks, author = {Fabio Petroni and Aleksandra Piktus and Angela Fan and Patrick S. H. Lewis and Majid Yazdani and Nicola De Cao and James Thorne and Yacine Jernite and Vladimir Karpukhin and Jean Maillard and Vassilis Plachouras and Tim Rockt{\"{a}}schel and Sebastian Riedel}, editor = {Kristina Toutanova and Anna Rumshisky and Luke Zettlemoyer and Dilek Hakkani{-}T{\"{u}}r and Iz Beltagy and Steven Bethard and Ryan Cotterell and Tanmoy Chakraborty and Yichao Zhou}, title = {{KILT:} a Benchmark for Knowledge Intensive Language Tasks}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021, Online, June 6-11, 2021}, pages = {2523--2544}, publisher = {Association for Computational Linguistics}, year = {2021}, url = {https://www.aclweb.org/anthology/2021.naacl-main.200/} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.

kilt_tasks

2022-03-02T23:29:22+00:00

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"data_files": [{"split": "train", "path": "triviaqa_support_only/train-*"}, {"split": "validation", "path": "triviaqa_support_only/validation-*"}, {"split": "test", "path": "triviaqa_support_only/test-*"}]}, {"config_name": "wned", "data_files": [{"split": "validation", "path": "wned/validation-*"}, {"split": "test", "path": "wned/test-*"}]}, {"config_name": "wow", "data_files": [{"split": "train", "path": "wow/train-*"}, {"split": "validation", "path": "wow/validation-*"}, {"split": "test", "path": "wow/test-*"}]}]}

2024-01-04T14:01:11+00:00

TAGS #task_categories-fill-mask #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-text-retrieval #task_categories-text2text-generation #task_ids-abstractive-qa #task_ids-dialogue-modeling #task_ids-document-retrieval #task_ids-entity-linking-retrieval #task_ids-extractive-qa #task_ids-fact-checking #task_ids-fact-checking-retrieval #task_ids-open-domain-abstractive-qa #task_ids-open-domain-qa #task_ids-slot-filling #annotations_creators-crowdsourced #annotations_creators-found #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #source_datasets-extended|natural_questions #source_datasets-extended|other-aidayago #source_datasets-extended|other-fever #source_datasets-extended|other-hotpotqa #source_datasets-extended|other-trex #source_datasets-extended|other-triviaqa #source_datasets-extended|other-wizardsofwikipedia #source_datasets-extended|other-wned-cweb #source_datasets-extended|other-wned-wiki #source_datasets-extended|other-zero-shot-re #source_datasets-original #language-English #license-mit #arxiv-2009.02252 #region-us

Dataset Card for KILT ===================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: URL * Point of Contact: ### Dataset Summary KILT has been built from 11 datasets representing 5 types of tasks: * Fact-checking * Entity linking * Slot filling * Open domain QA * Dialog generation All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. #### Loading the KILT knowledge source and task data The original KILT release only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found here. ### Languages All tasks are in English ('en'). Dataset Structure ----------------- ### Data Instances An example of open-domain QA from the Natural Questions 'nq' configuration looks as follows: ### Data Fields Examples from all configurations have the following features: * 'input': a 'string' feature representing the query. * 'output': a 'list' of features each containing information for an answer, made up of: + 'answer': a 'string' feature representing a possible answer. + 'provenance': a 'list' of features representing Wikipedia passages that support the 'answer', denoted by: - 'title': a 'string' feature, the title of the Wikipedia article the passage was retrieved from. - 'section': a 'string' feature, the title of the section in Wikipedia article. - 'wikipedia\_id': a 'string' feature, a unique identifier for the Wikipedia article. - 'start\_character': a 'int32' feature. - 'start\_paragraph\_id': a 'int32' feature. - 'end\_character': a 'int32' feature. - 'end\_paragraph\_id': a 'int32' feature. ### Data Splits The configurations have the following splits: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Cite as: ### Contributions Thanks to @thomwolf, @yjernite for adding this dataset.

82ebb39ff7bc2c9fab21f31feebe4b9815be0102

# Dataset Card for "kilt_wikipedia" ## 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/facebookresearch/KILT](https://github.com/facebookresearch/KILT) - **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:** 37.32 GB - **Size of the generated dataset:** 29.37 GB - **Total amount of disk used:** 66.69 GB ### Dataset Summary KILT-Wikipedia: Wikipedia pre-processed for KILT. ### 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 #### 2019-08-01 - **Size of downloaded dataset files:** 37.32 GB - **Size of the generated dataset:** 29.37 GB - **Total amount of disk used:** 66.69 GB An example of 'full' looks as follows. ``` { "anchors": { "end": [], "href": [], "paragraph_id": [], "start": [], "text": [], "wikipedia_id": [], "wikipedia_title": [] }, "categories": "", "history": { "pageid": 0, "parentid": 0, "pre_dump": true, "revid": 0, "timestamp": "", "url": "" }, "kilt_id": "", "text": { "paragraph": [] }, "wikidata_info": { "aliases": { "alias": [] }, "description": "", "enwikiquote_title": "", "wikidata_id": "", "wikidata_label": "", "wikipedia_title": "" }, "wikipedia_id": "", "wikipedia_title": "" } ``` ### Data Fields The data fields are the same among all splits. #### 2019-08-01 - `kilt_id`: a `string` feature. - `wikipedia_id`: a `string` feature. - `wikipedia_title`: a `string` feature. - `text`: a dictionary feature containing: - `paragraph`: a `string` feature. - `anchors`: a dictionary feature containing: - `paragraph_id`: a `int32` feature. - `start`: a `int32` feature. - `end`: a `int32` feature. - `text`: a `string` feature. - `href`: a `string` feature. - `wikipedia_title`: a `string` feature. - `wikipedia_id`: a `string` feature. - `categories`: a `string` feature. - `description`: a `string` feature. - `enwikiquote_title`: a `string` feature. - `wikidata_id`: a `string` feature. - `wikidata_label`: a `string` feature. - `wikipedia_title`: a `string` feature. - `aliases`: a dictionary feature containing: - `alias`: a `string` feature. - `pageid`: a `int32` feature. - `parentid`: a `int32` feature. - `revid`: a `int32` feature. - `pre_dump`: a `bool` feature. - `timestamp`: a `string` feature. - `url`: a `string` feature. ### Data Splits | name | full | |----------|------:| |2019-08-01|5903530| ## 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{fb_kilt, author = {Fabio Petroni and Aleksandra Piktus and Angela Fan and Patrick Lewis and Majid Yazdani and Nicola De Cao and James Thorne and Yacine Jernite and Vassilis Plachouras and Tim Rockt"aschel and Sebastian Riedel}, title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks}, journal = {CoRR}, archivePrefix = {arXiv}, year = {2020}, ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.

kilt_wikipedia

2022-03-02T23:29:22+00:00

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2024-01-18T11:07:33+00:00

TAGS #region-us

Dataset Card for "kilt\_wikipedia" ================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 37.32 GB * Size of the generated dataset: 29.37 GB * Total amount of disk used: 66.69 GB ### Dataset Summary KILT-Wikipedia: Wikipedia pre-processed for KILT. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### 2019-08-01 * Size of downloaded dataset files: 37.32 GB * Size of the generated dataset: 29.37 GB * Total amount of disk used: 66.69 GB An example of 'full' looks as follows. ### Data Fields The data fields are the same among all splits. #### 2019-08-01 * 'kilt\_id': a 'string' feature. * 'wikipedia\_id': a 'string' feature. * 'wikipedia\_title': a 'string' feature. * 'text': a dictionary feature containing: + 'paragraph': a 'string' feature. * 'anchors': a dictionary feature containing: + 'paragraph\_id': a 'int32' feature. + 'start': a 'int32' feature. + 'end': a 'int32' feature. + 'text': a 'string' feature. + 'href': a 'string' feature. + 'wikipedia\_title': a 'string' feature. + 'wikipedia\_id': a 'string' feature. * 'categories': a 'string' feature. * 'description': a 'string' feature. * 'enwikiquote\_title': a 'string' feature. * 'wikidata\_id': a 'string' feature. * 'wikidata\_label': a 'string' feature. * 'wikipedia\_title': a 'string' feature. * 'aliases': a dictionary feature containing: + 'alias': a 'string' feature. * 'pageid': a 'int32' feature. * 'parentid': a 'int32' feature. * 'revid': a 'int32' feature. * 'pre\_dump': a 'bool' feature. * 'timestamp': a 'string' feature. * 'url': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @yjernite for adding this dataset.

97dce354a363f853fa94c439dd42135fc31618f0

# Dataset Card for kinnews_kirnews ## 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:** [More Information Needed] - **Repository:** https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus - **Paper:** [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](https://arxiv.org/abs/2010.12174) - **Leaderboard:** NA - **Point of Contact:** [Rubungo Andre Niyongabo1](mailto:[email protected]) ### Dataset Summary Kinyarwanda and Kirundi news classification datasets (KINNEWS and KIRNEWS,respectively), which were both collected from Rwanda and Burundi news websites and newspapers, for low-resource monolingual and cross-lingual multiclass classification tasks. ### Supported Tasks and Leaderboards This dataset can be used for text classification of news articles in Kinyarwadi and Kirundi languages. Each news article can be classified into one of the 14 possible classes. The classes are: - politics - sport - economy - health - entertainment - history - technology - culture - religion - environment - education - relationship ### Languages Kinyarwanda and Kirundi ## Dataset Structure ### Data Instances Here is an example from the dataset: | Field | Value | | ----- | ----------- | | label | 1 | | kin_label/kir_label | 'inkino' | | url | 'https://nawe.bi/Primus-Ligue-Imirwi-igiye-guhura-gute-ku-ndwi-ya-6-y-ihiganwa.html' | | title | 'Primus Ligue\xa0: Imirwi igiye guhura gute ku ndwi ya 6 y’ihiganwa\xa0?'| | content | ' Inkino zitegekanijwe kuruno wa gatandatu igenekerezo rya 14 Nyakanga umwaka wa 2019...'| | en_label| 'sport'| ### Data Fields The raw version of the data for Kinyarwanda language consists of these fields - label: The category of the news article - kin_label/kir_label: The associated label in Kinyarwanda/Kirundi language - en_label: The associated label in English - url: The URL of the news article - title: The title of the news article - content: The content of the news article The cleaned version contains only the `label`, `title` and the `content` fields ### Data Splits Lang| Train | Test | |---| ----- | ---- | |Kinyarwandai Raw|17014|4254| |Kinyarwandai Clean|17014|4254| |Kirundi Raw|3689|923| |Kirundi Clean|3689|923| ## 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{niyongabo2020kinnews, title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi}, author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li}, journal={arXiv preprint arXiv:2010.12174}, year={2020} } ``` ### Contributions Thanks to [@saradhix](https://github.com/saradhix) for adding this dataset.

kinnews_kirnews

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["rn", "rw"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K", "1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "topic-classification"], "paperswithcode_id": "kinnews-and-kirnews", "pretty_name": "KinnewsKirnews", "config_names": ["kinnews_cleaned", "kinnews_raw", "kirnews_cleaned", "kirnews_raw"], "dataset_info": [{"config_name": "kinnews_raw", "features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "politics", "1": "sport", "2": "economy", "3": "health", "4": "entertainment", "5": "history", "6": "technology", "7": "tourism", "8": "culture", "9": "fashion", "10": "religion", "11": "environment", "12": "education", "13": "relationship"}}}}, {"name": "kin_label", "dtype": "string"}, {"name": "en_label", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38316546, "num_examples": 17014}, {"name": "test", "num_bytes": 11971938, "num_examples": 4254}], "download_size": 27377755, "dataset_size": 50288484}, {"config_name": "kinnews_cleaned", "features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "politics", "1": "sport", "2": "economy", "3": "health", "4": "entertainment", "5": "history", "6": "technology", "7": "tourism", "8": "culture", "9": "fashion", "10": "religion", "11": "environment", "12": "education", "13": "relationship"}}}}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32780382, "num_examples": 17014}, {"name": "test", "num_bytes": 8217453, "num_examples": 4254}], "download_size": 27377755, "dataset_size": 40997835}, {"config_name": "kirnews_raw", "features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "politics", "1": "sport", "2": "economy", "3": "health", "4": "entertainment", "5": "history", "6": "technology", "7": "tourism", "8": "culture", "9": "fashion", "10": "religion", "11": "environment", "12": "education", "13": "relationship"}}}}, {"name": "kir_label", "dtype": "string"}, {"name": "en_label", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7343223, "num_examples": 3689}, {"name": "test", "num_bytes": 2499189, "num_examples": 923}], "download_size": 5186111, "dataset_size": 9842412}, {"config_name": "kirnews_cleaned", "features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "politics", "1": "sport", "2": "economy", "3": "health", "4": "entertainment", "5": "history", "6": "technology", "7": "tourism", "8": "culture", "9": "fashion", "10": "religion", "11": "environment", "12": "education", "13": "relationship"}}}}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6629767, "num_examples": 3689}, {"name": "test", "num_bytes": 1570745, "num_examples": 923}], "download_size": 5186111, "dataset_size": 8200512}]}

2024-01-18T11:07:35+00:00

TAGS #task_categories-text-classification #task_ids-multi-class-classification #task_ids-topic-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #source_datasets-original #language-Rundi #language-Kinyarwanda #license-mit #arxiv-2010.12174 #region-us

Dataset Card for kinnews\_kirnews ================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Repository: URL * Paper: KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi * Leaderboard: NA * Point of Contact: Rubungo Andre Niyongabo1 ### Dataset Summary Kinyarwanda and Kirundi news classification datasets (KINNEWS and KIRNEWS,respectively), which were both collected from Rwanda and Burundi news websites and newspapers, for low-resource monolingual and cross-lingual multiclass classification tasks. ### Supported Tasks and Leaderboards This dataset can be used for text classification of news articles in Kinyarwadi and Kirundi languages. Each news article can be classified into one of the 14 possible classes. The classes are: * politics * sport * economy * health * entertainment * history * technology * culture * religion * environment * education * relationship ### Languages Kinyarwanda and Kirundi Dataset Structure ----------------- ### Data Instances Here is an example from the dataset: ### Data Fields The raw version of the data for Kinyarwanda language consists of these fields * label: The category of the news article * kin\_label/kir\_label: The associated label in Kinyarwanda/Kirundi language * en\_label: The associated label in English * url: The URL of the news article * title: The title of the news article * content: The content of the news article The cleaned version contains only the 'label', 'title' and the 'content' fields ### Data Splits Lang: Kinyarwandai Raw, Train: 17014, Test: 4254 Lang: Kinyarwandai Clean, Train: 17014, Test: 4254 Lang: Kirundi Raw, Train: 3689, Test: 923 Lang: Kirundi Clean, Train: 3689, Test: 923 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @saradhix for adding this dataset.

349481ec73fff722f88e0453ca05c77a447d967c

# Dataset Card for KLUE ## 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:** https://klue-benchmark.com/ - **Repository:** https://github.com/KLUE-benchmark/KLUE - **Paper:** [KLUE: Korean Language Understanding Evaluation](https://arxiv.org/abs/2105.09680) - **Leaderboard:** [Leaderboard](https://klue-benchmark.com/leaderboard) - **Point of Contact:** https://github.com/KLUE-benchmark/KLUE/issues ### Dataset Summary KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. ### Supported Tasks and Leaderboards Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking ### Languages `ko-KR` ## Dataset Structure ### Data Instances #### ynat An example of 'train' looks as follows. ``` {'date': '2016.06.30. 오전 10:36', 'guid': 'ynat-v1_train_00000', 'label': 3, 'title': '유튜브 내달 2일까지 크리에이터 지원 공간 운영', 'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008508947'} ``` #### sts An example of 'train' looks as follows. ``` {'guid': 'klue-sts-v1_train_00000', 'labels': {'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1}, 'sentence1': '숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.', 'sentence2': '숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.', 'source': 'airbnb-rtt'} ``` #### nli An example of 'train' looks as follows. ``` {'guid': 'klue-nli-v1_train_00000', 'hypothesis': '힛걸 진심 최고로 멋지다.', 'label': 0, 'premise': '힛걸 진심 최고다 그 어떤 히어로보다 멋지다', 'source': 'NSMC'} ``` #### ner An example of 'train' looks as follows. ``` {'tokens': ['특', '히', ' ', '영', '동', '고', '속', '도', '로', ' ', '강', '릉', ' ', '방', '향', ' ', '문', '막', '휴', '게', '소', '에', '서', ' ', '만', '종', '분', '기', '점', '까', '지', ' ', '5', '㎞', ' ', '구', '간', '에', '는', ' ', '승', '용', '차', ' ', '전', '용', ' ', '임', '시', ' ', '갓', '길', '차', '로', '제', '를', ' ', '운', '영', '하', '기', '로', ' ', '했', '다', '.'], 'ner_tags': [12, 12, 12, 2, 3, 3, 3, 3, 3, 12, 2, 3, 12, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 8, 9, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12], 'sentence': '특히 <영동고속도로:LC> <강릉:LC> 방향 <문막휴게소:LC>에서 <만종분기점:LC>까지 <5㎞:QT> 구간에는 승용차 전용 임시 갓길차로제를 운영하기로 했다.'} ``` #### re An example of 'train' looks as follows. ``` {'guid': 'klue-re-v1_train_00000', 'label': 0, 'object_entity': {'word': '조지 해리슨', 'start_idx': 13, 'end_idx': 18, 'type': 'PER'}, 'sentence': '〈Something〉는 조지 해리슨이 쓰고 비틀즈가 1969년 앨범 《Abbey Road》에 담은 노래다.', 'source': 'wikipedia', 'subject_entity': {'word': '비틀즈', 'start_idx': 24, 'end_idx': 26, 'type': 'ORG'}} ``` #### dp An example of 'train' looks as follows. ``` {'deprel': ['NP', 'NP_OBJ', 'VP', 'NP', 'NP_SBJ', 'NP', 'NP_MOD', 'NP_CNJ', 'NP_CNJ', 'NP', 'NP', 'NP_OBJ', 'AP', 'VP'], 'head': [2, 3, 14, 5, 14, 7, 10, 10, 10, 11, 12, 14, 14, 0], 'index': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 'lemma': ['해당', '그림 을', '보 면', '디즈니', '공주 들 이', '브리트니', '스피어스 의', '앨범 이나', '뮤직 비디오 ,', '화보', '속', '모습 을', '똑같이', '재연 하 였 다 .'], 'pos': ['NNG', 'NNG+JKO', 'VV+EC', 'NNP', 'NNG+XSN+JKS', 'NNP', 'NNP+JKG', 'NNG+JC', 'NNG+NNG+SP', 'NNG', 'NNG', 'NNG+JKO', 'MAG', 'NNG+XSA+EP+EF+SF'], 'sentence': '해당 그림을 보면 디즈니 공주들이 브리트니 스피어스의 앨범이나 뮤직비디오, 화보 속 모습을 똑같이 재연했다.', 'word_form': ['해당', '그림을', '보면', '디즈니', '공주들이', '브리트니', '스피어스의', '앨범이나', '뮤직비디오,', '화보', '속', '모습을', '똑같이', '재연했다.']} ``` #### mrc An example of 'train' looks as follows. ``` {'answers': {'answer_start': [478, 478], 'text': ['한 달가량', '한 달']}, 'context': '올여름 장마가 17일 제주도에서 시작됐다. 서울 등 중부지방은 예년보다 사나흘 정도 늦은 이달 말께 장마가 시작될 전망이다.17일 기상청에 따르면 제주도 남쪽 먼바다에 있는 장마전선의 영향으로 이날 제주도 산간 및 내륙지역에 호우주의보가 내려지면서 곳곳에 100㎜에 육박하는 많은 비가 내렸다. 제주의 장마는 평년보다 2~3일, 지난해보다는 하루 일찍 시작됐다. 장마는 고온다습한 북태평양 기단과 한랭 습윤한 오호츠크해 기단이 만나 형성되는 장마전선에서 내리는 비를 뜻한다.장마전선은 18일 제주도 먼 남쪽 해상으로 내려갔다가 20일께 다시 북상해 전남 남해안까지 영향을 줄 것으로 보인다. 이에 따라 20~21일 남부지방에도 예년보다 사흘 정도 장마가 일찍 찾아올 전망이다. 그러나 장마전선을 밀어올리는 북태평양 고기압 세력이 약해 서울 등 중부지방은 평년보다 사나흘가량 늦은 이달 말부터 장마가 시작될 것이라는 게 기상청의 설명이다. 장마전선은 이후 한 달가량 한반도 중남부를 오르내리며 곳곳에 비를 뿌릴 전망이다. 최근 30년간 평균치에 따르면 중부지방의 장마 시작일은 6월24~25일이었으며 장마기간은 32일, 강수일수는 17.2일이었다.기상청은 올해 장마기간의 평균 강수량이 350~400㎜로 평년과 비슷하거나 적을 것으로 내다봤다. 브라질 월드컵 한국과 러시아의 경기가 열리는 18일 오전 서울은 대체로 구름이 많이 끼지만 비는 오지 않을 것으로 예상돼 거리 응원에는 지장이 없을 전망이다.', 'guid': 'klue-mrc-v1_train_12759', 'is_impossible': False, 'news_category': '종합', 'question': '북태평양 기단과 오호츠크해 기단이 만나 국내에 머무르는 기간은?', 'question_type': 1, 'source': 'hankyung', 'title': '제주도 장마 시작 … 중부는 이달 말부터'} ``` #### wos An example of 'train' looks as follows. ``` {'dialogue': [{'role': 'user', 'text': '쇼핑을 하려는데 서울 서쪽에 있을까요?', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽']}, {'role': 'sys', 'text': '서울 서쪽에 쇼핑이 가능한 곳이라면 노량진 수산물 도매시장이 있습니다.', 'state': []}, {'role': 'user', 'text': '오 네 거기 주소 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '노량진 수산물 도매시장의 주소는 서울 동작구 93806입니다.', 'state': []}, {'role': 'user', 'text': '알려주시는김에 연락처랑 평점도 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '그럼. 연락처는 6182006591이고 평점은 4점입니다.', 'state': []}, {'role': 'user', 'text': '와 감사합니다.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '감사합니다.', 'state': []}], 'domains': ['관광'], 'guid': 'wos-v1_train_00001'} ``` ### Data Fields #### ynat + `guid`: a `string` feature + `title`: a `string` feature + `label`: a classification label, with possible values `IT과학`(0), `경제`(1), `사회`(2), `생활문화`(3), `세계`(4), `스포츠`(5), `정치`(6) + `url`: a `string` feature + `date`: a `string` feature #### sts + `guid`: a `string` feature + `source`: a `string` feature + `sentence1`: a `string` feature + `sentence2`: a `string` feature + `labels`: a dictionary feature containing + `label`: a `float64` feature + `real-label`: a `float64` feature + `binary-label`: a classification label, with possible values `negative`(0), `positive`(1) #### nli + `guid`: a `string` feature + `source`: a `string` feature + `premise`: a `string` feature + `hypothesis`: a `string` feature + `label`: a classification label, with possible values `entailment`(0), `neutral`(1), `contradiction`(2) #### ner + `sentence`: a `string` feature + `tokens`: a list of a `string` feature (tokenization is at character level) + `ner_tags`: a list of classification labels, with possible values including `B-DT`(0), `I-DT`(1), `B-LC`(2), `I-LC`(3), `B-OG`(4), `I-OG`(5), `B-PS`(6), `I-PS`(7), `B-QT`(8), `I-QT`(9), `B-TI`(10), `I-TI`(11), `O`(12) #### re + `guid`: a `string` feature + `sentence`: a `string` feature + `subject_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `object_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `label`: a list of labels, with possible values including `no_relation`(0), `org:dissolved`(1), `org:founded`(2), `org:place_of_headquarters`(3), `org:alternate_names`(4), `org:member_of`(5), `org:members`(6), `org:political/religious_affiliation`(7), `org:product`(8), `org:founded_by`(9),`org:top_members/employees`(10), `org:number_of_employees/members`(11), `per:date_of_birth`(12), `per:date_of_death`(13), `per:place_of_birth`(14), `per:place_of_death`(15), `per:place_of_residence`(16), `per:origin`(17), `per:employee_of`(18), `per:schools_attended`(19), `per:alternate_names`(20), `per:parents`(21), `per:children`(22), `per:siblings`(23), `per:spouse`(24), `per:other_family`(25), `per:colleagues`(26), `per:product`(27), `per:religion`(28), `per:title`(29), + `source`: a `string` feature #### dp + `sentence`: a `string` feature + `index`: a list of `int32` feature + `word_form`: a list of `string` feature + `lemma`: a list of `string` feature + `pos`: a list of `string` feature + `head`: a list of `int32` feature + `deprel`: a list of `string` feature #### mrc + `title`: a `string` feature + `context`: a `string` feature + `news_category`: a `string` feature + `source`: a `string` feature + `guid`: a `string` feature + `is_impossible`: a `bool` feature + `question_type`: a `int32` feature + `question`: a `string` feature + `answers`: a dictionary feature containing + `answer_start`: a `int32` feature + `text`: a `string` feature #### wos + `guid`: a `string` feature + `domains`: a `string` feature + `dialogue`: a list of dictionary feature containing + `role`: a `string` feature + `text`: a `string` feature + `state`: a `string` feature ### Data Splits #### ynat You can see more details in [here](https://klue-benchmark.com/tasks/66/data/description). + train: 45,678 + validation: 9,107 #### sts You can see more details in [here](https://klue-benchmark.com/tasks/67/data/description). + train: 11,668 + validation: 519 #### nli You can see more details in [here](https://klue-benchmark.com/tasks/68/data/description). + train: 24,998 + validation: 3,000 #### ner You can see more details in [here](https://klue-benchmark.com/tasks/69/overview/description). + train: 21,008 + validation: 5,000 #### re You can see more details in [here](https://klue-benchmark.com/tasks/70/overview/description). + train: 32,470 + validation: 7,765 #### dp You can see more details in [here](https://klue-benchmark.com/tasks/71/data/description). + train: 10,000 + validation: 2,000 #### mrc You can see more details in [here](https://klue-benchmark.com/tasks/72/overview/description). + train: 17,554 + validation: 5,841 #### wos You can see more details in [here](https://klue-benchmark.com/tasks/73/overview/description). + train: 8,000 + validation: 1,000 ## 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 ``` @misc{park2021klue, title={KLUE: Korean Language Understanding Evaluation}, author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho}, year={2021}, eprint={2105.09680}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@jungwhank](https://github.com/jungwhank), [@bzantium](https://github.com/bzantium) for adding this dataset.

klue

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["ko"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["fill-mask", "question-answering", "text-classification", "text-generation", "token-classification"], "task_ids": ["extractive-qa", "named-entity-recognition", "natural-language-inference", "parsing", "semantic-similarity-scoring", "text-scoring", "topic-classification"], "paperswithcode_id": "klue", "pretty_name": "KLUE", "config_names": ["dp", "mrc", "ner", "nli", "re", "sts", "wos", "ynat"], "tags": ["relation-extraction"], "dataset_info": [{"config_name": "dp", "features": [{"name": "sentence", "dtype": "string"}, {"name": "index", "list": "int32"}, {"name": "word_form", "list": "string"}, {"name": "lemma", "list": "string"}, {"name": "pos", "list": "string"}, {"name": "head", "list": "int32"}, {"name": "deprel", "list": "string"}], "splits": [{"name": "train", "num_bytes": 7899965, "num_examples": 10000}, {"name": "validation", "num_bytes": 1557462, "num_examples": 2000}], "download_size": 3742577, "dataset_size": 9457427}, {"config_name": "mrc", "features": [{"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "news_category", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "guid", "dtype": "string"}, {"name": "is_impossible", "dtype": "bool"}, {"name": "question_type", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 46505593, "num_examples": 17554}, {"name": "validation", "num_bytes": 15583017, "num_examples": 5841}], "download_size": 30098472, "dataset_size": 62088610}, {"config_name": "ner", "features": [{"name": "sentence", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "B-DT", "1": "I-DT", "2": "B-LC", "3": "I-LC", "4": "B-OG", "5": "I-OG", "6": "B-PS", "7": "I-PS", "8": "B-QT", "9": "I-QT", "10": "B-TI", "11": "I-TI", "12": "O"}}}}], "splits": [{"name": "train", "num_bytes": 19891905, "num_examples": 21008}, {"name": "validation", "num_bytes": 4937563, "num_examples": 5000}], "download_size": 5265887, "dataset_size": 24829468}, {"config_name": "nli", "features": [{"name": "guid", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 5719882, "num_examples": 24998}, {"name": "validation", "num_bytes": 673260, "num_examples": 3000}], "download_size": 2056116, "dataset_size": 6393142}, {"config_name": "re", "features": [{"name": "guid", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "subject_entity", "struct": [{"name": "word", "dtype": "string"}, {"name": "start_idx", "dtype": "int32"}, {"name": "end_idx", "dtype": "int32"}, {"name": "type", "dtype": "string"}]}, {"name": "object_entity", "struct": [{"name": "word", "dtype": "string"}, {"name": "start_idx", "dtype": "int32"}, {"name": "end_idx", "dtype": "int32"}, {"name": "type", "dtype": "string"}]}, {"name": "label", "dtype": {"class_label": {"names": {"0": "no_relation", "1": "org:dissolved", "2": "org:founded", "3": "org:place_of_headquarters", "4": "org:alternate_names", "5": "org:member_of", "6": "org:members", "7": "org:political/religious_affiliation", "8": "org:product", "9": "org:founded_by", "10": "org:top_members/employees", "11": "org:number_of_employees/members", "12": "per:date_of_birth", "13": "per:date_of_death", "14": "per:place_of_birth", "15": "per:place_of_death", "16": "per:place_of_residence", "17": "per:origin", "18": "per:employee_of", "19": "per:schools_attended", "20": "per:alternate_names", "21": "per:parents", "22": "per:children", "23": "per:siblings", "24": "per:spouse", "25": "per:other_family", "26": "per:colleagues", "27": "per:product", "28": "per:religion", "29": "per:title"}}}}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11145426, "num_examples": 32470}, {"name": "validation", "num_bytes": 2559272, "num_examples": 7765}], "download_size": 8190257, "dataset_size": 13704698}, {"config_name": "sts", "features": [{"name": "guid", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "struct": [{"name": "label", "dtype": "float64"}, {"name": "real-label", "dtype": "float64"}, {"name": "binary-label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}]}], "splits": [{"name": "train", "num_bytes": 2832889, "num_examples": 11668}, {"name": "validation", "num_bytes": 122641, "num_examples": 519}], "download_size": 1587855, "dataset_size": 2955530}, {"config_name": "wos", "features": [{"name": "guid", "dtype": "string"}, {"name": "domains", "list": "string"}, {"name": "dialogue", "list": [{"name": "role", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "state", "list": "string"}]}], "splits": [{"name": "train", "num_bytes": 26676970, "num_examples": 8000}, {"name": "validation", "num_bytes": 3488911, "num_examples": 1000}], "download_size": 6358855, "dataset_size": 30165881}, {"config_name": "ynat", "features": [{"name": "guid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "IT\uacfc\ud559", "1": "\uacbd\uc81c", "2": "\uc0ac\ud68c", "3": "\uc0dd\ud65c\ubb38\ud654", "4": "\uc138\uacc4", "5": "\uc2a4\ud3ec\uce20", "6": "\uc815\uce58"}}}}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10109584, "num_examples": 45678}, {"name": "validation", "num_bytes": 2039181, "num_examples": 9107}], "download_size": 5012303, "dataset_size": 12148765}], "configs": [{"config_name": "dp", "data_files": [{"split": "train", "path": "dp/train-*"}, {"split": "validation", "path": "dp/validation-*"}]}, {"config_name": "mrc", "data_files": [{"split": "train", "path": "mrc/train-*"}, {"split": "validation", "path": "mrc/validation-*"}]}, {"config_name": "ner", "data_files": [{"split": "train", "path": "ner/train-*"}, {"split": "validation", "path": "ner/validation-*"}]}, {"config_name": "nli", "data_files": [{"split": "train", "path": "nli/train-*"}, {"split": "validation", "path": "nli/validation-*"}]}, {"config_name": "re", "data_files": [{"split": "train", "path": "re/train-*"}, {"split": "validation", "path": "re/validation-*"}]}, {"config_name": "sts", "data_files": [{"split": "train", "path": "sts/train-*"}, {"split": "validation", "path": "sts/validation-*"}]}, {"config_name": "wos", "data_files": [{"split": "train", "path": "wos/train-*"}, {"split": "validation", "path": "wos/validation-*"}]}, {"config_name": "ynat", "data_files": [{"split": "train", "path": "ynat/train-*"}, {"split": "validation", "path": "ynat/validation-*"}]}]}

2024-01-04T14:05:57+00:00

TAGS #task_categories-fill-mask #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-token-classification #task_ids-extractive-qa #task_ids-named-entity-recognition #task_ids-natural-language-inference #task_ids-parsing #task_ids-semantic-similarity-scoring #task_ids-text-scoring #task_ids-topic-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Korean #license-cc-by-sa-4.0 #relation-extraction #arxiv-2105.09680 #region-us

# Dataset Card for KLUE ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information ## Dataset Description - Homepage: URL - Repository: URL - Paper: KLUE: Korean Language Understanding Evaluation - Leaderboard: Leaderboard - Point of Contact: URL ### Dataset Summary KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. ### Supported Tasks and Leaderboards Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking ### Languages 'ko-KR' ## Dataset Structure ### Data Instances #### ynat An example of 'train' looks as follows. #### sts An example of 'train' looks as follows. #### nli An example of 'train' looks as follows. #### ner An example of 'train' looks as follows. #### re An example of 'train' looks as follows. #### dp An example of 'train' looks as follows. #### mrc An example of 'train' looks as follows. #### wos An example of 'train' looks as follows. ### Data Fields #### ynat + 'guid': a 'string' feature + 'title': a 'string' feature + 'label': a classification label, with possible values 'IT과학'(0), '경제'(1), '사회'(2), '생활문화'(3), '세계'(4), '스포츠'(5), '정치'(6) + 'url': a 'string' feature + 'date': a 'string' feature #### sts + 'guid': a 'string' feature + 'source': a 'string' feature + 'sentence1': a 'string' feature + 'sentence2': a 'string' feature + 'labels': a dictionary feature containing + 'label': a 'float64' feature + 'real-label': a 'float64' feature + 'binary-label': a classification label, with possible values 'negative'(0), 'positive'(1) #### nli + 'guid': a 'string' feature + 'source': a 'string' feature + 'premise': a 'string' feature + 'hypothesis': a 'string' feature + 'label': a classification label, with possible values 'entailment'(0), 'neutral'(1), 'contradiction'(2) #### ner + 'sentence': a 'string' feature + 'tokens': a list of a 'string' feature (tokenization is at character level) + 'ner_tags': a list of classification labels, with possible values including 'B-DT'(0), 'I-DT'(1), 'B-LC'(2), 'I-LC'(3), 'B-OG'(4), 'I-OG'(5), 'B-PS'(6), 'I-PS'(7), 'B-QT'(8), 'I-QT'(9), 'B-TI'(10), 'I-TI'(11), 'O'(12) #### re + 'guid': a 'string' feature + 'sentence': a 'string' feature + 'subject_entity': a dictionary feature containing + 'word': a 'string' feature + 'start_idx': a 'int32' feature + 'end_idx': a 'int32' feature + 'type': a 'string' feature + 'object_entity': a dictionary feature containing + 'word': a 'string' feature + 'start_idx': a 'int32' feature + 'end_idx': a 'int32' feature + 'type': a 'string' feature + 'label': a list of labels, with possible values including 'no_relation'(0), 'org:dissolved'(1), 'org:founded'(2), 'org:place_of_headquarters'(3), 'org:alternate_names'(4), 'org:member_of'(5), 'org:members'(6), 'org:political/religious_affiliation'(7), 'org:product'(8), 'org:founded_by'(9),'org:top_members/employees'(10), 'org:number_of_employees/members'(11), 'per:date_of_birth'(12), 'per:date_of_death'(13), 'per:place_of_birth'(14), 'per:place_of_death'(15), 'per:place_of_residence'(16), 'per:origin'(17), 'per:employee_of'(18), 'per:schools_attended'(19), 'per:alternate_names'(20), 'per:parents'(21), 'per:children'(22), 'per:siblings'(23), 'per:spouse'(24), 'per:other_family'(25), 'per:colleagues'(26), 'per:product'(27), 'per:religion'(28), 'per:title'(29), + 'source': a 'string' feature #### dp + 'sentence': a 'string' feature + 'index': a list of 'int32' feature + 'word_form': a list of 'string' feature + 'lemma': a list of 'string' feature + 'pos': a list of 'string' feature + 'head': a list of 'int32' feature + 'deprel': a list of 'string' feature #### mrc + 'title': a 'string' feature + 'context': a 'string' feature + 'news_category': a 'string' feature + 'source': a 'string' feature + 'guid': a 'string' feature + 'is_impossible': a 'bool' feature + 'question_type': a 'int32' feature + 'question': a 'string' feature + 'answers': a dictionary feature containing + 'answer_start': a 'int32' feature + 'text': a 'string' feature #### wos + 'guid': a 'string' feature + 'domains': a 'string' feature + 'dialogue': a list of dictionary feature containing + 'role': a 'string' feature + 'text': a 'string' feature + 'state': a 'string' feature ### Data Splits #### ynat You can see more details in here. + train: 45,678 + validation: 9,107 #### sts You can see more details in here. + train: 11,668 + validation: 519 #### nli You can see more details in here. + train: 24,998 + validation: 3,000 #### ner You can see more details in here. + train: 21,008 + validation: 5,000 #### re You can see more details in here. + train: 32,470 + validation: 7,765 #### dp You can see more details in here. + train: 10,000 + validation: 2,000 #### mrc You can see more details in here. + train: 17,554 + validation: 5,841 #### wos You can see more details in here. + train: 8,000 + validation: 1,000 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jungwhank, @bzantium for adding this dataset.

9ae5d41448b176a3d4d5f937b921a120ee9e1139

# Dataset Card for 3i4K ## 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:** [3i4K](https://github.com/warnikchow/3i4k) - **Repository:** [3i4K](https://github.com/warnikchow/3i4k) - **Paper:** [Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency](https://arxiv.org/abs/1811.04231) - **Point of Contact:** [Won Ik Cho]([email protected]) ### Dataset Summary The 3i4K dataset is a set of frequently used Korean words (corpus provided by the Seoul National University Speech Language Processing Lab) and manually created questions/commands containing short utterances. The goal is to identify the speaker intention of a spoken utterance based on its transcript, and whether in some cases, requires using auxiliary acoustic features. The classification system decides whether the utterance is a fragment, statement, question, command, rhetorical question, rhetorical command, or an intonation-dependent utterance. This is important because in head-final languages like Korean, the level of the intonation plays a significant role in identifying the speaker's intention. ### Supported Tasks and Leaderboards * `intent-classification`: The dataset can be trained with a CNN or BiLISTM-Att to identify the intent of a spoken utterance in Korean and the performance can be measured by its F1 score. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`. ## Dataset Structure ### Data Instances An example data instance contains a short utterance and it's label: ``` { "label": 3, "text": "선수잖아 이 케이스 저 케이스 많을 거 아냐 선배라고 뭐 하나 인생에 도움도 안주는데 내가 이렇게 진지하게 나올 때 제대로 한번 조언 좀 해줘보지" } ``` ### Data Fields * `label`: determines the intention of the utterance and can be one of `fragment` (0), `statement` (1), `question` (2), `command` (3), `rhetorical question` (4), `rhetorical command` (5) and `intonation-depedent utterance` (6). * `text`: the text in Korean about common topics like housework, weather, transportation, etc. ### Data Splits The data is split into a training set comrpised of 55134 examples and a test set of 6121 examples. ## Dataset Creation ### Curation Rationale For head-final languages like Korean, intonation can be a determining factor in identifying the speaker's intention. The purpose of this dataset is to to determine whether an utterance is a fragment, statement, question, command, or a rhetorical question/command using the intonation-depedency from the head-finality. This is expected to improve language understanding of spoken Korean utterances and can be beneficial for speech-to-text applications. ### Source Data #### Initial Data Collection and Normalization The corpus was provided by Seoul National University Speech Language Processing Lab, a set of frequently used words from the National Institute of Korean Language and manually created commands and questions. The utterances cover topics like weather, transportation and stocks. 20k lines were randomly selected. #### Who are the source language producers? Korean speakers produced the commands and questions. ### Annotations #### Annotation process Utterances were classified into seven categories. They were provided clear instructions on the annotation guidelines (see [here](https://docs.google.com/document/d/1-dPL5MfsxLbWs7vfwczTKgBq_1DX9u1wxOgOPn1tOss/edit#) for the guidelines) and the resulting inter-annotator agreement was 0.85 and the final decision was done by majority voting. #### Who are the annotators? The annotation was completed by three Seoul Korean L1 speakers. ### 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 is curated by Won Ik Cho, Hyeon Seung Lee, Ji Won Yoon, Seok Min Kim and Nam Soo Kim. ### Licensing Information The dataset is licensed under the CC BY-SA-4.0. ### Citation Information ``` @article{cho2018speech, title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency}, author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo}, journal={arXiv preprint arXiv:1811.04231}, year={2018} } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.

kor_3i4k

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["ko"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification"], "pretty_name": "3i4K", "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "fragment", "1": "statement", "2": "question", "3": "command", "4": "rhetorical question", "5": "rhetorical command", "6": "intonation-dependent utterance"}}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3102158, "num_examples": 55134}, {"name": "test", "num_bytes": 344028, "num_examples": 6121}], "download_size": 2956114, "dataset_size": 3446186}}

2024-01-18T11:07:37+00:00

TAGS #task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Korean #license-cc-by-4.0 #arxiv-1811.04231 #region-us

# Dataset Card for 3i4K ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: 3i4K - Repository: 3i4K - Paper: Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency - Point of Contact: Won Ik Cho ### Dataset Summary The 3i4K dataset is a set of frequently used Korean words (corpus provided by the Seoul National University Speech Language Processing Lab) and manually created questions/commands containing short utterances. The goal is to identify the speaker intention of a spoken utterance based on its transcript, and whether in some cases, requires using auxiliary acoustic features. The classification system decides whether the utterance is a fragment, statement, question, command, rhetorical question, rhetorical command, or an intonation-dependent utterance. This is important because in head-final languages like Korean, the level of the intonation plays a significant role in identifying the speaker's intention. ### Supported Tasks and Leaderboards * 'intent-classification': The dataset can be trained with a CNN or BiLISTM-Att to identify the intent of a spoken utterance in Korean and the performance can be measured by its F1 score. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is 'ko-KR'. ## Dataset Structure ### Data Instances An example data instance contains a short utterance and it's label: ### Data Fields * 'label': determines the intention of the utterance and can be one of 'fragment' (0), 'statement' (1), 'question' (2), 'command' (3), 'rhetorical question' (4), 'rhetorical command' (5) and 'intonation-depedent utterance' (6). * 'text': the text in Korean about common topics like housework, weather, transportation, etc. ### Data Splits The data is split into a training set comrpised of 55134 examples and a test set of 6121 examples. ## Dataset Creation ### Curation Rationale For head-final languages like Korean, intonation can be a determining factor in identifying the speaker's intention. The purpose of this dataset is to to determine whether an utterance is a fragment, statement, question, command, or a rhetorical question/command using the intonation-depedency from the head-finality. This is expected to improve language understanding of spoken Korean utterances and can be beneficial for speech-to-text applications. ### Source Data #### Initial Data Collection and Normalization The corpus was provided by Seoul National University Speech Language Processing Lab, a set of frequently used words from the National Institute of Korean Language and manually created commands and questions. The utterances cover topics like weather, transportation and stocks. 20k lines were randomly selected. #### Who are the source language producers? Korean speakers produced the commands and questions. ### Annotations #### Annotation process Utterances were classified into seven categories. They were provided clear instructions on the annotation guidelines (see here for the guidelines) and the resulting inter-annotator agreement was 0.85 and the final decision was done by majority voting. #### Who are the annotators? The annotation was completed by three Seoul Korean L1 speakers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators The dataset is curated by Won Ik Cho, Hyeon Seung Lee, Ji Won Yoon, Seok Min Kim and Nam Soo Kim. ### Licensing Information The dataset is licensed under the CC BY-SA-4.0. ### Contributions Thanks to @stevhliu for adding this dataset.

bd1a7370caf712125fac1fda375834ca8ddefaca

# 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:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech) - **Repository:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech) - **Paper:** [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](https://arxiv.org/abs/2005.12503) - **Point of Contact:** [Steven Liu]([email protected]) ### Dataset Summary The Korean HateSpeech Dataset is a dataset of 8367 human-labeled entertainment news comments from a popular Korean news aggregation platform. Each comment was evaluated for either social bias (labels: `gender`, `others` `none`), hate speech (labels: `hate`, `offensive`, `none`) or gender bias (labels: `True`, `False`). The dataset was created to support the identification of toxic comments on online platforms where users can remain anonymous. ### Supported Tasks and Leaderboards * `multi-label classification`: The dataset can be used to train a model for hate speech detection. A BERT model can be presented with a Korean entertainment news comment and be asked to label whether it contains social bias, gender bias and hate speech. Users can participate in a Kaggle leaderboard [here](https://www.kaggle.com/c/korean-hate-speech-detection/overview). ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`. ## Dataset Structure ### Data Instances An example data instance contains a `comments` containing the text of the news comment and then labels for each of the following fields: `contain_gender_bias`, `bias` and `hate`. ```python {'comments':'설마 ㅈ 현정 작가 아니지??' 'contain_gender_bias': 'True', 'bias': 'gender', 'hate': 'hate' } ``` ### Data Fields * `comments`: text from the Korean news comment * `contain_gender_bias`: a binary `True`/`False` label for the presence of gender bias * `bias`: determines the type of social bias, which can be: * `gender`: if the text includes bias for gender role, sexual orientation, sexual identity, and any thoughts on gender-related acts * `others`: other kinds of factors that are considered not gender-related but social bias, including race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience * `none`: a comment that does not incorporate the bias * `hate`: determines how aggressive the comment is, which can be: * `hate`: if the text is defined as an expression that display aggressive stances towards individuals/groups with certain characteristics (gender role, sexual orientation, sexual identity, any thoughts on gender-related acts, race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience, etc.) * `offensive`: if the text contains rude or aggressive contents, can emit sarcasm through rhetorical question or irony, encompass an unethical expression or conveys unidentified rumors * `none`: a comment that does not incorporate hate ### Data Splits The data is split into a training and development (test) set. It contains 8371 annotated comments that are split into 7896 comments in the training set and 471 comments in the test set. ## Dataset Creation ### Curation Rationale The dataset was created to provide the first human-labeled Korean corpus for toxic speech detection from a Korean online entertainment news aggregator. Recently, two young Korean celebrities suffered from a series of tragic incidents that led to two major Korean web portals to close the comments section on their platform. However, this only serves as a temporary solution, and the fundamental issue has not been solved yet. This dataset hopes to improve Korean hate speech detection. ### Source Data #### Initial Data Collection and Normalization A total of 10.4 million comments were collected from an online Korean entertainment news aggregator between Jan. 1, 2018 and Feb. 29, 2020. 1,580 articles were drawn using stratified sampling and the top 20 comments were extracted ranked in order of their Wilson score on the downvote for each article. Duplicate comments, single token comments and comments with more than 100 characters were removed (because they could convey various opinions). From here, 10K comments were randomly chosen for annotation. #### Who are the source language producers? The language producers are users of the Korean online news platform between 2018 and 2020. ### Annotations #### Annotation process Each comment was assigned to three random annotators to assign a majority decision. For more ambiguous comments, annotators were allowed to skip the comment. See Appendix A in the [paper](https://arxiv.org/pdf/2005.12503.pdf) for more detailed guidelines. #### Who are the annotators? Annotation was performed by 32 annotators, consisting of 29 annotators from the crowdsourcing platform DeepNatural AI and three NLP researchers. ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to tackle the social issue of users creating toxic comments on online platforms. This dataset aims to improve detection of toxic comments online. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset is curated by Jihyung Moon, Won Ik Cho and Junbum Lee. ### Licensing Information [N/A] ### Citation Information ``` @inproceedings {moon-et-al-2020-beep title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection", author = "Moon, Jihyung and Cho, Won Ik and Lee, Junbum", booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.socialnlp-1.4", pages = "25--31", abstract = "Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff{'}s alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks.", } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.

kor_hate

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["found"], "language": ["ko"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "paperswithcode_id": "korean-hatespeech-dataset", "pretty_name": "Korean HateSpeech Dataset", "dataset_info": {"features": [{"name": "comments", "dtype": "string"}, {"name": "contain_gender_bias", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}, {"name": "bias", "dtype": {"class_label": {"names": {"0": "none", "1": "gender", "2": "others"}}}}, {"name": "hate", "dtype": {"class_label": {"names": {"0": "hate", "1": "offensive", "2": "none"}}}}], "splits": [{"name": "train", "num_bytes": 983608, "num_examples": 7896}, {"name": "test", "num_bytes": 58913, "num_examples": 471}], "download_size": 968449, "dataset_size": 1042521}}

2024-01-18T11:07:38+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Korean #license-cc-by-sa-4.0 #arxiv-2005.12503 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Korean HateSpeech Dataset - Repository: Korean HateSpeech Dataset - Paper: BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection - Point of Contact: Steven Liu ### Dataset Summary The Korean HateSpeech Dataset is a dataset of 8367 human-labeled entertainment news comments from a popular Korean news aggregation platform. Each comment was evaluated for either social bias (labels: 'gender', 'others' 'none'), hate speech (labels: 'hate', 'offensive', 'none') or gender bias (labels: 'True', 'False'). The dataset was created to support the identification of toxic comments on online platforms where users can remain anonymous. ### Supported Tasks and Leaderboards * 'multi-label classification': The dataset can be used to train a model for hate speech detection. A BERT model can be presented with a Korean entertainment news comment and be asked to label whether it contains social bias, gender bias and hate speech. Users can participate in a Kaggle leaderboard here. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is 'ko-KR'. ## Dataset Structure ### Data Instances An example data instance contains a 'comments' containing the text of the news comment and then labels for each of the following fields: 'contain_gender_bias', 'bias' and 'hate'. ### Data Fields * 'comments': text from the Korean news comment * 'contain_gender_bias': a binary 'True'/'False' label for the presence of gender bias * 'bias': determines the type of social bias, which can be: * 'gender': if the text includes bias for gender role, sexual orientation, sexual identity, and any thoughts on gender-related acts * 'others': other kinds of factors that are considered not gender-related but social bias, including race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience * 'none': a comment that does not incorporate the bias * 'hate': determines how aggressive the comment is, which can be: * 'hate': if the text is defined as an expression that display aggressive stances towards individuals/groups with certain characteristics (gender role, sexual orientation, sexual identity, any thoughts on gender-related acts, race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience, etc.) * 'offensive': if the text contains rude or aggressive contents, can emit sarcasm through rhetorical question or irony, encompass an unethical expression or conveys unidentified rumors * 'none': a comment that does not incorporate hate ### Data Splits The data is split into a training and development (test) set. It contains 8371 annotated comments that are split into 7896 comments in the training set and 471 comments in the test set. ## Dataset Creation ### Curation Rationale The dataset was created to provide the first human-labeled Korean corpus for toxic speech detection from a Korean online entertainment news aggregator. Recently, two young Korean celebrities suffered from a series of tragic incidents that led to two major Korean web portals to close the comments section on their platform. However, this only serves as a temporary solution, and the fundamental issue has not been solved yet. This dataset hopes to improve Korean hate speech detection. ### Source Data #### Initial Data Collection and Normalization A total of 10.4 million comments were collected from an online Korean entertainment news aggregator between Jan. 1, 2018 and Feb. 29, 2020. 1,580 articles were drawn using stratified sampling and the top 20 comments were extracted ranked in order of their Wilson score on the downvote for each article. Duplicate comments, single token comments and comments with more than 100 characters were removed (because they could convey various opinions). From here, 10K comments were randomly chosen for annotation. #### Who are the source language producers? The language producers are users of the Korean online news platform between 2018 and 2020. ### Annotations #### Annotation process Each comment was assigned to three random annotators to assign a majority decision. For more ambiguous comments, annotators were allowed to skip the comment. See Appendix A in the paper for more detailed guidelines. #### Who are the annotators? Annotation was performed by 32 annotators, consisting of 29 annotators from the crowdsourcing platform DeepNatural AI and three NLP researchers. ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to tackle the social issue of users creating toxic comments on online platforms. This dataset aims to improve detection of toxic comments online. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators This dataset is curated by Jihyung Moon, Won Ik Cho and Junbum Lee. ### Licensing Information [N/A] ### Contributions Thanks to @stevhliu for adding this dataset.

f84e2370f885fb087e6b2daa6e93a6898ab2f379

# Dataset Card for KorNER ## 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/kmounlp/NER) - **Repository:** [Github](https://github.com/kmounlp/NER) - **Paper:** - **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 Each row consists of the following fields: - `text`: The full text, as is - `annot_text`: Annotated text including POS-tagged information - `tokens`: An ordered list of tokens from the full text - `pos_tags`: Part-of-speech tags for each token - `ner_tags`: Named entity recognition tags for each token Note that by design, the length of `tokens`, `pos_tags`, and `ner_tags` will always be identical. `pos_tags` corresponds to the list below: ``` ['SO', 'SS', 'VV', 'XR', 'VCP', 'JC', 'VCN', 'JKB', 'MM', 'SP', 'XSN', 'SL', 'NNP', 'NP', 'EP', 'JKQ', 'IC', 'XSA', 'EC', 'EF', 'SE', 'XPN', 'ETN', 'SH', 'XSV', 'MAG', 'SW', 'ETM', 'JKO', 'NNB', 'MAJ', 'NNG', 'JKV', 'JKC', 'VA', 'NR', 'JKG', 'VX', 'SF', 'JX', 'JKS', 'SN'] ``` `ner_tags` correspond to the following: ``` ["I", "O", "B_OG", "B_TI", "B_LC", "B_DT", "B_PS"] ``` The prefix `B` denotes the first item of a phrase, and an `I` denotes any non-initial word. In addition, `OG` represens an organization; `TI`, time; `DT`, date, and `PS`, person. ### 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 [@jaketae](https://github.com/jaketae) for adding this dataset.

kor_ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["ko"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "KorNER", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "annot_text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "SO", "1": "SS", "2": "VV", "3": "XR", "4": "VCP", "5": "JC", "6": "VCN", "7": "JKB", "8": "MM", "9": "SP", "10": "XSN", "11": "SL", "12": "NNP", "13": "NP", "14": "EP", "15": "JKQ", "16": "IC", "17": "XSA", "18": "EC", "19": "EF", "20": "SE", "21": "XPN", "22": "ETN", "23": "SH", "24": "XSV", "25": "MAG", "26": "SW", "27": "ETM", "28": "JKO", "29": "NNB", "30": "MAJ", "31": "NNG", "32": "JKV", "33": "JKC", "34": "VA", "35": "NR", "36": "JKG", "37": "VX", "38": "SF", "39": "JX", "40": "JKS", "41": "SN"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "I", "1": "O", "2": "B_OG", "3": "B_TI", "4": "B_LC", "5": "B_DT", "6": "B_PS"}}}}], "splits": [{"name": "train", "num_bytes": 3948938, "num_examples": 2928}, {"name": "test", "num_bytes": 476850, "num_examples": 366}, {"name": "validation", "num_bytes": 486178, "num_examples": 366}], "download_size": 3493175, "dataset_size": 4911966}}

2024-01-18T11:07:39+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Korean #license-mit #region-us

# Dataset Card for KorNER ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields Each row consists of the following fields: - 'text': The full text, as is - 'annot_text': Annotated text including POS-tagged information - 'tokens': An ordered list of tokens from the full text - 'pos_tags': Part-of-speech tags for each token - 'ner_tags': Named entity recognition tags for each token Note that by design, the length of 'tokens', 'pos_tags', and 'ner_tags' will always be identical. 'pos_tags' corresponds to the list below: 'ner_tags' correspond to the following: The prefix 'B' denotes the first item of a phrase, and an 'I' denotes any non-initial word. In addition, 'OG' represens an organization; 'TI', time; 'DT', date, and 'PS', person. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jaketae for adding this dataset.

67f64b872c300e1dea2849d35779ed80a99caeee

# Dataset Card for "kor_nli" ## 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/kakaobrain/KorNLUDatasets](https://github.com/kakaobrain/KorNLUDatasets) - **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:** 126.34 MB - **Size of the generated dataset:** 166.43 MB - **Total amount of disk used:** 292.77 MB ### Dataset Summary Korean Natural Language Inference 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 #### multi_nli - **Size of downloaded dataset files:** 42.11 MB - **Size of the generated dataset:** 84.72 MB - **Total amount of disk used:** 126.85 MB An example of 'train' looks as follows. ``` ``` #### snli - **Size of downloaded dataset files:** 42.11 MB - **Size of the generated dataset:** 80.13 MB - **Total amount of disk used:** 122.25 MB An example of 'train' looks as follows. ``` ``` #### xnli - **Size of downloaded dataset files:** 42.11 MB - **Size of the generated dataset:** 1.56 MB - **Total amount of disk used:** 43.68 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### multi_nli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### snli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### xnli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits #### multi_nli | |train | |---------|-----:| |multi_nli|392702| #### snli | |train | |----|-----:| |snli|550152| #### xnli | |validation|test| |----|---------:|---:| |xnli| 2490|5010| ## 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 dataset is licensed under Creative Commons [Attribution-ShareAlike license (CC BY-SA 4.0)](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @article{ham2020kornli, title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding}, author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon}, journal={arXiv preprint arXiv:2004.03289}, year={2020} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

kor_nli

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["machine-generated", "expert-generated"], "language": ["ko"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|multi_nli", "extended|snli", "extended|xnli"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"], "paperswithcode_id": "kornli", "pretty_name": "KorNLI", "dataset_info": [{"config_name": "multi_nli", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 84729207, "num_examples": 392702}], "download_size": 42113232, "dataset_size": 84729207}, {"config_name": "snli", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 80137097, "num_examples": 550152}], "download_size": 42113232, "dataset_size": 80137097}, {"config_name": "xnli", "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": 518830, "num_examples": 2490}, {"name": "test", "num_bytes": 1047437, "num_examples": 5010}], "download_size": 42113232, "dataset_size": 1566267}]}

2024-01-18T11:07:40+00:00

TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-crowdsourced #language_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|multi_nli #source_datasets-extended|snli #source_datasets-extended|xnli #language-Korean #license-cc-by-sa-4.0 #region-us

Dataset Card for "kor\_nli" =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 126.34 MB * Size of the generated dataset: 166.43 MB * Total amount of disk used: 292.77 MB ### Dataset Summary Korean Natural Language Inference datasets. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### multi\_nli * Size of downloaded dataset files: 42.11 MB * Size of the generated dataset: 84.72 MB * Total amount of disk used: 126.85 MB An example of 'train' looks as follows. #### snli * Size of downloaded dataset files: 42.11 MB * Size of the generated dataset: 80.13 MB * Total amount of disk used: 122.25 MB An example of 'train' looks as follows. #### xnli * Size of downloaded dataset files: 42.11 MB * Size of the generated dataset: 1.56 MB * Total amount of disk used: 43.68 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### multi\_nli * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### snli * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### xnli * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). ### Data Splits #### multi\_nli #### snli #### xnli Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is licensed under Creative Commons Attribution-ShareAlike license (CC BY-SA 4.0). ### Contributions Thanks to @thomwolf, @lhoestq, @lewtun, @patrickvonplaten for adding this dataset.

c5981f100295338f8e74986833b09596902a7290

# 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:** [Github](https://github.com/kakaobrain/KorNLUDatasets) - **Repository:** [Github](https://github.com/kakaobrain/KorNLUDatasets) - **Paper:** [Arxiv](https://arxiv.org/abs/2004.03289) - **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 [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.

kor_nlu

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["expert-generated", "found", "machine-generated"], "language": ["ko"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|snli"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "semantic-similarity-scoring", "text-scoring"], "pretty_name": "KorNlu", "dataset_info": [{"config_name": "nli", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 80135707, "num_examples": 550146}, {"name": "validation", "num_bytes": 318170, "num_examples": 1570}, {"name": "test", "num_bytes": 1047250, "num_examples": 4954}], "download_size": 80030037, "dataset_size": 81501127}, {"config_name": "sts", "features": [{"name": "genre", "dtype": {"class_label": {"names": {"0": "main-news", "1": "main-captions", "2": "main-forum", "3": "main-forums"}}}}, {"name": "filename", "dtype": {"class_label": {"names": {"0": "images", "1": "MSRpar", "2": "MSRvid", "3": "headlines", "4": "deft-forum", "5": "deft-news", "6": "track5.en-en", "7": "answers-forums", "8": "answer-answer"}}}}, {"name": "year", "dtype": {"class_label": {"names": {"0": "2017", "1": "2016", "2": "2013", "3": "2012train", "4": "2014", "5": "2015", "6": "2012test"}}}}, {"name": "id", "dtype": "int32"}, {"name": "score", "dtype": "float32"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1056664, "num_examples": 5703}, {"name": "validation", "num_bytes": 305009, "num_examples": 1471}, {"name": "test", "num_bytes": 249671, "num_examples": 1379}], "download_size": 1603824, "dataset_size": 1611344}]}

2024-01-18T11:07:43+00:00

TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-semantic-similarity-scoring #task_ids-text-scoring #annotations_creators-found #language_creators-expert-generated #language_creators-found #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|snli #language-Korean #license-cc-by-sa-4.0 #arxiv-2004.03289 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: Arxiv - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @sumanthd17 for adding this dataset.

fec9d365296c55c2ec8a94a570fd0b23fd965857

# 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:** [Github](https://github.com/songys/Question_pair) - **Repository:** [Github](https://github.com/songys/Question_pair) - **Paper:** - **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 Each row in the dataset contains two questions and a `is_duplicate` label. - `question1`: The first question - `question2`: The second question - `is_duplicate`: 0 if `question1` and `question2` are semantically similar; 1 otherwise ### 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 [@jaketae](https://github.com/jaketae) for adding this dataset.

kor_qpair

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["ko"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "pretty_name": "KorQpair", "dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "is_duplicate", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 515365, "num_examples": 6136}, {"name": "test", "num_bytes": 63466, "num_examples": 758}, {"name": "validation", "num_bytes": 57242, "num_examples": 682}], "download_size": 545236, "dataset_size": 636073}}

2024-01-18T11:07:45+00:00

TAGS #task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Korean #license-mit #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields Each row in the dataset contains two questions and a 'is_duplicate' label. - 'question1': The first question - 'question2': The second question - 'is_duplicate': 0 if 'question1' and 'question2' are semantically similar; 1 otherwise ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jaketae for adding this dataset.

4fc17d08cd0c43ecd0d715044151adbd4eac37ea

# Dataset Card for Structured Argument Extraction for Korean ## 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:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k) - **Repository:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k) - **Paper:** [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://arxiv.org/abs/1912.00342) - **Point of Contact:** [Won Ik Cho]([email protected]) ### Dataset Summary The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech. ### Supported Tasks and Leaderboards * `intent_classification`: The dataset can be trained with a Transformer like [BERT](https://huggingface.co/bert-base-uncased) to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`. ## Dataset Structure ### Data Instances An example data instance contains a question or command pair and its label: ``` { "intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘" "intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기" "label": 4 } ``` ### Data Fields * `intent_pair1`: a question/command pair * `intent_pair2`: a corresponding question/command pair * `label`: determines the intent argument of the pair and can be one of `yes/no` (0), `alternative` (1), `wh- questions` (2), `prohibitions` (3), `requirements` (4) and `strong requirements` (5) ### Data Splits The corpus contains 30,837 examples. ## Dataset Creation ### Curation Rationale The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the `Who, what, where, when and why` usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance. ### Source Data #### Initial Data Collection and Normalization The corpus was taken from the one constructed by [Cho et al.](https://arxiv.org/abs/1811.04231), a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives. #### Who are the source language producers? Korean speakers are the source language producers. ### Annotations #### Annotation process Utterances were categorized as question or command arguments and then further classified according to their intent argument. #### Who are the annotators? The annotation was done by three Korean natives with a background in computational linguistics. ### 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 is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim. ### Licensing Information The dataset is licensed under the CC BY-SA-4.0. ### Citation Information ``` @article{cho2019machines, title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives}, author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo}, journal={arXiv preprint arXiv:1912.00342}, year={2019} } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.

kor_sae

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["ko"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification"], "pretty_name": "Structured Argument Extraction for Korean", "dataset_info": {"features": [{"name": "intent_pair1", "dtype": "string"}, {"name": "intent_pair2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "yes/no", "1": "alternative", "2": "wh- questions", "3": "prohibitions", "4": "requirements", "5": "strong requirements"}}}}], "splits": [{"name": "train", "num_bytes": 2885167, "num_examples": 30837}], "download_size": 2545926, "dataset_size": 2885167}}

2024-01-18T11:07:47+00:00

TAGS #task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Korean #license-cc-by-sa-4.0 #arxiv-1912.00342 #arxiv-1811.04231 #region-us

# Dataset Card for Structured Argument Extraction for Korean ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Structured Argument Extraction for Korean - Repository: Structured Argument Extraction for Korean - Paper: Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives - Point of Contact: Won Ik Cho ### Dataset Summary The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech. ### Supported Tasks and Leaderboards * 'intent_classification': The dataset can be trained with a Transformer like BERT to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is 'ko-KR'. ## Dataset Structure ### Data Instances An example data instance contains a question or command pair and its label: ### Data Fields * 'intent_pair1': a question/command pair * 'intent_pair2': a corresponding question/command pair * 'label': determines the intent argument of the pair and can be one of 'yes/no' (0), 'alternative' (1), 'wh- questions' (2), 'prohibitions' (3), 'requirements' (4) and 'strong requirements' (5) ### Data Splits The corpus contains 30,837 examples. ## Dataset Creation ### Curation Rationale The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the 'Who, what, where, when and why' usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance. ### Source Data #### Initial Data Collection and Normalization The corpus was taken from the one constructed by Cho et al., a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives. #### Who are the source language producers? Korean speakers are the source language producers. ### Annotations #### Annotation process Utterances were categorized as question or command arguments and then further classified according to their intent argument. #### Who are the annotators? The annotation was done by three Korean natives with a background in computational linguistics. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators The dataset is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim. ### Licensing Information The dataset is licensed under the CC BY-SA-4.0. ### Contributions Thanks to @stevhliu for adding this dataset.

8079d24b9f1278c6fbc992921c1271457a1064ff

# Dataset Card for Korean Sarcasm Detection ## 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:** [Korean Sarcasm Detection](https://github.com/SpellOnYou/korean-sarcasm) - **Repository:** [Korean Sarcasm Detection](https://github.com/SpellOnYou/korean-sarcasm) - **Point of Contact:** [Dionne Kim]([email protected]) ### Dataset Summary The Korean Sarcasm Dataset was created to detect sarcasm in text, which can significantly alter the original meaning of a sentence. 9319 tweets were collected from Twitter and labeled for `sarcasm` or `not_sarcasm`. These tweets were gathered by querying for: `역설, 아무말, 운수좋은날, 笑, 뭐래 아닙니다, 그럴리없다, 어그로, irony sarcastic, and sarcasm`. The dataset was pre-processed by removing the keyword hashtag, urls and mentions of the user to maintain anonymity. ### Supported Tasks and Leaderboards * `sarcasm_detection`: The dataset can be used to train a model to detect sarcastic tweets. A [BERT](https://huggingface.co/bert-base-uncased) model can be presented with a tweet in Korean and be asked to determine whether it is sarcastic or not. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`. ## Dataset Structure ### Data Instances An example data instance contains a Korean tweet and a label whether it is sarcastic or not. `1` maps to sarcasm and `0` maps to no sarcasm. ``` { "tokens": "[ 수도권 노선 아이템 ] 17 . 신분당선의 #딸기 : 그의 이미지 컬러 혹은 머리 색에서 유래한 아이템이다 . #메트로라이프" "label": 0 } ``` ### Data Fields * `tokens`: contains the text of the tweet * `label`: determines whether the text is sarcastic (`1`: sarcasm, `0`: no sarcasm) ### Data Splits The data is split into a training set comrpised of 9018 tweets and a test set of 301 tweets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The dataset was created by gathering HTML data from Twitter. Queries for hashtags that include sarcasm and variants of it were used to return tweets. It was preprocessed by removing the keyword hashtag, urls and mentions of the user to preserve anonymity. #### Who are the source language producers? The source language producers are Korean Twitter users. ### Annotations #### Annotation process Tweets were labeled `1` for sarcasm and `0` for no sarcasm. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information Mentions of the user in a tweet were removed to keep them anonymous. ## 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 curated by Dionne Kim. ### Licensing Information This dataset is licensed under the MIT License. ### Citation Information ``` @misc{kim2019kocasm, author = {Kim, Jiwon and Cho, Won Ik}, title = {Kocasm: Korean Automatic Sarcasm Detection}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/SpellOnYou/korean-sarcasm}} } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.

kor_sarcasm

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ko"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "Korean Sarcasm Detection", "tags": ["sarcasm-detection"], "dataset_info": {"features": [{"name": "tokens", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "no_sarcasm", "1": "sarcasm"}}}}], "splits": [{"name": "train", "num_bytes": 1012030, "num_examples": 9000}, {"name": "test", "num_bytes": 32480, "num_examples": 301}], "download_size": 1008955, "dataset_size": 1044510}}

2024-01-18T11:07:49+00:00

TAGS #task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Korean #license-mit #sarcasm-detection #region-us

# Dataset Card for Korean Sarcasm Detection ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Korean Sarcasm Detection - Repository: Korean Sarcasm Detection - Point of Contact: Dionne Kim ### Dataset Summary The Korean Sarcasm Dataset was created to detect sarcasm in text, which can significantly alter the original meaning of a sentence. 9319 tweets were collected from Twitter and labeled for 'sarcasm' or 'not_sarcasm'. These tweets were gathered by querying for: '역설, 아무말, 운수좋은날, 笑, 뭐래 아닙니다, 그럴리없다, 어그로, irony sarcastic, and sarcasm'. The dataset was pre-processed by removing the keyword hashtag, urls and mentions of the user to maintain anonymity. ### Supported Tasks and Leaderboards * 'sarcasm_detection': The dataset can be used to train a model to detect sarcastic tweets. A BERT model can be presented with a tweet in Korean and be asked to determine whether it is sarcastic or not. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is 'ko-KR'. ## Dataset Structure ### Data Instances An example data instance contains a Korean tweet and a label whether it is sarcastic or not. '1' maps to sarcasm and '0' maps to no sarcasm. ### Data Fields * 'tokens': contains the text of the tweet * 'label': determines whether the text is sarcastic ('1': sarcasm, '0': no sarcasm) ### Data Splits The data is split into a training set comrpised of 9018 tweets and a test set of 301 tweets. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The dataset was created by gathering HTML data from Twitter. Queries for hashtags that include sarcasm and variants of it were used to return tweets. It was preprocessed by removing the keyword hashtag, urls and mentions of the user to preserve anonymity. #### Who are the source language producers? The source language producers are Korean Twitter users. ### Annotations #### Annotation process Tweets were labeled '1' for sarcasm and '0' for no sarcasm. #### Who are the annotators? ### Personal and Sensitive Information Mentions of the user in a tweet were removed to keep them anonymous. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators This dataset was curated by Dionne Kim. ### Licensing Information This dataset is licensed under the MIT License. ### Contributions Thanks to @stevhliu for adding this dataset.

ab88c38c9fbba78822bfcc5ad02cf0ec4511b6bd

# Dataset Card for LABR ## Table of Contents - [Dataset Card for LABR](#dataset-card-for-labr) - [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:** [LABR](https://github.com/mohamedadaly/LABR) - **Paper:** [LABR: Large-scale Arabic Book Reviews Dataset](https://aclanthology.org/P13-2088/) - **Point of Contact:** [Mohammed Aly](mailto:[email protected]) ### Dataset Summary This dataset contains over 63,000 book reviews in Arabic. It is the largest sentiment analysis dataset for Arabic to-date. The book reviews were harvested from the website Goodreads during the month or March 2013. Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the text of the review. ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://www.aclweb.org/anthology/P13-2088.pdf). ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A typical data point comprises a rating from 1 to 5 where the higher the rating the better the review. ### Data Fields - `text` (str): Review text. - `label` (int): Review rating. ### Data Splits The data is split into a training and testing. The split is organized as the following | | train | test | |---------- |-------:|------:| |data split | 11,760 | 2,935 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization downloaded over 220,000 reviews from the book readers social network www.goodreads.com during the month of March 2013 #### Who are the source language producers? Reviews. ### 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{aly2013labr, title={Labr: A large scale arabic book reviews dataset}, author={Aly, Mohamed and Atiya, Amir}, booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, pages={494--498}, year={2013} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset.

labr

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "labr", "pretty_name": "LABR", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5"}}}}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 7051103, "num_examples": 11760}, {"name": "test", "num_bytes": 1703399, "num_examples": 2935}], "download_size": 39953712, "dataset_size": 8754502}}

2024-01-18T11:07:51+00:00

TAGS #task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Arabic #license-unknown #region-us

Dataset Card for LABR ===================== Table of Contents ----------------- * Dataset Card for LABR + Table of Contents + Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages + Dataset Structure - Data Instances - Data Fields - Data Splits + |split|num examples| + Dataset Creation - Curation Rationale - Source Data * Initial Data Collection and Normalization * Who are the source language producers? - Annotations * Annotation process * Who are the annotators? - Personal and Sensitive Information + Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations + Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions Dataset Description ------------------- * Repository: LABR * Paper: LABR: Large-scale Arabic Book Reviews Dataset * Point of Contact: Mohammed Aly ### Dataset Summary This dataset contains over 63,000 book reviews in Arabic. It is the largest sentiment analysis dataset for Arabic to-date. The book reviews were harvested from the website Goodreads during the month or March 2013. Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the text of the review. ### Supported Tasks and Leaderboards The dataset was published on this paper. ### Languages The dataset is based on Arabic. Dataset Structure ----------------- ### Data Instances A typical data point comprises a rating from 1 to 5 where the higher the rating the better the review. ### Data Fields * 'text' (str): Review text. * 'label' (int): Review rating. ### Data Splits The data is split into a training and testing. The split is organized as the following Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization downloaded over 220,000 reviews from the book readers social network URL during the month of March 2013 #### Who are the source language producers? Reviews. ### Annotations The dataset does not contain any additional annotations. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @zaidalyafeai for adding this dataset.

7e2dd65ba9ddd6c5cc2ff60a17994d046f81f73a

# Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models. ## 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/facebookresearch/LAMA - **Repository:** https://github.com/facebookresearch/LAMA - **Paper:** @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Dataset Summary This dataset provides the data for LAMA. The dataset include a subset of Google_RE (https://code.google.com/archive/p/relation-extraction-corpus/), TRex (subset of wikidata triples), Conceptnet (https://github.com/commonsense/conceptnet5/wiki) and Squad. There are configs for each of "google_re", "trex", "conceptnet" and "squad", respectively. The dataset includes some cleanup, and addition of a masked sentence and associated answers for the [MASK] token. The accuracy in predicting the [MASK] token shows how well the language model knows facts and common sense information. The [MASK] tokens are only for the "object" slots. This version of the dataset includes "negated" sentences as well as the masked sentence. Also, certain of the config includes "template" and "template_negated" fields of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively of certain relations. See the paper for more details. For more information, also see: https://github.com/facebookresearch/LAMA ### Languages en ## Dataset Structure ### Data Instances The trex config has the following fields: `` {'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'} `` The conceptnet config has the following fields: `` {'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'} `` The squad config has the following fields: `` {'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'} `` The google_re config has the following fields: `` {'evidences': '[{\'url\': \'http://en.wikipedia.org/wiki/Peter_F._Martin\', \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'} `` ### Data Fields The trex config has the following fields: * uuid: the id * obj_uri: a uri for the object slot * obj_label: a label for the object slot * sub_uri: a uri for the subject slot * sub_label: a label for the subject slot * predicate_id: the predicate/relationship * sub_surface: the surface text for the subject * obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token. * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string. * template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings. * label: the label for the relationship/predicate. label may be missing and replaced with an empty string. * description': a description of the relationship/predicate. description may be missing and replaced with an empty string. * type: a type id for the relationship/predicate. type may be missing and replaced with an empty string. The conceptnet config has the following fields: * uuid: the id * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object to be predicted. obj may be missing and replaced with an empty string. * pred: the predicate/relationship * obj_label: the object label * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The squad config has the following fields: * id: the id * sub_label: the subject label * obj_label: the object label that is being predicted * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The google_re config has the following fields: * uuid: the id * pred: the predicate * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object. obj may be missing and replaced with an empty string. * evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information. * judgments: data about judgments * sub_q: unknown * sub_label: label for the subject * sub_aliases: unknown * obj_w: unknown * obj_label: label for the object * obj_aliases: unknown * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. * template_negated: Same as above, except the [Y] is not the object. ### Data Splits There are no data splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created to probe what language models understand. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created gathered from various other datasets with cleanups for probing. #### Who are the source language producers? The LAMA authors and the original authors of the various configs. ### Annotations #### Annotation process Human annotations under the original datasets (conceptnet), and various machine annotations. #### Who are the annotators? Human annotations and machine annotations. ### Personal and Sensitive Information Unkown, but likely names of famous people. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to probe the understanding of language models. ### Discussion of Biases Since the data is from human annotators, there is likely to be baises. [More Information Needed] ### Other Known Limitations The original documentation for the datafields are limited. ## Additional Information ### Dataset Curators The authors of LAMA at Facebook and the authors of the original datasets. ### Licensing Information The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE ### Citation Information @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.

lama

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K", "1K<n<10K", "1M<n<10M", "n<1K"], "source_datasets": ["extended|conceptnet5", "extended|squad"], "task_categories": ["text-retrieval", "text-classification"], "task_ids": ["fact-checking-retrieval", "text-scoring"], "paperswithcode_id": "lama", "pretty_name": "LAMA: LAnguage Model Analysis", "config_names": ["conceptnet", "google_re", "squad", "trex"], "tags": ["probing"], "dataset_info": [{"config_name": "trex", "features": [{"name": "uuid", "dtype": "string"}, {"name": "obj_uri", "dtype": "string"}, {"name": "obj_label", "dtype": "string"}, {"name": "sub_uri", "dtype": "string"}, {"name": "sub_label", "dtype": "string"}, {"name": "predicate_id", "dtype": "string"}, {"name": "sub_surface", "dtype": "string"}, {"name": "obj_surface", "dtype": "string"}, {"name": "masked_sentence", "dtype": "string"}, {"name": "template", "dtype": "string"}, {"name": "template_negated", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 656913189, "num_examples": 1304391}], "download_size": 74652201, "dataset_size": 656913189}, {"config_name": "squad", "features": [{"name": "id", "dtype": "string"}, {"name": "sub_label", "dtype": "string"}, {"name": "obj_label", "dtype": "string"}, {"name": "negated", "dtype": "string"}, {"name": "masked_sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57188, "num_examples": 305}], "download_size": 74639115, "dataset_size": 57188}, {"config_name": "google_re", "features": [{"name": "pred", "dtype": "string"}, {"name": "sub", "dtype": "string"}, {"name": "obj", "dtype": "string"}, {"name": "evidences", "dtype": "string"}, {"name": "judgments", "dtype": "string"}, {"name": "sub_w", "dtype": "string"}, {"name": "sub_label", "dtype": "string"}, {"name": "sub_aliases", "dtype": "string"}, {"name": "obj_w", "dtype": "string"}, {"name": "obj_label", "dtype": "string"}, {"name": "obj_aliases", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "masked_sentence", "dtype": "string"}, {"name": "template", "dtype": "string"}, {"name": "template_negated", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7638657, "num_examples": 6106}], "download_size": 74639115, "dataset_size": 7638657}, {"config_name": "conceptnet", "features": [{"name": "uuid", "dtype": "string"}, {"name": "sub", "dtype": "string"}, {"name": "obj", "dtype": "string"}, {"name": "pred", "dtype": "string"}, {"name": "obj_label", "dtype": "string"}, {"name": "masked_sentence", "dtype": "string"}, {"name": "negated", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4130000, "num_examples": 29774}], "download_size": 74639115, "dataset_size": 4130000}]}

2024-01-18T11:07:52+00:00

TAGS #task_categories-text-retrieval #task_categories-text-classification #task_ids-fact-checking-retrieval #task_ids-text-scoring #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #size_categories-n<1K #source_datasets-extended|conceptnet5 #source_datasets-extended|squad #language-English #license-cc-by-4.0 #probing #region-us

# Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models. ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={URL } ### Dataset Summary This dataset provides the data for LAMA. The dataset include a subset of Google_RE (URL TRex (subset of wikidata triples), Conceptnet (URL and Squad. There are configs for each of "google_re", "trex", "conceptnet" and "squad", respectively. The dataset includes some cleanup, and addition of a masked sentence and associated answers for the [MASK] token. The accuracy in predicting the [MASK] token shows how well the language model knows facts and common sense information. The [MASK] tokens are only for the "object" slots. This version of the dataset includes "negated" sentences as well as the masked sentence. Also, certain of the config includes "template" and "template_negated" fields of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively of certain relations. See the paper for more details. For more information, also see: URL ### Languages en ## Dataset Structure ### Data Instances The trex config has the following fields: '' {'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'} '' The conceptnet config has the following fields: '' {'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'} '' The squad config has the following fields: '' {'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'} '' The google_re config has the following fields: '' {'evidences': '[{\'url\': \'URL \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'} '' ### Data Fields The trex config has the following fields: * uuid: the id * obj_uri: a uri for the object slot * obj_label: a label for the object slot * sub_uri: a uri for the subject slot * sub_label: a label for the subject slot * predicate_id: the predicate/relationship * sub_surface: the surface text for the subject * obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token. * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string. * template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings. * label: the label for the relationship/predicate. label may be missing and replaced with an empty string. * description': a description of the relationship/predicate. description may be missing and replaced with an empty string. * type: a type id for the relationship/predicate. type may be missing and replaced with an empty string. The conceptnet config has the following fields: * uuid: the id * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object to be predicted. obj may be missing and replaced with an empty string. * pred: the predicate/relationship * obj_label: the object label * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The squad config has the following fields: * id: the id * sub_label: the subject label * obj_label: the object label that is being predicted * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The google_re config has the following fields: * uuid: the id * pred: the predicate * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object. obj may be missing and replaced with an empty string. * evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information. * judgments: data about judgments * sub_q: unknown * sub_label: label for the subject * sub_aliases: unknown * obj_w: unknown * obj_label: label for the object * obj_aliases: unknown * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. * template_negated: Same as above, except the [Y] is not the object. ### Data Splits There are no data splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created to probe what language models understand. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created gathered from various other datasets with cleanups for probing. #### Who are the source language producers? The LAMA authors and the original authors of the various configs. ### Annotations #### Annotation process Human annotations under the original datasets (conceptnet), and various machine annotations. #### Who are the annotators? Human annotations and machine annotations. ### Personal and Sensitive Information Unkown, but likely names of famous people. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to probe the understanding of language models. ### Discussion of Biases Since the data is from human annotators, there is likely to be baises. ### Other Known Limitations The original documentation for the datafields are limited. ## Additional Information ### Dataset Curators The authors of LAMA at Facebook and the authors of the original datasets. ### Licensing Information The Creative Commons Attribution-Noncommercial 4.0 International License. see URL @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={URL } ### Contributions Thanks to @ontocord for adding this dataset.

5953bd97664b64b95754f299b2309ecfbfbe81b9

# Dataset Card for LAMBADA ## 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:** [LAMBADA homepage](https://zenodo.org/record/2630551#.X8UP76pKiIa) - **Paper:** [The LAMBADA dataset: Word prediction requiring a broad discourse context∗](https://www.aclweb.org/anthology/P16-1144.pdf) - **Data:** https://doi.org/10.5281/zenodo.2630551 ### Dataset Summary The LAMBADA evaluates the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. The LAMBADA dataset is extracted from BookCorpus and consists of 10'022 passages, divided into 4'869 development and 5'153 test passages. The training data for language models to be tested on LAMBADA include the full text of 2'662 novels (disjoint from those in dev+test), comprising 203 million words. ### Supported Tasks and Leaderboards Long range dependency evaluated as (last) word prediction ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A data point is a text sequence (passage) including the context, the target sentence (the last one) and the target word. For each passage in the dev and the test splits, the word to be guessed is the last one. The training data include the full text of 2'662 novels (disjoint from those in dev+test), comprising more than 200M words. It consists of text from the same domain as the dev+test passages, but not filtered in any way. Each training instance has a `category` field indicating which sub-category the book was extracted from. This field is not given for the dev and test splits. An example looks like this: ``` {"category": "Mystery", "text": "bob could have been called in at this point , but he was n't miffed at his exclusion at all . he was relieved at not being brought into this initial discussion with central command . `` let 's go make some grub , '' said bob as he turned to danny . danny did n't keep his stoic expression , but with a look of irritation got up and left the room with bob", } ``` ### Data Fields - `category`: the sub-category of books from which the book was extracted from. Only available for the training split. - `text`: the text (concatenation of context, target sentence and target word). The word to be guessed is the last one. ### Data Splits - train: 2'662 novels - dev: 4'869 passages - test: 5'153 passages ## Dataset Creation ### Curation Rationale The dataset aims at evaluating the ability of language models to hold long-term contextual memories. Instances are extracted from books because they display long-term dependencies. In particular, the data are curated such that the target words are easy to guess by human subjects when they can look at the whole passage they come from, but nearly impossible if only the last sentence is considered. ### Source Data #### Initial Data Collection and Normalization The corpus was duplicated and potentially offensive material were filtered out with a stop word list. #### Who are the source language producers? The passages are extracted from novels from [Book Corpus](https://github.com/huggingface/datasets/tree/master/datasets/bookcorpus). ### Annotations #### Annotation process The authors required two consecutive subjects (paid crowdsourcers) to exactly match the missing word based on the whole passage (comprising the context and the target sentence), and made sure that no subject (out of ten) was able to provide it based on local context only, even when given 3 guesses. #### Who are the annotators? The text is self-annotated but was curated by asking (paid) crowdsourcers to guess the last word. ### 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 The dataset is released under the [CC BY 4.0](Creative Commons Attribution 4.0 International) license. ### Citation Information ``` @InProceedings{paperno-EtAl:2016:P16-1, author = {Paperno, Denis and Kruszewski, Germ\'{a}n and Lazaridou, Angeliki and Pham, Ngoc Quan and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernandez, Raquel}, title = {The {LAMBADA} dataset: Word prediction requiring a broad discourse context}, booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {August}, year = {2016}, address = {Berlin, Germany}, publisher = {Association for Computational Linguistics}, pages = {1525--1534}, url = {http://www.aclweb.org/anthology/P16-1144} } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

lambada

2022-03-02T23:29:22+00:00

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2024-01-04T14:16:25+00:00

TAGS #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|bookcorpus #language-English #license-cc-by-4.0 #long-range-dependency #region-us

# Dataset Card for LAMBADA ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: LAMBADA homepage - Paper: The LAMBADA dataset: Word prediction requiring a broad discourse context∗ - Data: URL ### Dataset Summary The LAMBADA evaluates the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. The LAMBADA dataset is extracted from BookCorpus and consists of 10'022 passages, divided into 4'869 development and 5'153 test passages. The training data for language models to be tested on LAMBADA include the full text of 2'662 novels (disjoint from those in dev+test), comprising 203 million words. ### Supported Tasks and Leaderboards Long range dependency evaluated as (last) word prediction ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. ## Dataset Structure ### Data Instances A data point is a text sequence (passage) including the context, the target sentence (the last one) and the target word. For each passage in the dev and the test splits, the word to be guessed is the last one. The training data include the full text of 2'662 novels (disjoint from those in dev+test), comprising more than 200M words. It consists of text from the same domain as the dev+test passages, but not filtered in any way. Each training instance has a 'category' field indicating which sub-category the book was extracted from. This field is not given for the dev and test splits. An example looks like this: ### Data Fields - 'category': the sub-category of books from which the book was extracted from. Only available for the training split. - 'text': the text (concatenation of context, target sentence and target word). The word to be guessed is the last one. ### Data Splits - train: 2'662 novels - dev: 4'869 passages - test: 5'153 passages ## Dataset Creation ### Curation Rationale The dataset aims at evaluating the ability of language models to hold long-term contextual memories. Instances are extracted from books because they display long-term dependencies. In particular, the data are curated such that the target words are easy to guess by human subjects when they can look at the whole passage they come from, but nearly impossible if only the last sentence is considered. ### Source Data #### Initial Data Collection and Normalization The corpus was duplicated and potentially offensive material were filtered out with a stop word list. #### Who are the source language producers? The passages are extracted from novels from Book Corpus. ### Annotations #### Annotation process The authors required two consecutive subjects (paid crowdsourcers) to exactly match the missing word based on the whole passage (comprising the context and the target sentence), and made sure that no subject (out of ten) was able to provide it based on local context only, even when given 3 guesses. #### Who are the annotators? The text is self-annotated but was curated by asking (paid) crowdsourcers to guess the last word. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Contributions Thanks to @VictorSanh for adding this dataset.

b140e17cb4a8f2721083f7e6517c24441e67662a

# Dataset Card for The Large Spanish 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/josecannete/spanish-corpora](https://github.com/josecannete/spanish-corpora) - **Repository:** [https://github.com/josecannete/spanish-corpora](https://github.com/josecannete/spanish-corpora) - **Paper:** - **Data:** https://doi.org/10.5281/zenodo.3247731 - **Leaderboard:** - **Point of Contact:** [José Cañete](mailto:[email protected]) (corpus creator) or [Lewis Tunstall](mailto:[email protected]) (corpus submitter) ### Dataset Summary The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament notes. Each config contains the data corresponding to a different corpus. For example, `all_wiki` only includes examples from Spanish Wikipedia: ```python from datasets import load_dataset all_wiki = load_dataset('large_spanish_corpus', name='all_wiki') ``` By default, the config is set to "combined" which loads all the corpora. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Spanish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits The following is taken from the corpus' source repsository: * Spanish Wikis: Which include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (https://github.com/josecannete/wikiextractorforBERT) using the wikis dump of 20/04/2019. * ParaCrawl: Spanish portion of ParaCrawl (http://opus.nlpl.eu/ParaCrawl.php) * EUBookshop: Spanish portion of EUBookshop (http://opus.nlpl.eu/EUbookshop.php) * MultiUN: Spanish portion of MultiUN (http://opus.nlpl.eu/MultiUN.php) * OpenSubtitles: Spanish portion of OpenSubtitles2018 (http://opus.nlpl.eu/OpenSubtitles-v2018.php) * DGC: Spanish portion of DGT (http://opus.nlpl.eu/DGT.php) * DOGC: Spanish portion of DOGC (http://opus.nlpl.eu/DOGC.php) * ECB: Spanish portion of ECB (http://opus.nlpl.eu/ECB.php) * EMEA: Spanish portion of EMEA (http://opus.nlpl.eu/EMEA.php) * Europarl: Spanish portion of Europarl (http://opus.nlpl.eu/Europarl.php) * GlobalVoices: Spanish portion of GlobalVoices (http://opus.nlpl.eu/GlobalVoices.php) * JRC: Spanish portion of JRC (http://opus.nlpl.eu/JRC-Acquis.php) * News-Commentary11: Spanish portion of NCv11 (http://opus.nlpl.eu/News-Commentary-v11.php) * TED: Spanish portion of TED (http://opus.nlpl.eu/TED2013.php) * UN: Spanish portion of UN (http://opus.nlpl.eu/UN.php) ## 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.

large_spanish_corpus

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["es"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "100M<n<1B", "10K<n<100K", "10M<n<100M", "1M<n<10M"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "pretty_name": "The Large Spanish Corpus", "config_names": ["DGT", "DOGC", "ECB", "EMEA", "EUBookShop", "Europarl", "GlobalVoices", "JRC", "NewsCommentary11", "OpenSubtitles2018", "ParaCrawl", "TED", "UN", "all_wikis", "combined", "multiUN"], "tags": [], "dataset_info": [{"config_name": "JRC", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 380895504, "num_examples": 3410620}], "download_size": 4099166669, "dataset_size": 380895504}, {"config_name": "EMEA", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 100259598, "num_examples": 1221233}], "download_size": 4099166669, "dataset_size": 100259598}, {"config_name": "GlobalVoices", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 114435784, "num_examples": 897075}], "download_size": 4099166669, "dataset_size": 114435784}, {"config_name": "ECB", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 336285757, "num_examples": 1875738}], "download_size": 4099166669, "dataset_size": 336285757}, {"config_name": "DOGC", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 898279656, "num_examples": 10917053}], "download_size": 4099166669, "dataset_size": 898279656}, {"config_name": "all_wikis", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3782280549, "num_examples": 28109484}], "download_size": 4099166669, "dataset_size": 3782280549}, {"config_name": "TED", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15858148, "num_examples": 157910}], "download_size": 4099166669, "dataset_size": 15858148}, {"config_name": "multiUN", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2327269369, "num_examples": 13127490}], "download_size": 4099166669, "dataset_size": 2327269369}, {"config_name": "Europarl", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 359897865, "num_examples": 2174141}], "download_size": 4099166669, "dataset_size": 359897865}, {"config_name": "NewsCommentary11", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48350573, "num_examples": 288771}], "download_size": 4099166669, "dataset_size": 48350573}, {"config_name": "UN", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23654590, "num_examples": 74067}], "download_size": 4099166669, "dataset_size": 23654590}, {"config_name": "EUBookShop", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1326861077, "num_examples": 8214959}], "download_size": 4099166669, "dataset_size": 1326861077}, {"config_name": "ParaCrawl", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1840430234, "num_examples": 15510649}], "download_size": 4099166669, "dataset_size": 1840430234}, {"config_name": "OpenSubtitles2018", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7477281776, "num_examples": 213508602}], "download_size": 4099166669, "dataset_size": 7477281776}, {"config_name": "DGT", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 396217351, "num_examples": 3168368}], "download_size": 4099166669, "dataset_size": 396217351}, {"config_name": "combined", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19428257807, "num_examples": 302656160}], "download_size": 4099166669, "dataset_size": 19428257807}]}

2023-06-07T20:20:55+00:00

TAGS #task_categories-other #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-100M<n<1B #size_categories-10K<n<100K #size_categories-10M<n<100M #size_categories-1M<n<10M #source_datasets-original #language-Spanish #license-mit #region-us

# Dataset Card for The Large Spanish Corpus ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: - Data: URL - Leaderboard: - Point of Contact: José Cañete (corpus creator) or Lewis Tunstall (corpus submitter) ### Dataset Summary The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament notes. Each config contains the data corresponding to a different corpus. For example, 'all_wiki' only includes examples from Spanish Wikipedia: By default, the config is set to "combined" which loads all the corpora. ### Supported Tasks and Leaderboards ### Languages Spanish ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits The following is taken from the corpus' source repsository: * Spanish Wikis: Which include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (URL using the wikis dump of 20/04/2019. * ParaCrawl: Spanish portion of ParaCrawl (URL * EUBookshop: Spanish portion of EUBookshop (URL * MultiUN: Spanish portion of MultiUN (URL * OpenSubtitles: Spanish portion of OpenSubtitles2018 (URL * DGC: Spanish portion of DGT (URL * DOGC: Spanish portion of DOGC (URL * ECB: Spanish portion of ECB (URL * EMEA: Spanish portion of EMEA (URL * Europarl: Spanish portion of Europarl (URL * GlobalVoices: Spanish portion of GlobalVoices (URL * JRC: Spanish portion of JRC (URL * News-Commentary11: Spanish portion of NCv11 (URL * TED: Spanish portion of TED (URL * UN: Spanish portion of UN (URL ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @lewtun for adding this dataset.

358bcc95aeddd5d07a4524ee416f03d993099b23

# Dataset Card for LaRoSeDa ## 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/ancatache/LaRoSeDa) - **Repository:** [Github](https://github.com/ancatache/LaRoSeDa) - **Paper:** [Arxiv](https://arxiv.org/pdf/2101.04197.pdf) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [email protected] ### Dataset Summary LaRoSeDa - A **La**rge and **Ro**manian **Se**ntiment **Da**ta Set. LaRoSeDa contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative. The samples have one of four star ratings: 1 or 2 - for reviews that can be considered of negative polarity, and 4 or 5 for the positive ones. The 15,000 samples featured in the corpus and labelled with the star rating, are splitted in a train and test subsets, with 12,000 and 3,000 samples in each subset. ### Supported Tasks and Leaderboards [LiRo Benchmark and Leaderboard](https://eemlcommunity.github.io/ro_benchmark_leaderboard/site/) ### Languages The text dataset is in Romanian (`ro`). ## Dataset Structure ### Data Instances Below we have an example of sample from LaRoSeDa: ``` { "index": "9675", "title": "Nu recomand", "content": "probleme cu localizarea, mari...", "starRating": 1, } ``` where "9675" is the sample index, followed by the title of the review, review content and then the star rating given by the user. ### Data Fields - `index`: string, the unique indentifier of a sample. - `title`: string, the review title. - `content`: string, the content of the review. - `starRating`: integer, with values in the following set {1, 2, 4, 5}. ### Data Splits The train/test split contains 12,000/3,000 samples tagged with the star rating assigned to each sample in the dataset. ## Dataset Creation ### Curation Rationale The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics. For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the [paper](https://arxiv.org/abs/1901.06543). ### Source Data #### Data Collection and Normalization For the data collection, one of the largest Romanian e-commerce platform was targetted. Along with the textual content of each review, the associated star ratings was also collected in order to automatically assign labels to the collected text samples. #### Who are the source language producers? The original text comes from one of the largest e-commerce platforms in Romania. ### Annotations #### Annotation process As mentioned above, LaRoSeDa is composed of product reviews from one of the largest e-commerce websites in Romania. The resulting samples are automatically tagged with the star rating assigned by the users. #### Who are the annotators? N/A ### Personal and Sensitive Information The textual data collected for LaRoSeDa consists in product reviews freely available on the Internet. To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected. ## 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. In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language. ### Discussion of Biases *We note that most of the negative reviews (5,561) are rated with one star. Similarly, most of the positive reviews (6,238) are rated with five stars. Hence, the corpus is highly polarized.* ### Other Known Limitations *The star rating might not always reflect the polarity of the text. We thus acknowledge that the automatic labeling process is not optimal, i.e. some labels might be noisy.* ## Additional Information ### Dataset Curators Published and managed by Anca Tache, Mihaela Gaman and Radu Tudor Ionescu. ### Licensing Information CC BY-SA 4.0 License ### Citation Information ``` @article{ tache2101clustering, title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set}, author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu}, journal={ArXiv}, year = {2021} } ``` ### Contributions Thanks to [@MihaelaGaman](https://github.com/MihaelaGaman) for adding this dataset.

laroseda

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ro"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "LaRoSeDa", "dataset_info": {"features": [{"name": "index", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "starRating", "dtype": "int64"}], "config_name": "laroseda", "splits": [{"name": "train", "num_bytes": 2932819, "num_examples": 12000}, {"name": "test", "num_bytes": 700834, "num_examples": 3000}], "download_size": 5257183, "dataset_size": 3633653}}

2024-01-18T11:07:55+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Romanian #license-cc-by-4.0 #arxiv-2101.04197 #arxiv-1901.06543 #region-us

# Dataset Card for LaRoSeDa ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: Arxiv - Leaderboard: - Point of Contact: raducu.ionescu@URL ### Dataset Summary LaRoSeDa - A Large and Romanian Sentiment Data Set. LaRoSeDa contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative. The samples have one of four star ratings: 1 or 2 - for reviews that can be considered of negative polarity, and 4 or 5 for the positive ones. The 15,000 samples featured in the corpus and labelled with the star rating, are splitted in a train and test subsets, with 12,000 and 3,000 samples in each subset. ### Supported Tasks and Leaderboards LiRo Benchmark and Leaderboard ### Languages The text dataset is in Romanian ('ro'). ## Dataset Structure ### Data Instances Below we have an example of sample from LaRoSeDa: where "9675" is the sample index, followed by the title of the review, review content and then the star rating given by the user. ### Data Fields - 'index': string, the unique indentifier of a sample. - 'title': string, the review title. - 'content': string, the content of the review. - 'starRating': integer, with values in the following set {1, 2, 4, 5}. ### Data Splits The train/test split contains 12,000/3,000 samples tagged with the star rating assigned to each sample in the dataset. ## Dataset Creation ### Curation Rationale The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics. For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the paper. ### Source Data #### Data Collection and Normalization For the data collection, one of the largest Romanian e-commerce platform was targetted. Along with the textual content of each review, the associated star ratings was also collected in order to automatically assign labels to the collected text samples. #### Who are the source language producers? The original text comes from one of the largest e-commerce platforms in Romania. ### Annotations #### Annotation process As mentioned above, LaRoSeDa is composed of product reviews from one of the largest e-commerce websites in Romania. The resulting samples are automatically tagged with the star rating assigned by the users. #### Who are the annotators? N/A ### Personal and Sensitive Information The textual data collected for LaRoSeDa consists in product reviews freely available on the Internet. To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected. ## 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. In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language. ### Discussion of Biases *We note that most of the negative reviews (5,561) are rated with one star. Similarly, most of the positive reviews (6,238) are rated with five stars. Hence, the corpus is highly polarized.* ### Other Known Limitations *The star rating might not always reflect the polarity of the text. We thus acknowledge that the automatic labeling process is not optimal, i.e. some labels might be noisy.* ## Additional Information ### Dataset Curators Published and managed by Anca Tache, Mihaela Gaman and Radu Tudor Ionescu. ### Licensing Information CC BY-SA 4.0 License ### Contributions Thanks to @MihaelaGaman for adding this dataset.

b054a263b39b4a1cd041f85806e500ffdcb5be7f

# Dataset Card for LC-QuAD 2.0 ## 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://lc-quad.sda.tech/](http://lc-quad.sda.tech/) - **Repository:** https://github.com/AskNowQA/LC-QuAD2.0 - **Paper:** [LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia](https://api.semanticscholar.org/CorpusID:198166992) - **Point of Contact:** [Mohnish Dubey](mailto:[email protected]) or [Mohnish Dubey](mailto:[email protected]) - **Size of downloaded dataset files:** 3.87 MB - **Size of the generated dataset:** 20.73 MB - **Total amount of disk used:** 24.60 MB ### Dataset Summary LC-QuAD 2.0 is a Large Question Answering dataset with 30,000 pairs of question and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version. Please see our paper for details about the dataset creation process and framework. ### 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:** 3.87 MB - **Size of the generated dataset:** 20.73 MB - **Total amount of disk used:** 24.60 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "NNQT_question": "What is the {periodical literature} for {mouthpiece} of {Delta Air Lines}", "paraphrased_question": "What is Delta Air Line's periodical literature mouthpiece?", "question": "What periodical literature does Delta Air Lines use as a moutpiece?", "sparql_dbpedia18": "\"select distinct ?obj where { ?statement <http://www.w3.org/1999/02/22-rdf-syntax-ns#subject> <http://wikidata.dbpedia.org/resou...", "sparql_wikidata": " select distinct ?obj where { wd:Q188920 wdt:P2813 ?obj . ?obj wdt:P31 wd:Q1002697 } ", "subgraph": "simple question right", "template": " <S P ?O ; ?O instanceOf Type>", "template_index": 65, "uid": 19719 } ``` ### Data Fields The data fields are the same among all splits. #### default - `NNQT_question`: a `string` feature. - `uid`: a `int32` feature. - `subgraph`: a `string` feature. - `template_index`: a `int32` feature. - `question`: a `string` feature. - `sparql_wikidata`: a `string` feature. - `sparql_dbpedia18`: a `string` feature. - `template`: a `string` feature. - `paraphrased_question`: a `string` feature. ### Data Splits | name |train|test| |-------|----:|---:| |default|19293|4781| ## 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 LC-QuAD 2.0 is licensed under a [Creative Commons Attribution 3.0 Unported License](http://creativecommons.org/licenses/by/3.0/deed.en_US). ### Citation Information ``` @inproceedings{dubey2017lc2, title={LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia}, author={Dubey, Mohnish and Banerjee, Debayan and Abdelkawi, Abdelrahman and Lehmann, Jens}, booktitle={Proceedings of the 18th International Semantic Web Conference (ISWC)}, year={2019}, organization={Springer} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

lc_quad

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": [], "paperswithcode_id": "lc-quad-2-0", "pretty_name": "LC-QuAD 2.0: Large-scale Complex Question Answering Dataset", "tags": ["knowledge-base-qa"], "dataset_info": {"features": [{"name": "NNQT_question", "dtype": "string"}, {"name": "uid", "dtype": "int32"}, {"name": "subgraph", "dtype": "string"}, {"name": "template_index", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "sparql_wikidata", "dtype": "string"}, {"name": "sparql_dbpedia18", "dtype": "string"}, {"name": "template", "dtype": "string"}, {"name": "paraphrased_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16637751, "num_examples": 19293}, {"name": "test", "num_bytes": 4067092, "num_examples": 4781}], "download_size": 3959901, "dataset_size": 20704843}}

2024-01-18T11:07:57+00:00

TAGS #task_categories-question-answering #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-3.0 #knowledge-base-qa #region-us

Dataset Card for LC-QuAD 2.0 ============================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia * Point of Contact: Mohnish Dubey or Mohnish Dubey * Size of downloaded dataset files: 3.87 MB * Size of the generated dataset: 20.73 MB * Total amount of disk used: 24.60 MB ### Dataset Summary LC-QuAD 2.0 is a Large Question Answering dataset with 30,000 pairs of question and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version. Please see our paper for details about the dataset creation process and framework. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 3.87 MB * Size of the generated dataset: 20.73 MB * Total amount of disk used: 24.60 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'NNQT\_question': a 'string' feature. * 'uid': a 'int32' feature. * 'subgraph': a 'string' feature. * 'template\_index': a 'int32' feature. * 'question': a 'string' feature. * 'sparql\_wikidata': a 'string' feature. * 'sparql\_dbpedia18': a 'string' feature. * 'template': a 'string' feature. * 'paraphrased\_question': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information LC-QuAD 2.0 is licensed under a Creative Commons Attribution 3.0 Unported License. ### Contributions Thanks to @lewtun, @thomwolf, @patrickvonplaten for adding this dataset.

d3c3d9ca1daefef417781e2a5346f9348c4ebe84

# Dataset Card for leNER-br ## 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:** [leNER-BR homepage](https://cic.unb.br/~teodecampos/LeNER-Br/) - **Repository:** [leNER-BR repository](https://github.com/peluz/lener-br) - **Paper:** [leNER-BR: Long Form Question Answering](https://cic.unb.br/~teodecampos/LeNER-Br/luz_etal_propor2018.pdf) - **Point of Contact:** [Pedro H. Luz de Araujo](mailto:[email protected]) ### Dataset Summary LeNER-Br is a Portuguese language dataset for named entity recognition applied to legal documents. LeNER-Br consists entirely of manually annotated legislation and legal cases texts and contains tags for persons, locations, time entities, organizations, legislation and legal cases. To compose the dataset, 66 legal documents from several Brazilian Courts were collected. Courts of superior and state levels were considered, such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the dataset looks as follows: ``` { "id": "0", "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0], "tokens": [ "EMENTA", ":", "APELAÇÃO", "CÍVEL", "-", "AÇÃO", "DE", "INDENIZAÇÃO", "POR", "DANOS", "MORAIS", "-", "PRELIMINAR", "-", "ARGUIDA", "PELO", "MINISTÉRIO", "PÚBLICO", "EM", "GRAU", "RECURSAL"] } ``` ### 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: ``` "O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA" ``` 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. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | Train | Val | Test | | ------ | ----- | ---- | | 7828 | 1177 | 1390 | ## 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{luz_etal_propor2018, author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and Renato R. R. {de Oliveira} and Matheus Stauffer and Samuel Couto and Paulo Bermejo}, title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text}, booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})}, publisher = {Springer}, series = {Lecture Notes on Computer Science ({LNCS})}, pages = {313--323}, year = {2018}, month = {September 24-26}, address = {Canela, RS, Brazil}, doi = {10.1007/978-3-319-99722-3_32}, url = {https://cic.unb.br/~teodecampos/LeNER-Br/}, } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.

lener_br

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["pt"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "lener-br", "pretty_name": "leNER-br", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-ORGANIZACAO", "2": "I-ORGANIZACAO", "3": "B-PESSOA", "4": "I-PESSOA", "5": "B-TEMPO", "6": "I-TEMPO", "7": "B-LOCAL", "8": "I-LOCAL", "9": "B-LEGISLACAO", "10": "I-LEGISLACAO", "11": "B-JURISPRUDENCIA", "12": "I-JURISPRUDENCIA"}}}}], "config_name": "lener_br", "splits": [{"name": "train", "num_bytes": 3984189, "num_examples": 7828}, {"name": "validation", "num_bytes": 719433, "num_examples": 1177}, {"name": "test", "num_bytes": 823708, "num_examples": 1390}], "download_size": 2983137, "dataset_size": 5527330}, "tags": ["legal"]}

2024-01-18T11:07:59+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Portuguese #license-unknown #legal #region-us

Dataset Card for leNER-br ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: leNER-BR homepage * Repository: leNER-BR repository * Paper: leNER-BR: Long Form Question Answering * Point of Contact: Pedro H. Luz de Araujo ### Dataset Summary LeNER-Br is a Portuguese language dataset for named entity recognition applied to legal documents. LeNER-Br consists entirely of manually annotated legislation and legal cases texts and contains tags for persons, locations, time entities, organizations, legislation and legal cases. To compose the dataset, 66 legal documents from several Brazilian Courts were collected. Courts of superior and state levels were considered, such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents ### Supported Tasks and Leaderboards ### Languages The language supported is Portuguese. Dataset Structure ----------------- ### Data Instances An example from the dataset looks as follows: ### 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: 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. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: Train: 7828, Val: 1177, Test: 1390 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jonatasgrosman for adding this dataset.

c23fdff1a6bf74e0e1a71cb86f1e781d37da888c

# Dataset Card for "LexGLUE" ## 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/coastalcph/lex-glue - **Repository:** https://github.com/coastalcph/lex-glue - **Paper:** https://arxiv.org/abs/2110.00976 - **Leaderboard:** https://github.com/coastalcph/lex-glue - **Point of Contact:** [Ilias Chalkidis](mailto:[email protected]) ### Dataset Summary Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the *Legal General Language Understanding Evaluation (LexGLUE) benchmark*, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE. As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks. LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue. ### Supported Tasks and Leaderboards The supported tasks are the following: <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><td>Classes</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td><td>10+1</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td><td>10+1</td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td><td>14</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td><td>100</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td><td>100</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td><td>8+1</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td><td>n/a</td></tr> </table> #### ecthr_a The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of the ECHR that were violated (if any). #### ecthr_b The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of ECHR that were allegedly violated (considered by the court). #### scotus The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. This is a single-label multi-class classification task, where given a document (court opinion), the task is to predict the relevant issue areas. The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute). #### eurlex European Union (EU) legislation is published in EUR-Lex portal. All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office. The current version of EuroVoc contains more than 7k concepts referring to various activities of the EU and its Member States (e.g., economics, health-care, trade). Given a document, the task is to predict its EuroVoc labels (concepts). #### ledgar LEDGAR dataset aims contract provision (paragraph) classification. The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) filings, which are publicly available from EDGAR. Each label represents the single main topic (theme) of the corresponding contract provision. #### unfair_tos The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law. #### case_hold The CaseHOLD (Case Holdings on Legal Decisions) dataset includes multiple choice questions about holdings of US court cases from the Harvard Law Library case law corpus. Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. The input consists of an excerpt (or prompt) from a court decision, containing a reference to a particular case, while the holding statement is masked out. The model must identify the correct (masked) holding statement from a selection of five choices. The current leaderboard includes several Transformer-based (Vaswaniet al., 2017) pre-trained language models, which achieve state-of-the-art performance in most NLP tasks (Bommasani et al., 2021) and NLU benchmarks (Wang et al., 2019a). Results reported by [Chalkidis et al. (2021)](https://arxiv.org/abs/2110.00976): *Task-wise Test Results* <table> <tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr> <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr> <tr><td>TFIDF+SVM</td><td> 64.7 / 51.7 </td><td>74.6 / 65.1 </td><td> <b>78.2</b> / <b>69.5</b> </td><td>71.3 / 51.4 </td><td>87.2 / 82.4 </td><td>95.4 / 78.8</td><td>n/a </td></tr> <tr><td colspan="8" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr> <td>BERT</td> <td> 71.2 / 63.6 </td> <td> 79.7 / 73.4 </td> <td> 68.3 / 58.3 </td> <td> 71.4 / 57.2 </td> <td> 87.6 / 81.8 </td> <td> 95.6 / 81.3 </td> <td> 70.8 </td> </tr> <td>RoBERTa</td> <td> 69.2 / 59.0 </td> <td> 77.3 / 68.9 </td> <td> 71.6 / 62.0 </td> <td> 71.9 / <b>57.9</b> </td> <td> 87.9 / 82.3 </td> <td> 95.2 / 79.2 </td> <td> 71.4 </td> </tr> <td>DeBERTa</td> <td> 70.0 / 60.8 </td> <td> 78.8 / 71.0 </td> <td> 71.1 / 62.7 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.1 </td> <td> 95.5 / 80.3 </td> <td> 72.6 </td> </tr> <td>Longformer</td> <td> 69.9 / 64.7 </td> <td> 79.4 / 71.7 </td> <td> 72.9 / 64.0 </td> <td> 71.6 / 57.7 </td> <td> 88.2 / 83.0 </td> <td> 95.5 / 80.9 </td> <td> 71.9 </td> </tr> <td>BigBird</td> <td> 70.0 / 62.9 </td> <td> 78.8 / 70.9 </td> <td> 72.8 / 62.0 </td> <td> 71.5 / 56.8 </td> <td> 87.8 / 82.6 </td> <td> 95.7 / 81.3 </td> <td> 70.8 </td> </tr> <td>Legal-BERT</td> <td> 70.0 / 64.0 </td> <td> <b>80.4</b> / <b>74.7</b> </td> <td> 76.4 / 66.5 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.0 </td> <td> <b>96.0</b> / <b>83.0</b> </td> <td> 75.3 </td> </tr> <td>CaseLaw-BERT</td> <td> 69.8 / 62.9 </td> <td> 78.8 / 70.3 </td> <td> 76.6 / 65.9 </td> <td> 70.7 / 56.6 </td> <td> 88.3 / 83.0 </td> <td> <b>96.0</b> / 82.3 </td> <td> <b>75.4</b> </td> </tr> <tr><td colspan="8" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr> <tr><td>RoBERTa</td> <td> <b>73.8</b> / <b>67.6</b> </td> <td> 79.8 / 71.6 </td> <td> 75.5 / 66.3 </td> <td> 67.9 / 50.3 </td> <td> <b>88.6</b> / <b>83.6</b> </td> <td> 95.8 / 81.6 </td> <td> 74.4 </td> </tr> </table> *Averaged (Mean over Tasks) Test Results* <table> <tr><td><b>Averaging</b></td><td><b>Arithmetic</b></td><td><b>Harmonic</b></td><td><b>Geometric</b></td></tr> <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td></tr> <tr><td colspan="4" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr> <tr><td>BERT</td><td> 77.8 / 69.5 </td><td> 76.7 / 68.2 </td><td> 77.2 / 68.8 </td></tr> <tr><td>RoBERTa</td><td> 77.8 / 68.7 </td><td> 76.8 / 67.5 </td><td> 77.3 / 68.1 </td></tr> <tr><td>DeBERTa</td><td> 78.3 / 69.7 </td><td> 77.4 / 68.5 </td><td> 77.8 / 69.1 </td></tr> <tr><td>Longformer</td><td> 78.5 / 70.5 </td><td> 77.5 / 69.5 </td><td> 78.0 / 70.0 </td></tr> <tr><td>BigBird</td><td> 78.2 / 69.6 </td><td> 77.2 / 68.5 </td><td> 77.7 / 69.0 </td></tr> <tr><td>Legal-BERT</td><td> <b>79.8</b> / <b>72.0</b> </td><td> <b>78.9</b> / <b>70.8</b> </td><td> <b>79.3</b> / <b>71.4</b> </td></tr> <tr><td>CaseLaw-BERT</td><td> 79.4 / 70.9 </td><td> 78.5 / 69.7 </td><td> 78.9 / 70.3 </td></tr> <tr><td colspan="4" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr> <tr><td>RoBERTa</td><td> 79.4 / 70.8 </td><td> 78.4 / 69.1 </td><td> 78.9 / 70.0 </td></tr> </table> ### Languages We only consider English datasets, to make experimentation easier for researchers across the globe. ## Dataset Structure ### Data Instances #### ecthr_a An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "labels": [6] } ``` #### ecthr_b An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "label": [5, 6] } ``` #### scotus An example of 'train' looks as follows. ```json { "text": "Per Curiam\nSUPREME COURT OF THE UNITED STATES\nRANDY WHITE, WARDEN v. ROGER L. WHEELER\n Decided December 14, 2015\nPER CURIAM.\nA death sentence imposed by a Kentucky trial court and\naffirmed by the ...", "label": 8 } ``` #### eurlex An example of 'train' looks as follows. ```json { "text": "COMMISSION REGULATION (EC) No 1629/96 of 13 August 1996 on an invitation to tender for the refund on export of wholly milled round grain rice to certain third countries ...", "labels": [4, 20, 21, 35, 68] } ``` #### ledgar An example of 'train' looks as follows. ```json { "text": "All Taxes shall be the financial responsibility of the party obligated to pay such Taxes as determined by applicable law and neither party is or shall be liable at any time for any of the other party ...", "label": 32 } ``` #### unfair_tos An example of 'train' looks as follows. ```json { "text": "tinder may terminate your account at any time without notice if it believes that you have violated this agreement.", "label": 2 } ``` #### casehold An example of 'test' looks as follows. ```json { "context": "In Granato v. City and County of Denver, No. CIV 11-0304 MSK/BNB, 2011 WL 3820730 (D.Colo. Aug. 20, 2011), the Honorable Marcia S. Krieger, now-Chief United States District Judge for the District of Colorado, ruled similarly: At a minimum, a party asserting a Mo-nell claim must plead sufficient facts to identify ... to act pursuant to City or State policy, custom, decision, ordinance, re d 503, 506-07 (3d Cir.l985)(<HOLDING>).", "endings": ["holding that courts are to accept allegations in the complaint as being true including monell policies and writing that a federal court reviewing the sufficiency of a complaint has a limited task", "holding that for purposes of a class certification motion the court must accept as true all factual allegations in the complaint and may draw reasonable inferences therefrom", "recognizing that the allegations of the complaint must be accepted as true on a threshold motion to dismiss", "holding that a court need not accept as true conclusory allegations which are contradicted by documents referred to in the complaint", "holding that where the defendant was in default the district court correctly accepted the fact allegations of the complaint as true" ], "label": 0 } ``` ### Data Fields #### ecthr_a - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description). - `labels`: a list of classification labels (a list of violated ECHR articles, if any) . <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### ecthr_b - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description) - `labels`: a list of classification labels (a list of articles considered). <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### scotus - `text`: a `string` feature (the court opinion). - `label`: a classification label (the relevant issue area). <details> <summary>List of issue areas</summary> (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action) </details> #### eurlex - `text`: a `string` feature (an EU law). - `labels`: a list of classification labels (a list of relevant EUROVOC concepts). <details> <summary>List of EUROVOC concepts</summary> The list is very long including 100 EUROVOC concepts. You can find the EUROVOC concepts descriptors <a href="https://raw.githubusercontent.com/nlpaueb/multi-eurlex/master/data/eurovoc_descriptors.json">here</a>. </details> #### ledgar - `text`: a `string` feature (a contract provision/paragraph). - `label`: a classification label (the type of contract provision). <details> <summary>List of contract provision types</summary> "Adjustments", "Agreements", "Amendments", "Anti-Corruption Laws", "Applicable Laws", "Approvals", "Arbitration", "Assignments", "Assigns", "Authority", "Authorizations", "Base Salary", "Benefits", "Binding Effects", "Books", "Brokers", "Capitalization", "Change In Control", "Closings", "Compliance With Laws", "Confidentiality", "Consent To Jurisdiction", "Consents", "Construction", "Cooperation", "Costs", "Counterparts", "Death", "Defined Terms", "Definitions", "Disability", "Disclosures", "Duties", "Effective Dates", "Effectiveness", "Employment", "Enforceability", "Enforcements", "Entire Agreements", "Erisa", "Existence", "Expenses", "Fees", "Financial Statements", "Forfeitures", "Further Assurances", "General", "Governing Laws", "Headings", "Indemnifications", "Indemnity", "Insurances", "Integration", "Intellectual Property", "Interests", "Interpretations", "Jurisdictions", "Liens", "Litigations", "Miscellaneous", "Modifications", "No Conflicts", "No Defaults", "No Waivers", "Non-Disparagement", "Notices", "Organizations", "Participations", "Payments", "Positions", "Powers", "Publicity", "Qualifications", "Records", "Releases", "Remedies", "Representations", "Sales", "Sanctions", "Severability", "Solvency", "Specific Performance", "Submission To Jurisdiction", "Subsidiaries", "Successors", "Survival", "Tax Withholdings", "Taxes", "Terminations", "Terms", "Titles", "Transactions With Affiliates", "Use Of Proceeds", "Vacations", "Venues", "Vesting", "Waiver Of Jury Trials", "Waivers", "Warranties", "Withholdings", </details> #### unfair_tos - `text`: a `string` feature (a ToS sentence) - `labels`: a list of classification labels (a list of unfair types, if any). <details> <summary>List of unfair types</summary> "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration" </details> #### casehold - `context`: a `string` feature (a context sentence incl. a masked holding statement). - `holdings`: a list of `string` features (a list of candidate holding statements). - `label`: a classification label (the id of the original/correct holding). ### Data Splits <table> <tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr> <tr><td>ECtHR (Task A)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>ECtHR (Task B)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>SCOTUS</td><td>5,000</td><td>1,400</td><td>1,400</td><td>7,800</td></tr> <tr><td>EUR-LEX</td><td>55,000</td><td>5,000</td><td>5,000</td><td>65,000</td></tr> <tr><td>LEDGAR</td><td>60,000</td><td>10,000</td><td>10,000</td><td>80,000</td></tr> <tr><td>UNFAIR-ToS</td><td>5,532</td><td>2,275</td><td>1,607</td><td>9,414</td></tr> <tr><td>CaseHOLD</td><td>45,000</td><td>3,900</td><td>3,900</td><td>52,800</td></tr> </table> ## 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 <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td></tr> </table> #### 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Curators *Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.* ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information [*Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.*](https://arxiv.org/abs/2110.00976) ``` @inproceedings{chalkidis-etal-2021-lexglue, title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, author={Chalkidis, Ilias and Jana, Abhik and Hartung, Dirk and Bommarito, Michael and Androutsopoulos, Ion and Katz, Daniel Martin and Aletras, Nikolaos}, year={2022}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, address={Dubln, Ireland}, } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.

lex_glue

2022-03-02T23:29:22+00:00

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"num_examples": 5000}], "download_size": 208028049, "dataset_size": 492053811}, {"config_name": "ledgar", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Adjustments", "1": "Agreements", "2": "Amendments", "3": "Anti-Corruption Laws", "4": "Applicable Laws", "5": "Approvals", "6": "Arbitration", "7": "Assignments", "8": "Assigns", "9": "Authority", "10": "Authorizations", "11": "Base Salary", "12": "Benefits", "13": "Binding Effects", "14": "Books", "15": "Brokers", "16": "Capitalization", "17": "Change In Control", "18": "Closings", "19": "Compliance With Laws", "20": "Confidentiality", "21": "Consent To Jurisdiction", "22": "Consents", "23": "Construction", "24": "Cooperation", "25": "Costs", "26": "Counterparts", "27": "Death", "28": "Defined Terms", "29": "Definitions", "30": "Disability", "31": "Disclosures", "32": "Duties", "33": "Effective Dates", "34": "Effectiveness", "35": "Employment", "36": "Enforceability", "37": "Enforcements", "38": "Entire Agreements", "39": "Erisa", "40": "Existence", "41": "Expenses", "42": "Fees", "43": "Financial Statements", "44": "Forfeitures", "45": "Further Assurances", "46": "General", "47": "Governing Laws", "48": "Headings", "49": "Indemnifications", "50": "Indemnity", "51": "Insurances", "52": "Integration", "53": "Intellectual Property", "54": "Interests", "55": "Interpretations", "56": "Jurisdictions", "57": "Liens", "58": "Litigations", "59": "Miscellaneous", "60": "Modifications", "61": "No Conflicts", "62": "No Defaults", "63": "No Waivers", "64": "Non-Disparagement", "65": "Notices", "66": "Organizations", "67": "Participations", "68": "Payments", "69": "Positions", "70": "Powers", "71": "Publicity", "72": "Qualifications", "73": "Records", "74": "Releases", "75": "Remedies", "76": "Representations", "77": "Sales", "78": "Sanctions", "79": "Severability", "80": "Solvency", "81": "Specific Performance", "82": "Submission To Jurisdiction", "83": "Subsidiaries", "84": "Successors", "85": "Survival", "86": "Tax Withholdings", "87": "Taxes", "88": "Terminations", "89": "Terms", "90": "Titles", "91": "Transactions With Affiliates", "92": "Use Of Proceeds", "93": "Vacations", "94": "Venues", "95": "Vesting", "96": "Waiver Of Jury Trials", "97": "Waivers", "98": "Warranties", "99": "Withholdings"}}}}], "splits": [{"name": "train", "num_bytes": 43358291, "num_examples": 60000}, {"name": "test", "num_bytes": 6845581, "num_examples": 10000}, {"name": "validation", "num_bytes": 7143588, "num_examples": 10000}], "download_size": 27650585, "dataset_size": 57347460}, {"config_name": "scotus", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"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"}}}}], "splits": [{"name": "train", "num_bytes": 178959316, "num_examples": 5000}, {"name": "test", "num_bytes": 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"data_files": [{"split": "train", "path": "ecthr_a/train-*"}, {"split": "test", "path": "ecthr_a/test-*"}, {"split": "validation", "path": "ecthr_a/validation-*"}]}, {"config_name": "ecthr_b", "data_files": [{"split": "train", "path": "ecthr_b/train-*"}, {"split": "test", "path": "ecthr_b/test-*"}, {"split": "validation", "path": "ecthr_b/validation-*"}]}, {"config_name": "eurlex", "data_files": [{"split": "train", "path": "eurlex/train-*"}, {"split": "test", "path": "eurlex/test-*"}, {"split": "validation", "path": "eurlex/validation-*"}]}, {"config_name": "ledgar", "data_files": [{"split": "train", "path": "ledgar/train-*"}, {"split": "test", "path": "ledgar/test-*"}, {"split": "validation", "path": "ledgar/validation-*"}]}, {"config_name": "scotus", "data_files": [{"split": "train", "path": "scotus/train-*"}, {"split": "test", "path": "scotus/test-*"}, {"split": "validation", "path": "scotus/validation-*"}]}, {"config_name": "unfair_tos", "data_files": [{"split": "train", "path": 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2024-01-04T14:25:27+00:00

TAGS #task_categories-question-answering #task_categories-text-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-multiple-choice-qa #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2110.00976 #arxiv-2109.00904 #arxiv-1805.01217 #arxiv-2104.08671 #region-us

Dataset Card for "LexGLUE" ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: URL * Point of Contact: Ilias Chalkidis ### Dataset Summary Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the *Legal General Language Understanding Evaluation (LexGLUE) benchmark*, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE. As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks. LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: URL ### Supported Tasks and Leaderboards The supported tasks are the following: #### ecthr\_a The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of the ECHR that were violated (if any). #### ecthr\_b The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of ECHR that were allegedly violated (considered by the court). #### scotus The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. This is a single-label multi-class classification task, where given a document (court opinion), the task is to predict the relevant issue areas. The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute). #### eurlex European Union (EU) legislation is published in EUR-Lex portal. All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office. The current version of EuroVoc contains more than 7k concepts referring to various activities of the EU and its Member States (e.g., economics, health-care, trade). Given a document, the task is to predict its EuroVoc labels (concepts). #### ledgar LEDGAR dataset aims contract provision (paragraph) classification. The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) filings, which are publicly available from EDGAR. Each label represents the single main topic (theme) of the corresponding contract provision. #### unfair\_tos The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law. #### case\_hold The CaseHOLD (Case Holdings on Legal Decisions) dataset includes multiple choice questions about holdings of US court cases from the Harvard Law Library case law corpus. Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. The input consists of an excerpt (or prompt) from a court decision, containing a reference to a particular case, while the holding statement is masked out. The model must identify the correct (masked) holding statement from a selection of five choices. The current leaderboard includes several Transformer-based (Vaswaniet al., 2017) pre-trained language models, which achieve state-of-the-art performance in most NLP tasks (Bommasani et al., 2021) and NLU benchmarks (Wang et al., 2019a). Results reported by Chalkidis et al. (2021): *Task-wise Test Results* *Averaged (Mean over Tasks) Test Results* ### Languages We only consider English datasets, to make experimentation easier for researchers across the globe. Dataset Structure ----------------- ### Data Instances #### ecthr\_a An example of 'train' looks as follows. #### ecthr\_b An example of 'train' looks as follows. #### scotus An example of 'train' looks as follows. #### eurlex An example of 'train' looks as follows. #### ledgar An example of 'train' looks as follows. #### unfair\_tos An example of 'train' looks as follows. #### casehold An example of 'test' looks as follows. ### Data Fields #### ecthr\_a * 'text': a list of 'string' features (list of factual paragraphs (facts) from the case description). * 'labels': a list of classification labels (a list of violated ECHR articles, if any) . List of ECHR articles "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" #### ecthr\_b * 'text': a list of 'string' features (list of factual paragraphs (facts) from the case description) * 'labels': a list of classification labels (a list of articles considered). List of ECHR articles "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" #### scotus * 'text': a 'string' feature (the court opinion). * 'label': a classification label (the relevant issue area). List of issue areas (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action) #### eurlex * 'text': a 'string' feature (an EU law). * 'labels': a list of classification labels (a list of relevant EUROVOC concepts). List of EUROVOC concepts The list is very long including 100 EUROVOC concepts. You can find the EUROVOC concepts descriptors [#### unfair\_tos * 'text': a 'string' feature (a ToS sentence) * 'labels': a list of classification labels (a list of unfair types, if any). List of unfair types "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration" #### casehold * 'context': a 'string' feature (a context sentence incl. a masked holding statement). * 'holdings': a list of 'string' features (a list of candidate holding statements). * 'label': a classification label (the id of the original/correct holding). ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators *Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.* ### Licensing Information *Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.* ### Contributions Thanks to @iliaschalkidis for adding this dataset.](URL </details> <h4>ledgar</h4> <ul> <li>'text': a 'string' feature (a contract provision/paragraph).</li> <li>'label': a classification label (the type of contract provision).</li> </ul> <details> <summary>List of contract provision types</summary> )

ff3a15312273f5294c4418c1c3c28781e1d51afe

# 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://sites.cs.ucsb.edu/~william/ - **Repository:** - **Paper:** https://arxiv.org/abs/1705.00648 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English. ## 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 [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.

liar

2022-03-02T23:29:22+00:00

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2024-01-18T11:08:08+00:00

TAGS #task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #fake-news-detection #arxiv-1705.00648 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary LIAR is a dataset for fake news detection with 12.8K human labeled short statements from URL's API, and each statement is evaluated by a URL editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment. ### Supported Tasks and Leaderboards ### Languages English. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @hugoabonizio for adding this dataset.

8aeb8cac5ad165fc5574d4e84218154a8f4eca7b

# Dataset Card for librispeech_asr ## 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:** [LibriSpeech ASR corpus](http://www.openslr.org/12) - **Repository:** [Needs More Information] - **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) - **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - **Point of Contact:** [Daniel Povey](mailto:[email protected]) ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: 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 has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: `clean` and `other`. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac 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. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. | | Train.500 | Train.360 | Train.100 | Valid | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | clean | - | 104014 | 28539 | 2703 | 2620| | other | 148688 | - | - | 2864 | 2939 | ## 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 [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

librispeech_asr

2022-03-02T23:29:22+00:00

{"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", "audio-classification"], "task_ids": ["speaker-identification"], "paperswithcode_id": "librispeech-1", "pretty_name": "LibriSpeech", "dataset_info": [{"config_name": "clean", "features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train.100", "num_bytes": 6619683041, "num_examples": 28539}, {"name": "train.360", "num_bytes": 23898214592, "num_examples": 104014}, {"name": "validation", "num_bytes": 359572231, "num_examples": 2703}, {"name": "test", "num_bytes": 367705423, "num_examples": 2620}], "download_size": 30121377654, "dataset_size": 31245175287}, {"config_name": "other", "features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train.500", "num_bytes": 31810256902, "num_examples": 148688}, {"name": "validation", "num_bytes": 337283304, "num_examples": 2864}, {"name": "test", "num_bytes": 352396474, "num_examples": 2939}], "download_size": 31236565377, "dataset_size": 32499936680}, {"config_name": "all", "features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train.clean.100", "num_bytes": 6627791685, "num_examples": 28539}, {"name": "train.clean.360", "num_bytes": 23927767570, "num_examples": 104014}, {"name": "train.other.500", "num_bytes": 31852502880, "num_examples": 148688}, {"name": "validation.clean", "num_bytes": 359505691, "num_examples": 2703}, {"name": "validation.other", "num_bytes": 337213112, "num_examples": 2864}, {"name": "test.clean", "num_bytes": 368449831, "num_examples": 2620}, {"name": "test.other", "num_bytes": 353231518, "num_examples": 2939}], "download_size": 61357943031, "dataset_size": 63826462287}]}

2024-01-18T11:08:09+00:00

TAGS #task_categories-automatic-speech-recognition #task_categories-audio-classification #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us

Dataset Card for librispeech\_asr ================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: LibriSpeech ASR corpus * Repository: * Paper: LibriSpeech: An ASR Corpus Based On Public Domain Audio Books * Leaderboard: The Speech Bench * Point of Contact: Daniel Povey ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards * 'automatic-speech-recognition', 'audio-speaker-identification': 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 has an active Hugging Face leaderboard which can be found at URL The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at URL ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: 'clean' and 'other'. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". Dataset Structure ----------------- ### Data Instances A typical data point comprises the path to the audio file, usually called 'file' and its transcription, called 'text'. Some additional information about the speaker and the passage which contains the transcription is provided. ### Data Fields * file: A path to the downloaded audio file in .flac 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. * id: unique id of the data sample. * speaker\_id: unique id of the speaker. The same speaker id can be found for multiple data samples. * chapter\_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information CC BY 4.0 ### Contributions Thanks to @patrickvonplaten for adding this dataset.

aea06a5d472949161231b1feaa56395872e0f828

# Dataset Card for "librispeech_lm" ## 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.openslr.org/11](http://www.openslr.org/11) - **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:** 1.51 GB - **Size of the generated dataset:** 4.42 GB - **Total amount of disk used:** 5.93 GB ### Dataset Summary Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus. ### 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:** 1.51 GB - **Size of the generated dataset:** 4.42 GB - **Total amount of disk used:** 5.93 GB An example of 'train' looks as follows. ``` { "text": "This is a test file" } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. ### Data Splits | name | train | |-------|-------:| |default|40418260| ## 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{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

librispeech_lm

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "LibrispeechLm", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4418577129, "num_examples": 40418260}], "download_size": 1507274412, "dataset_size": 4418577129}}

2024-01-18T11:08:11+00:00

TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc0-1.0 #region-us

Dataset Card for "librispeech\_lm" ================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.51 GB * Size of the generated dataset: 4.42 GB * Total amount of disk used: 5.93 GB ### Dataset Summary Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 1.51 GB * Size of the generated dataset: 4.42 GB * Total amount of disk used: 5.93 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'text': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @lewtun, @jplu, @thomwolf for adding this dataset.

f2b5b147221550a6e8f9ad80bd94114e357a2b6a

# Dataset Card for LiMiT ## 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:** [github](https://github.com/ilmgut/limit_dataset) - **Paper:** [LiMiT: The Literal Motion in Text Dataset](https://www.aclweb.org/anthology/2020.findings-emnlp.88/) - **Leaderboard:** N/A - **Point of Contact:** [More Information Needed] ### Dataset Summary Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances Example of one instance in the dataset ``` { "id": 0, "motion": "yes", "motion_entities": [ { "entity": "little boy", "start_index": 2 }, { "entity": "ball", "start_index": 30 } ], "sentence": " A little boy holding a yellow ball walks by." } ``` ### Data Fields - `id`: intger index of the example - `motion`: indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not - `motion_entities`: A `list` of `dicts` with following keys - `entity`: the extracted entity in motion - `start_index`: index in the sentence for the first char of the entity text ### Data Splits The dataset is split into a `train`, and `test` split with the following sizes: | | train | validation | | ----- |------:|-----------:| | Number of examples | 23559 | 1000 | ## 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 ``` @inproceedings{manotas-etal-2020-limit, title = "{L}i{M}i{T}: The Literal Motion in Text Dataset", author = "Manotas, Irene and Vo, Ngoc Phuoc An and Sheinin, Vadim", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88", doi = "10.18653/v1/2020.findings-emnlp.88", pages = "991--1000", abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.", } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

limit

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|net-activities-captions", "original"], "task_categories": ["token-classification", "text-classification"], "task_ids": ["multi-class-classification", "named-entity-recognition"], "paperswithcode_id": "limit", "pretty_name": "LiMiT", "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "sentence", "dtype": "string"}, {"name": "motion", "dtype": "string"}, {"name": "motion_entities", "list": [{"name": "entity", "dtype": "string"}, {"name": "start_index", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 3064208, "num_examples": 23559}, {"name": "test", "num_bytes": 139742, "num_examples": 1000}], "download_size": 4214925, "dataset_size": 3203950}}

2024-01-18T11:08:12+00:00

TAGS #task_categories-token-classification #task_categories-text-classification #task_ids-multi-class-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|net-activities-captions #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us

Dataset Card for LiMiT ====================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: - * Repository: github * Paper: LiMiT: The Literal Motion in Text Dataset * Leaderboard: N/A * Point of Contact: ### Dataset Summary Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. ### Supported Tasks and Leaderboards ### Languages The text in the dataset is in English ('en'). Dataset Structure ----------------- ### Data Instances Example of one instance in the dataset ### Data Fields * 'id': intger index of the example * 'motion': indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not * 'motion\_entities': A 'list' of 'dicts' with following keys + 'entity': the extracted entity in motion + 'start\_index': index in the sentence for the first char of the entity text ### Data Splits The dataset is split into a 'train', and 'test' split with the following sizes: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patil-suraj for adding this dataset.

88298d41cff1ca21d9b57fb26bf145f391047c39

# Dataset Card for "lince" ## 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://ritual.uh.edu/lince](http://ritual.uh.edu/lince) - **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:** 9.09 MB - **Size of the generated dataset:** 56.42 MB - **Total amount of disk used:** 65.52 MB ### Dataset Summary LinCE is a centralized Linguistic Code-switching Evaluation benchmark (https://ritual.uh.edu/lince/) that contains data for training and evaluating NLP systems on code-switching 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 #### lid_hineng - **Size of downloaded dataset files:** 0.43 MB - **Size of the generated dataset:** 2.39 MB - **Total amount of disk used:** 2.82 MB An example of 'validation' looks as follows. ``` { "idx": 0, "lid": ["other", "other", "lang1", "lang1", "lang1", "other", "lang1", "lang1", "lang1", "lang1", "lang1", "lang1", "lang1", "mixed", "lang1", "lang1", "other"], "words": ["@ZahirJ", "@BinyavangaW", "Loved", "the", "ending", "!", "I", "could", "have", "offered", "you", "some", "ironic", "chai-tea", "for", "it", ";)"] } ``` #### lid_msaea - **Size of downloaded dataset files:** 0.81 MB - **Size of the generated dataset:** 4.89 MB - **Total amount of disk used:** 5.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "idx": 0, "lid": ["ne", "lang2", "other", "lang2", "lang2", "other", "other", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "lang2", "other", "lang2", "lang2", "lang2", "ne", "lang2", "lang2"], "words": "[\"علاء\", \"بخير\", \"،\", \"معنوياته\", \"كويسة\", \".\", \"..\", \"اسخف\", \"حاجة\", \"بس\", \"ان\", \"كل\", \"واحد\", \"منهم\", \"بييقى\", \"مقفول\", \"عليه\"..." } ``` #### lid_nepeng - **Size of downloaded dataset files:** 0.55 MB - **Size of the generated dataset:** 3.21 MB - **Total amount of disk used:** 3.75 MB An example of 'validation' looks as follows. ``` { "idx": 1, "lid": ["other", "lang2", "lang2", "lang2", "lang2", "lang1", "lang1", "lang1", "lang1", "lang1", "lang2", "lang2", "other", "mixed", "lang2", "lang2", "other", "other", "other", "other"], "words": ["@nirvikdada", "la", "hamlai", "bhetna", "paayeko", "will", "be", "your", "greatest", "gift", "ni", "dada", ";P", "#TreatChaiyo", "j", "hos", ";)", "@zappylily", "@AsthaGhm", "@ayacs_asis"] } ``` #### lid_spaeng - **Size of downloaded dataset files:** 1.18 MB - **Size of the generated dataset:** 6.83 MB - **Total amount of disk used:** 8.01 MB An example of 'train' looks as follows. ``` { "idx": 0, "lid": ["other", "other", "lang1", "lang1", "lang1", "other", "lang1", "lang1"], "words": ["11:11", ".....", "make", "a", "wish", ".......", "night", "night"] } ``` #### ner_hineng - **Size of downloaded dataset files:** 0.14 MB - **Size of the generated dataset:** 0.79 MB - **Total amount of disk used:** 0.92 MB An example of 'train' looks as follows. ``` { "idx": 1, "lid": ["en", "en", "en", "en", "en", "en", "hi", "hi", "hi", "hi", "hi", "hi", "hi", "en", "en", "en", "en", "rest"], "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "I-PERSON", "O", "O", "O", "B-PERSON", "I-PERSON"], "words": ["I", "liked", "a", "@YouTube", "video", "https://t.co/DmVqhZbdaI", "Kabhi", "Palkon", "Pe", "Aasoon", "Hai-", "Kishore", "Kumar", "-Vocal", "Cover", "By", "Stephen", "Qadir"] } ``` ### Data Fields The data fields are the same among all splits. #### lid_hineng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### lid_msaea - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### lid_nepeng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### lid_spaeng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. #### ner_hineng - `idx`: a `int32` feature. - `words`: a `list` of `string` features. - `lid`: a `list` of `string` features. - `ner`: a `list` of `string` features. ### Data Splits | name |train|validation|test| |----------|----:|---------:|---:| |lid_hineng| 4823| 744|1854| |lid_msaea | 8464| 1116|1663| |lid_nepeng| 8451| 1332|3228| |lid_spaeng|21030| 3332|8289| |ner_hineng| 1243| 314| 522| ## 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{aguilar-etal-2020-lince, title = "{L}in{CE}: A Centralized Benchmark for Linguistic Code-switching Evaluation", author = "Aguilar, Gustavo and Kar, Sudipta and Solorio, Thamar", 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-1.223", pages = "1803--1813", language = "English", ISBN = "979-10-95546-34-4", } ``` Note that each LinCE dataset has its own citation too. Please see [here](https://ritual.uh.edu/lince/datasets) for the correct citation on each dataset. ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@gaguilar](https://github.com/gaguilar) for adding this dataset.

lince

2022-03-02T23:29:22+00:00

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2024-01-18T11:08:14+00:00

TAGS #region-us

Dataset Card for "lince" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 9.09 MB * Size of the generated dataset: 56.42 MB * Total amount of disk used: 65.52 MB ### Dataset Summary LinCE is a centralized Linguistic Code-switching Evaluation benchmark (URL that contains data for training and evaluating NLP systems on code-switching tasks. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### lid\_hineng * Size of downloaded dataset files: 0.43 MB * Size of the generated dataset: 2.39 MB * Total amount of disk used: 2.82 MB An example of 'validation' looks as follows. #### lid\_msaea * Size of downloaded dataset files: 0.81 MB * Size of the generated dataset: 4.89 MB * Total amount of disk used: 5.69 MB An example of 'train' looks as follows. #### lid\_nepeng * Size of downloaded dataset files: 0.55 MB * Size of the generated dataset: 3.21 MB * Total amount of disk used: 3.75 MB An example of 'validation' looks as follows. #### lid\_spaeng * Size of downloaded dataset files: 1.18 MB * Size of the generated dataset: 6.83 MB * Total amount of disk used: 8.01 MB An example of 'train' looks as follows. #### ner\_hineng * Size of downloaded dataset files: 0.14 MB * Size of the generated dataset: 0.79 MB * Total amount of disk used: 0.92 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### lid\_hineng * 'idx': a 'int32' feature. * 'words': a 'list' of 'string' features. * 'lid': a 'list' of 'string' features. #### lid\_msaea * 'idx': a 'int32' feature. * 'words': a 'list' of 'string' features. * 'lid': a 'list' of 'string' features. #### lid\_nepeng * 'idx': a 'int32' feature. * 'words': a 'list' of 'string' features. * 'lid': a 'list' of 'string' features. #### lid\_spaeng * 'idx': a 'int32' feature. * 'words': a 'list' of 'string' features. * 'lid': a 'list' of 'string' features. #### ner\_hineng * 'idx': a 'int32' feature. * 'words': a 'list' of 'string' features. * 'lid': a 'list' of 'string' features. * 'ner': a 'list' of 'string' features. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Note that each LinCE dataset has its own citation too. Please see here for the correct citation on each dataset. ### Contributions Thanks to @lhoestq, @thomwolf, @gaguilar for adding this dataset.

0f7960610ecb15ddb6a2c4811d1e230379c31c6e

# 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:** [linnaeus](http://linnaeus.sourceforge.net/) - **Repository:** https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/linnaeus-IOB - **Paper:** [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The LINNAEUS corpus consists of 100 full-text documents from the PMCOA document set which were randomly selected. All mentions of species terms were manually annotated and normalized to the NCBI taxonomy IDs of the intended species. The original LINNAEUS corpus is available in a TAB-separated standoff format. The resource does not define training, development or test subsets. We converted the corpus into BioNLP shared task standoff format using a custom script, split it into 50-, 17-, and 33- document training, development and test sets, and then converted these into the CoNLL format using standoff2conll. As a full-text corpus, LINNAEUS contains comparatively frequent non-ASCII characters, which were mapped to ASCII using the standoff2conll -a option. The conversion was highly accurate, but due to sentence-splitting errors within entity mentions, the number of annotations in the converted data was larger by four (100.09%) than that in the source data. 99.77% of names in the original annotation matched names in the converted data. ### Supported Tasks and Leaderboards This dataset is used for species Named Entity Recognition. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances An example from the dataset is: ``` {'id': '2', 'tokens': ['Scp160p', 'is', 'a', '160', 'kDa', 'protein', 'in', 'the', 'yeast', 'Saccharomyces', 'cerevisiae', 'that', 'contains', '14', 'repeats', 'of', 'the', 'hnRNP', 'K', '-', 'homology', '(', 'KH', ')', 'domain', ',', 'and', 'demonstrates', 'significant', 'sequence', 'homology', 'to', 'a', 'family', 'of', 'proteins', 'collectively', 'known', 'as', 'vigilins', '.'], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} ``` ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no species mentioned, `1` signals the first token of a species and `2` the subsequent tokens of the species. ### Data Splits | name |train|validation|test| |----------|----:|---------:|---:| | linnaeus |11936| 4079|7143| ## 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 This version of the dataset is licensed under [Creative Commons Attribution 4.0 International](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/blob/master/LICENSE.md). ### Citation Information ```bibtex @article{crichton2017neural, title={A neural network multi-task learning approach to biomedical named entity recognition}, author={Crichton, Gamal and Pyysalo, Sampo and Chiu, Billy and Korhonen, Anna}, journal={BMC Bioinformatics}, volume={18}, number={1}, pages={368}, year={2017}, publisher={BioMed Central} doi = {10.1186/s12859-017-1776-8}, issn = {1471-2105}, url = {https://doi.org/10.1186/s12859-017-1776-8}, } @article{Gerner2010, author = {Gerner, Martin and Nenadic, Goran and Bergman, Casey M}, doi = {10.1186/1471-2105-11-85}, issn = {1471-2105}, journal = {BMC Bioinformatics}, number = {1}, pages = {85}, title = {{LINNAEUS: A species name identification system for biomedical literature}}, url = {https://doi.org/10.1186/1471-2105-11-85}, volume = {11}, year = {2010} } ``` ### Contributions Thanks to [@edugp](https://github.com/edugp) for adding this dataset.

linnaeus

2022-03-02T23:29:22+00:00

{"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": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "linnaeus", "pretty_name": "LINNAEUS", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B", "2": "I"}}}}], "config_name": "linnaeus", "splits": [{"name": "train", "num_bytes": 4772417, "num_examples": 11936}, {"name": "validation", "num_bytes": 1592823, "num_examples": 4079}, {"name": "test", "num_bytes": 2802877, "num_examples": 7143}], "download_size": 18204624, "dataset_size": 9168117}}

2023-06-15T13:40:39+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #region-us

Dataset Card for [Dataset Name] =============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: linnaeus * Repository: URL * Paper: BMC Bioinformatics * Leaderboard: * Point of Contact: ### Dataset Summary The LINNAEUS corpus consists of 100 full-text documents from the PMCOA document set which were randomly selected. All mentions of species terms were manually annotated and normalized to the NCBI taxonomy IDs of the intended species. The original LINNAEUS corpus is available in a TAB-separated standoff format. The resource does not define training, development or test subsets. We converted the corpus into BioNLP shared task standoff format using a custom script, split it into 50-, 17-, and 33- document training, development and test sets, and then converted these into the CoNLL format using standoff2conll. As a full-text corpus, LINNAEUS contains comparatively frequent non-ASCII characters, which were mapped to ASCII using the standoff2conll -a option. The conversion was highly accurate, but due to sentence-splitting errors within entity mentions, the number of annotations in the converted data was larger by four (100.09%) than that in the source data. 99.77% of names in the original annotation matched names in the converted data. ### Supported Tasks and Leaderboards This dataset is used for species Named Entity Recognition. ### Languages The dataset is in English. Dataset Structure ----------------- ### Data Instances An example from the dataset is: ### Data Fields * 'id': Sentence identifier. * 'tokens': Array of tokens composing a sentence. * 'ner\_tags': Array of tags, where '0' indicates no species mentioned, '1' signals the first token of a species and '2' the subsequent tokens of the species. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information This version of the dataset is licensed under Creative Commons Attribution 4.0 International. ### Contributions Thanks to @edugp for adding this dataset.

d881de691676adc9706f59add6aab9cc55d1f159

# Dataset Card for LiveQA ## 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/PKU-TANGENT/LiveQA) - **Repository:** [Github](https://github.com/PKU-TANGENT/LiveQA) - **Paper:** [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf) - **Leaderboard:** N/A - **Point of Contact:** Qianying Liu ### Dataset Summary The LiveQA dataset is a Chinese question-answering resource constructed from playby-play live broadcasts. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu website. ### Supported Tasks and Leaderboards Question Answering. [More Information Needed] ### Languages Chinese. ## Dataset Structure ### Data Instances Each instance represents a timeline (i.e., a game) with an identifier. The passages field comprise an array of text or question segments. In the following truncated example, user comments about the game is followed by a question about which team will be the first to reach 60 points. ```python { 'id': 1, 'passages': [ { "is_question": False, "text": "'我希望两位球员都能做到！！", "candidate1": "", "candidate2": "", "answer": "", }, { "is_question": False, "text": "新年给我们送上精彩比赛！", "candidate1": "", "candidate2": "", "answer": "", }, { "is_question": True, "text": "先达到60分？", "candidate1": "火箭", "candidate2": "勇士", "answer": "勇士", }, { "is_question": False, "text": "自己急停跳投！！！", "candidate1": "", "candidate2": "", "answer": "", } ] } ``` ### Data Fields - id: identifier for the game - passages: collection of text/question segments - text: real-time text comment or binary question related to the context - candidate1/2: one of the two answer options to the question - answer: correct answer to the question in text ### Data Splits There is no predefined split in this dataset. ## 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 This resource is developed by [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf). ``` @inproceedings{qianying-etal-2020-liveqa, title = "{L}ive{QA}: A Question Answering Dataset over Sports Live", author = "Qianying, Liu and Sicong, Jiang and Yizhong, Wang and Sujian, Li", booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics", month = oct, year = "2020", address = "Haikou, China", publisher = "Chinese Information Processing Society of China", url = "https://www.aclweb.org/anthology/2020.ccl-1.98", pages = "1057--1067" } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.

liveqa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["zh"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "liveqa", "pretty_name": "LiveQA", "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "passages", "sequence": [{"name": "is_question", "dtype": "bool"}, {"name": "text", "dtype": "string"}, {"name": "candidate1", "dtype": "string"}, {"name": "candidate2", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 112187507, "num_examples": 1670}], "download_size": 114704569, "dataset_size": 112187507}}

2024-01-18T11:08:15+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Chinese #license-unknown #region-us

# Dataset Card for LiveQA ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: Liu et al., 2020 - Leaderboard: N/A - Point of Contact: Qianying Liu ### Dataset Summary The LiveQA dataset is a Chinese question-answering resource constructed from playby-play live broadcasts. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu website. ### Supported Tasks and Leaderboards Question Answering. ### Languages Chinese. ## Dataset Structure ### Data Instances Each instance represents a timeline (i.e., a game) with an identifier. The passages field comprise an array of text or question segments. In the following truncated example, user comments about the game is followed by a question about which team will be the first to reach 60 points. ### Data Fields - id: identifier for the game - passages: collection of text/question segments - text: real-time text comment or binary question related to the context - candidate1/2: one of the two answer options to the question - answer: correct answer to the question in text ### Data Splits There is no predefined split in this dataset. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information This resource is developed by Liu et al., 2020. ### Contributions Thanks to @j-chim for adding this dataset.

af8fa32e4f43a1251b5a2d9cc121181d66575939

# Dataset Card for lj_speech ## 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 LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/) - **Repository:** [N/A] - **Paper:** [N/A] - **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech) - **Point of Contact:** [Keith Ito](mailto:[email protected]) ### Dataset Summary This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. ### Supported Tasks and Leaderboards The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS). - `automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text. The most common ASR evaluation metric is the word error rate (WER). - `text-to-speech`, `text-to-audio`: A TTS model is given a written text in natural language and asked to generate a speech audio file. A reasonable evaluation metric is the mean opinion score (MOS) of audio quality. The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech ### Languages The transcriptions and audio are in English. ## Dataset Structure ### Data Instances A data point comprises the path to the audio file, called `file` and its transcription, called `text`. A normalized version of the text is also provided. ``` { 'id': 'LJ002-0026', 'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 22050}, 'text': 'in the three years between 1813 and 1816,' 'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,', } ``` Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. ### Data Fields - id: unique id of the data sample. - 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. - normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words. ### Data Splits The dataset is not pre-split. Some statistics: - Total Clips: 13,100 - Total Words: 225,715 - Total Characters: 1,308,678 - Total Duration: 23:55:17 - Mean Clip Duration: 6.57 sec - Min Clip Duration: 1.11 sec - Max Clip Duration: 10.10 sec - Mean Words per Clip: 17.23 - Distinct Words: 13,821 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization This dataset consists of excerpts from the following works: - Morris, William, et al. Arts and Crafts Essays. 1893. - Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884. - Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42. - Harland, Marion. Marion Harland's Cookery for Beginners. 1893. - Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910. - Banks, Edgar J. The Seven Wonders of the Ancient World. 1916. - President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964. Some details about normalization: - The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8) - 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être"). - The following abbreviations appear in the text. They may be expanded as follows: | Abbreviation | Expansion | |--------------|-----------| | Mr. | Mister | | Mrs. | Misess (*) | | Dr. | Doctor | | No. | Number | | St. | Saint | | Co. | Company | | Jr. | Junior | | Maj. | Major | | Gen. | General | | Drs. | Doctors | | Rev. | Reverend | | Lt. | Lieutenant | | Hon. | Honorable | | Sgt. | Sergeant | | Capt. | Captain | | Esq. | Esquire | | Ltd. | Limited | | Col. | Colonel | | Ft. | Fort | (*) there's no standard expansion for "Mrs." #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process - The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always. - The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio. #### Who are the annotators? Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. ### 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 - The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding. ## Additional Information ### Dataset Curators The dataset was initially created by Keith Ito and Linda Johnson. ### Licensing Information Public Domain ([LibriVox](https://librivox.org/pages/public-domain/)) ### Citation Information ``` @misc{ljspeech17, author = {Keith Ito and Linda Johnson}, title = {The LJ Speech Dataset}, howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}}, year = 2017 } ``` ### Contributions Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.

lj_speech

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["unlicense"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition", "text-to-speech", "text-to-audio"], "task_ids": [], "paperswithcode_id": "ljspeech", "pretty_name": "LJ Speech", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 22050}}}, {"name": "file", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "normalized_text", "dtype": "string"}], "config_name": "main", "splits": [{"name": "train", "num_bytes": 4667022, "num_examples": 13100}], "download_size": 2748572632, "dataset_size": 4667022}, "train-eval-index": [{"config": "main", "task": "automatic-speech-recognition", "task_id": "speech_recognition", "splits": {"train_split": "train"}, "col_mapping": {"file": "path", "text": "text"}, "metrics": [{"type": "wer", "name": "WER"}, {"type": "cer", "name": "CER"}]}]}

2024-01-18T11:08:21+00:00

TAGS #task_categories-automatic-speech-recognition #task_categories-text-to-speech #task_categories-text-to-audio #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unlicense #region-us

Dataset Card for lj\_speech =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: The LJ Speech Dataset * Repository: [N/A] * Paper: [N/A] * Leaderboard: Paperswithcode Leaderboard * Point of Contact: Keith Ito ### Dataset Summary This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. ### Supported Tasks and Leaderboards The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS). * 'automatic-speech-recognition': An ASR model is presented with an audio file and asked to transcribe the audio file to written text. The most common ASR evaluation metric is the word error rate (WER). * 'text-to-speech', 'text-to-audio': A TTS model is given a written text in natural language and asked to generate a speech audio file. A reasonable evaluation metric is the mean opinion score (MOS) of audio quality. The dataset has an active leaderboard which can be found at URL ### Languages The transcriptions and audio are in English. Dataset Structure ----------------- ### Data Instances A data point comprises the path to the audio file, called 'file' and its transcription, called 'text'. A normalized version of the text is also provided. Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. ### Data Fields * id: unique id of the data sample. * 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. * normalized\_text: the transcription with numbers, ordinals, and monetary units expanded into full words. ### Data Splits The dataset is not pre-split. Some statistics: * Total Clips: 13,100 * Total Words: 225,715 * Total Characters: 1,308,678 * Total Duration: 23:55:17 * Mean Clip Duration: 6.57 sec * Min Clip Duration: 1.11 sec * Max Clip Duration: 10.10 sec * Mean Words per Clip: 17.23 * Distinct Words: 13,821 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization This dataset consists of excerpts from the following works: * Morris, William, et al. Arts and Crafts Essays. 1893. * Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884. * Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42. * Harland, Marion. Marion Harland's Cookery for Beginners. 1893. * Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910. * Banks, Edgar J. The Seven Wonders of the Ancient World. 1916. * President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964. Some details about normalization: * The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8) * 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être"). * The following abbreviations appear in the text. They may be expanded as follows: #### Who are the source language producers? ### Annotations #### Annotation process * The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always. * The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio. #### Who are the annotators? Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. ### 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 ### Discussion of Biases ### Other Known Limitations * The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding. Additional Information ---------------------- ### Dataset Curators The dataset was initially created by Keith Ito and Linda Johnson. ### Licensing Information Public Domain (LibriVox) ### Contributions Thanks to @anton-l for adding this dataset.

35161838ea9e05371a25a8db037f94fcae4c2064

# Dataset Card for One Billion Word Language Model Benchmark ## 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:** [statmt](http://www.statmt.org/lm-benchmark/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [arxiv](https://arxiv.org/pdf/1312.3005v3.pdf) - **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:** 1.79 GB - **Size of the generated dataset:** 4.28 GB - **Total amount of disk used:** 6.07 GB ### Dataset Summary A benchmark corpus to be used for measuring progress in statistical language modeling. This has almost one billion words in the training data. ### 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:** 1.79 GB - **Size of the generated dataset:** 4.28 GB - **Total amount of disk used:** 6.07 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "While athletes in different professions dealt with doping scandals and other controversies , Woods continued to do what he did best : dominate the field of professional golf and rake in endorsements ." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits | name | train | test | |------------|----------|--------| | plain_text | 30301028 | 306688 | ## 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 The dataset doesn't contain annotations. ### 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 Needeate this repository accordingly. ### Citation Information ```bibtex @misc{chelba2014billion, title={One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling}, author={Ciprian Chelba and Tomas Mikolov and Mike Schuster and Qi Ge and Thorsten Brants and Phillipp Koehn and Tony Robinson}, year={2014}, eprint={1312.3005}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

lm1b

2022-03-02T23:29:22+00:00

{"language": ["en"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "billion-word-benchmark", "pretty_name": "One Billion Word Language Model Benchmark", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 4238206516, "num_examples": 30301028}, {"name": "test", "num_bytes": 42942045, "num_examples": 306688}], "download_size": 1792209805, "dataset_size": 4281148561}}

2024-01-18T11:08:23+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #language-English #arxiv-1312.3005 #region-us

Dataset Card for One Billion Word Language Model Benchmark ========================================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: statmt * Repository: * Paper: arxiv * Point of Contact: * Size of downloaded dataset files: 1.79 GB * Size of the generated dataset: 4.28 GB * Total amount of disk used: 6.07 GB ### Dataset Summary A benchmark corpus to be used for measuring progress in statistical language modeling. This has almost one billion words in the training data. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### plain\_text * Size of downloaded dataset files: 1.79 GB * Size of the generated dataset: 4.28 GB * Total amount of disk used: 6.07 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### plain\_text * 'text': a 'string' feature. ### Data Splits name: plain\_text, train: 30301028, test: 306688 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations The dataset doesn't contain annotations. ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information More Information Needeate this repository accordingly. ### Contributions Thanks to [@patrickvonplaten, @lewtun, @jplu, @thomwolf for adding this dataset.

49542818a137d7be194ab522c113142580403afe

# Dataset Card for LST20 ## 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:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [email]([email protected]) ### Dataset Summary LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand. It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries. At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with 16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is considered large enough for developing joint neural models for NLP. Manually download at https://aiforthai.in.th/corpus.php See `LST20 Annotation Guideline.pdf` and `LST20 Brief Specification.pdf` within the downloaded `AIFORTHAI-LST20Corpus.tar.gz` for more details. ### Supported Tasks and Leaderboards - POS tagging - NER tagging - clause segmentation - sentence segmentation - word tokenization ### Languages Thai ## Dataset Structure ### Data Instances ``` {'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '0', 'ner_tags': [8, 0, 0, 0, 0, 0, 0, 0, 25], 'pos_tags': [0, 0, 0, 1, 0, 8, 8, 8, 0], 'tokens': ['ธรรมนูญ', 'แชมป์', 'สิงห์คลาสสิก', 'กวาด', 'รางวัล', 'แสน', 'สี่', 'หมื่น', 'บาท']} {'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '1', 'ner_tags': [8, 18, 28, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 15, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6], 'pos_tags': [0, 2, 0, 2, 1, 1, 2, 8, 2, 10, 2, 8, 2, 1, 0, 1, 0, 4, 7, 1, 0, 2, 8, 2, 10, 1, 10, 4, 2, 8, 2, 4, 0, 4, 0, 2, 8, 2, 10, 2, 8], 'tokens': ['ธรรมนูญ', '_', 'ศรีโรจน์', '_', 'เก็บ', 'เพิ่ม', '_', '4', '_', 'อันเดอร์พาร์', '_', '68', '_', 'เข้า', 'ป้าย', 'รับ', 'แชมป์', 'ใน', 'การ', 'เล่น', 'อาชีพ', '_', '19', '_', 'ปี', 'เป็น', 'ครั้ง', 'ที่', '_', '8', '_', 'ใน', 'ชีวิต', 'ด้วย', 'สกอร์', '_', '18', '_', 'อันเดอร์พาร์', '_', '270']} ``` ### Data Fields - `id`: nth sentence in each set, starting at 0 - `fname`: text file from which the sentence comes from - `tokens`: word tokens - `pos_tags`: POS tags - `ner_tags`: NER tags - `clause_tags`: clause tags ### Data Splits | | train | eval | test | all | |----------------------|-----------|-------------|-------------|-----------| | words | 2,714,848 | 240,891 | 207,295 | 3,163,034 | | named entities | 246,529 | 23,176 | 18,315 | 288,020 | | clauses | 214,645 | 17,486 | 16,050 | 246,181 | | sentences | 63,310 | 5,620 | 5,250 | 74,180 | | distinct words | 42,091 | (oov) 2,595 | (oov) 2,006 | 46,692 | | breaking spaces※ | 63,310 | 5,620 | 5,250 | 74,180 | | non-breaking spaces※※| 402,380 | 39,920 | 32,204 | 475,504 | ※ Breaking space = space that is used as a sentence boundary marker ※※ Non-breaking space = space that is not used as a sentence boundary marker ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Respective authors of the news articles ### Annotations #### Annotation process Detailed annotation guideline can be found in `LST20 Annotation Guideline.pdf`. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information All texts are from public news. No personal and sensitive information is expected to be included. ## Considerations for Using the Data ### Social Impact of Dataset - Large-scale Thai NER & POS tagging, clause & sentence segmentatation, word tokenization ### Discussion of Biases - All 3,745 texts are from news domain: - politics: 841 - crime and accident: 592 - economics: 512 - entertainment: 472 - sports: 402 - international: 279 - science, technology and education: 216 - health: 92 - general: 75 - royal: 54 - disaster: 52 - development: 45 - environment: 40 - culture: 40 - weather forecast: 33 - Word tokenization is done accoding to Inter­BEST 2009 Guideline. ### Other Known Limitations - Some NER tags do not correspond with given labels (`B`, `I`, and so on) ## Additional Information ### Dataset Curators [NECTEC](https://www.nectec.or.th/en/) ### Licensing Information 1. Non-commercial use, research, and open source Any non-commercial use of the dataset for research and open-sourced projects is encouraged and free of charge. Please cite our technical report for reference. If you want to perpetuate your models trained on our dataset and share them to the research community in Thailand, please send your models, code, and APIs to the AI for Thai Project. Please contact Dr. Thepchai Supnithi via [email protected] for more information. Note that modification and redistribution of the dataset by any means are strictly prohibited unless authorized by the corpus authors. 2. Commercial use In any commercial use of the dataset, there are two options. - Option 1 (in kind): Contributing a dataset of 50,000 words completely annotated with our annotation scheme within 1 year. Your data will also be shared and recognized as a dataset co-creator in the research community in Thailand. - Option 2 (in cash): Purchasing a lifetime license for the entire dataset is required. The purchased rights of use cover only this dataset. In both options, please contact Dr. Thepchai Supnithi via [email protected] for more information. ### Citation Information ``` @article{boonkwan2020annotation, title={The Annotation Guideline of LST20 Corpus}, author={Boonkwan, Prachya and Luantangsrisuk, Vorapon and Phaholphinyo, Sitthaa and Kriengket, Kanyanat and Leenoi, Dhanon and Phrombut, Charun and Boriboon, Monthika and Kosawat, Krit and Supnithi, Thepchai}, journal={arXiv preprint arXiv:2008.05055}, year={2020} } ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.

lst20

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["th"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition", "part-of-speech"], "pretty_name": "LST20", "tags": ["word-segmentation", "clause-segmentation", "sentence-segmentation"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "fname", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "NN", "1": "VV", "2": "PU", "3": "CC", "4": "PS", "5": "AX", "6": "AV", "7": "FX", "8": "NU", "9": "AJ", "10": "CL", "11": "PR", "12": "NG", "13": "PA", "14": "XX", "15": "IJ"}}}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B_BRN", "2": "B_DES", "3": "B_DTM", "4": "B_LOC", "5": "B_MEA", "6": "B_NUM", "7": "B_ORG", "8": "B_PER", "9": "B_TRM", "10": "B_TTL", "11": "I_BRN", "12": "I_DES", "13": "I_DTM", "14": "I_LOC", "15": "I_MEA", "16": "I_NUM", "17": "I_ORG", "18": "I_PER", "19": "I_TRM", "20": "I_TTL", "21": "E_BRN", "22": "E_DES", "23": "E_DTM", "24": "E_LOC", "25": "E_MEA", "26": "E_NUM", "27": "E_ORG", "28": "E_PER", "29": "E_TRM", "30": "E_TTL"}}}}, {"name": "clause_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B_CLS", "2": "I_CLS", "3": "E_CLS"}}}}], "config_name": "lst20", "splits": [{"name": "train", "num_bytes": 107725145, "num_examples": 63310}, {"name": "validation", "num_bytes": 9646167, "num_examples": 5620}, {"name": "test", "num_bytes": 8217425, "num_examples": 5250}], "download_size": 0, "dataset_size": 125588737}}

2024-01-18T11:08:24+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #task_ids-part-of-speech #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Thai #license-other #word-segmentation #clause-segmentation #sentence-segmentation #region-us

Dataset Card for LST20 ====================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Leaderboard: * Point of Contact: email ### Dataset Summary LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand. It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries. At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with 16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is considered large enough for developing joint neural models for NLP. Manually download at URL See 'LST20 Annotation URL' and 'LST20 Brief URL' within the downloaded 'URL' for more details. ### Supported Tasks and Leaderboards * POS tagging * NER tagging * clause segmentation * sentence segmentation * word tokenization ### Languages Thai Dataset Structure ----------------- ### Data Instances ### Data Fields * 'id': nth sentence in each set, starting at 0 * 'fname': text file from which the sentence comes from * 'tokens': word tokens * 'pos\_tags': POS tags * 'ner\_tags': NER tags * 'clause\_tags': clause tags ### Data Splits ※ Breaking space = space that is used as a sentence boundary marker ※※ Non-breaking space = space that is not used as a sentence boundary marker Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? Respective authors of the news articles ### Annotations #### Annotation process Detailed annotation guideline can be found in 'LST20 Annotation URL'. #### Who are the annotators? ### Personal and Sensitive Information All texts are from public news. No personal and sensitive information is expected to be included. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset * Large-scale Thai NER & POS tagging, clause & sentence segmentatation, word tokenization ### Discussion of Biases * All 3,745 texts are from news domain: + politics: 841 + crime and accident: 592 + economics: 512 + entertainment: 472 + sports: 402 + international: 279 + science, technology and education: 216 + health: 92 + general: 75 + royal: 54 + disaster: 52 + development: 45 + environment: 40 + culture: 40 + weather forecast: 33 * Word tokenization is done accoding to Inter­BEST 2009 Guideline. ### Other Known Limitations * Some NER tags do not correspond with given labels ('B', 'I', and so on) Additional Information ---------------------- ### Dataset Curators NECTEC ### Licensing Information 1. Non-commercial use, research, and open source Any non-commercial use of the dataset for research and open-sourced projects is encouraged and free of charge. Please cite our technical report for reference. If you want to perpetuate your models trained on our dataset and share them to the research community in Thailand, please send your models, code, and APIs to the AI for Thai Project. Please contact Dr. Thepchai Supnithi via thepchai@URL for more information. Note that modification and redistribution of the dataset by any means are strictly prohibited unless authorized by the corpus authors. 2. Commercial use In any commercial use of the dataset, there are two options. * Option 1 (in kind): Contributing a dataset of 50,000 words completely annotated with our annotation scheme within 1 year. Your data will also be shared and recognized as a dataset co-creator in the research community in Thailand. * Option 2 (in cash): Purchasing a lifetime license for the entire dataset is required. The purchased rights of use cover only this dataset. In both options, please contact Dr. Thepchai Supnithi via thepchai@URL for more information. ### Contributions Thanks to @cstorm125 for adding this dataset.

895227f5970ac99f45734814133280126818ca7a

# 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:** [Multilingual LAMA](http://cistern.cis.lmu.de/mlama/) - **Repository:** [Github](https://github.com/norakassner/mlama) - **Paper:** [Arxiv](https://arxiv.org/abs/2102.00894) - **Point of Contact:** [Contact section](http://cistern.cis.lmu.de/mlama/) ### Dataset Summary This dataset provides the data for mLAMA, a multilingual version of LAMA. Regarding LAMA see https://github.com/facebookresearch/LAMA. For mLAMA the TREx and GoogleRE part of LAMA was considered and machine translated using Google Translate, and the Wikidata and Google Knowledge Graph API. The machine translated templates were checked for validity, i.e., whether they contain exactly one '[X]' and one '[Y]'. This data can be used for creating fill-in-the-blank queries like "Paris is the capital of [MASK]" across 53 languages. For more details see the website http://cistern.cis.lmu.de/mlama/ or the github repo https://github.com/norakassner/mlama. ### Supported Tasks and Leaderboards Language model knowledge probing. ### Languages This dataset contains data in 53 languages: af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh ## Dataset Structure For each of the 53 languages and each of the 43 relations/predicates there is a set of triples. ### Data Instances For each language and relation there are triples, that consists of an object, a predicate and a subject. For each predicate there is a template available. An example for `dataset["test"][0]` is given here: ```python { 'language': 'af', 'lineid': 0, 'obj_label': 'Frankryk', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'President van Frankryk', 'sub_uri': 'Q191954', 'template': "[X] is 'n wettige term in [Y].", 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a' } ``` ### Data Fields Each instance has the following fields * "uuid": a unique identifier * "lineid": a identifier unique to mlama * "obj_id": knowledge graph id of the object * "obj_label": surface form of the object * "sub_id": knowledge graph id of the subject * "sub_label": surface form of the subject * "template": template * "language": language code * "predicate_id": relation id ### Data Splits There is only one partition that is labelled as 'test data'. ## Dataset Creation ### Curation Rationale The dataset was translated into 53 languages to investigate knowledge in pretrained language models multilingually. ### Source Data #### Initial Data Collection and Normalization The data has several sources: LAMA (https://github.com/facebookresearch/LAMA) licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) T-REx (https://hadyelsahar.github.io/t-rex/) licensed under Creative Commons Attribution-ShareAlike 4.0 International License Google-RE (https://github.com/google-research-datasets/relation-extraction-corpus) Wikidata (https://www.wikidata.org/) licensed under Creative Commons CC0 License and Creative Commons Attribution-ShareAlike License #### Who are the source language producers? See links above. ### Annotations #### Annotation process Crowdsourced (wikidata) and machine translated. #### Who are the annotators? Unknown. ### Personal and Sensitive Information Names of (most likely) famous people who have entries in Google Knowledge Graph or Wikidata. ## Considerations for Using the Data Data was created through machine translation and automatic processes. ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Not all triples are available in all languages. ## Additional Information ### Dataset Curators The authors of the mLAMA paper and the authors of the original datasets. ### Licensing Information The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/ ### Citation Information ``` @article{kassner2021multilingual, author = {Nora Kassner and Philipp Dufter and Hinrich Sch{\"{u}}tze}, title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained Language Models}, journal = {CoRR}, volume = {abs/2102.00894}, year = {2021}, url = {https://arxiv.org/abs/2102.00894}, archivePrefix = {arXiv}, eprint = {2102.00894}, timestamp = {Tue, 09 Feb 2021 13:35:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}, note = {to appear in EACL2021} } ``` ### Contributions Thanks to [@pdufter](https://github.com/pdufter) for adding this dataset.

m_lama

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language": ["af", "ar", "az", "be", "bg", "bn", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "ga", "gl", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "ko", "la", "lt", "lv", "ms", "nl", "pl", "pt", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "ta", "th", "tr", "uk", "ur", "vi", "zh"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["translation"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|lama"], "task_categories": ["question-answering", "text-classification"], "task_ids": ["open-domain-qa", "text-scoring"], "pretty_name": "MLama", "tags": ["probing"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "lineid", "dtype": "uint32"}, {"name": "obj_uri", "dtype": "string"}, {"name": "obj_label", "dtype": "string"}, {"name": "sub_uri", "dtype": "string"}, {"name": "sub_label", "dtype": "string"}, {"name": "template", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "predicate_id", "dtype": "string"}], "config_name": "all", "splits": [{"name": "test", "num_bytes": 125919995, "num_examples": 843143}], "download_size": 40772287, "dataset_size": 125919995}}

2024-01-18T11:08:27+00:00

TAGS #task_categories-question-answering #task_categories-text-classification #task_ids-open-domain-qa #task_ids-text-scoring #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #multilinguality-translation #size_categories-100K<n<1M #source_datasets-extended|lama #language-Afrikaans #language-Arabic #language-Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Catalan #language-Cebuano #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Irish #language-Galician #language-Hebrew #language-Hindi #language-Croatian #language-Hungarian #language-Armenian #language-Indonesian #language-Italian #language-Japanese #language-Georgian #language-Korean #language-Latin #language-Lithuanian #language-Latvian #language-Malay (macrolanguage) #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovak #language-Slovenian #language-Albanian #language-Serbian #language-Swedish #language-Tamil #language-Thai #language-Turkish #language-Ukrainian #language-Urdu #language-Vietnamese #language-Chinese #license-cc-by-nc-sa-4.0 #probing #arxiv-2102.00894 #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Multilingual LAMA - Repository: Github - Paper: Arxiv - Point of Contact: Contact section ### Dataset Summary This dataset provides the data for mLAMA, a multilingual version of LAMA. Regarding LAMA see URL For mLAMA the TREx and GoogleRE part of LAMA was considered and machine translated using Google Translate, and the Wikidata and Google Knowledge Graph API. The machine translated templates were checked for validity, i.e., whether they contain exactly one '[X]' and one '[Y]'. This data can be used for creating fill-in-the-blank queries like "Paris is the capital of [MASK]" across 53 languages. For more details see the website URL or the github repo URL ### Supported Tasks and Leaderboards Language model knowledge probing. ### Languages This dataset contains data in 53 languages: af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh ## Dataset Structure For each of the 53 languages and each of the 43 relations/predicates there is a set of triples. ### Data Instances For each language and relation there are triples, that consists of an object, a predicate and a subject. For each predicate there is a template available. An example for 'dataset["test"][0]' is given here: ### Data Fields Each instance has the following fields * "uuid": a unique identifier * "lineid": a identifier unique to mlama * "obj_id": knowledge graph id of the object * "obj_label": surface form of the object * "sub_id": knowledge graph id of the subject * "sub_label": surface form of the subject * "template": template * "language": language code * "predicate_id": relation id ### Data Splits There is only one partition that is labelled as 'test data'. ## Dataset Creation ### Curation Rationale The dataset was translated into 53 languages to investigate knowledge in pretrained language models multilingually. ### Source Data #### Initial Data Collection and Normalization The data has several sources: LAMA (URL licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) T-REx (URL licensed under Creative Commons Attribution-ShareAlike 4.0 International License Google-RE (URL Wikidata (URL licensed under Creative Commons CC0 License and Creative Commons Attribution-ShareAlike License #### Who are the source language producers? See links above. ### Annotations #### Annotation process Crowdsourced (wikidata) and machine translated. #### Who are the annotators? Unknown. ### Personal and Sensitive Information Names of (most likely) famous people who have entries in Google Knowledge Graph or Wikidata. ## Considerations for Using the Data Data was created through machine translation and automatic processes. ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Not all triples are available in all languages. ## Additional Information ### Dataset Curators The authors of the mLAMA paper and the authors of the original datasets. ### Licensing Information The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). URL ### Contributions Thanks to @pdufter for adding this dataset.

f93f27cf72f7f8bc0a1e21d38c8e8904dab7d3bc

# Dataset Card for Mac-Morpho ## 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:** [Mac-Morpho homepage](http://nilc.icmc.usp.br/macmorpho/) - **Repository:** [Mac-Morpho repository](http://nilc.icmc.usp.br/macmorpho/) - **Paper:** [Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese](https://journal-bcs.springeropen.com/articles/10.1186/s13173-014-0020-x) - **Point of Contact:** [Erick R Fonseca](mailto:[email protected]) ### Dataset Summary Mac-Morpho is a corpus of Brazilian Portuguese texts annotated with part-of-speech tags. Its first version was released in 2003 [1], and since then, two revisions have been made in order to improve the quality of the resource [2, 3]. The corpus is available for download split into train, development and test sections. These are 76%, 4% and 20% of the corpus total, respectively (the reason for the unusual numbers is that the corpus was first split into 80%/20% train/test, and then 5% of the train section was set aside for development). This split was used in [3], and new POS tagging research with Mac-Morpho is encouraged to follow it in order to make consistent comparisons possible. [1] Aluísio, S., Pelizzoni, J., Marchi, A.R., de Oliveira, L., Manenti, R., Marquiafável, V. 2003. An account of the challenge of tagging a reference corpus for brazilian portuguese. In: Proceedings of the 6th International Conference on Computational Processing of the Portuguese Language. PROPOR 2003 [2] Fonseca, E.R., Rosa, J.L.G. 2013. Mac-morpho revisited: Towards robust part-of-speech. In: Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology – STIL [3] Fonseca, E.R., Aluísio, Sandra Maria, Rosa, J.L.G. 2015. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese. Journal of the Brazilian Computer Society. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Portuguese ## Dataset Structure ### Data Instances An example from the Mac-Morpho dataset looks as follows: ``` { "id": "0", "pos_tags": [14, 19, 14, 15, 22, 7, 14, 9, 14, 9, 3, 15, 3, 3, 24], "tokens": ["Jersei", "atinge", "média", "de", "Cr$", "1,4", "milhão", "na", "venda", "da", "Pinhal", "em", "São", "Paulo", "."] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `pos`: the PoS tags of each token The PoS tags correspond to this list: ``` "PREP+PROADJ", "IN", "PREP+PRO-KS", "NPROP", "PREP+PROSUB", "KC", "PROPESS", "NUM", "PROADJ", "PREP+ART", "KS", "PRO-KS", "ADJ", "ADV-KS", "N", "PREP", "PROSUB", "PREP+PROPESS", "PDEN", "V", "PREP+ADV", "PCP", "CUR", "ADV", "PU", "ART" ``` ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | Train | Val | Test | | ------ | ----- | ----- | | 37948 | 1997 | 9987 | ## 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{fonseca2015evaluating, title={Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese}, author={Fonseca, Erick R and Rosa, Jo{\~a}o Lu{\'\i}s G and Alu{\'\i}sio, Sandra Maria}, journal={Journal of the Brazilian Computer Society}, volume={21}, number={1}, pages={2}, year={2015}, publisher={Springer} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.

mac_morpho

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["pt"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["part-of-speech"], "pretty_name": "Mac-Morpho", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "PREP+PROADJ", "1": "IN", "2": "PREP+PRO-KS", "3": "NPROP", "4": "PREP+PROSUB", "5": "KC", "6": "PROPESS", "7": "NUM", "8": "PROADJ", "9": "PREP+ART", "10": "KS", "11": "PRO-KS", "12": "ADJ", "13": "ADV-KS", "14": "N", "15": "PREP", "16": "PROSUB", "17": "PREP+PROPESS", "18": "PDEN", "19": "V", "20": "PREP+ADV", "21": "PCP", "22": "CUR", "23": "ADV", "24": "PU", "25": "ART"}}}}], "splits": [{"name": "train", "num_bytes": 12635011, "num_examples": 37948}, {"name": "test", "num_bytes": 3095292, "num_examples": 9987}, {"name": "validation", "num_bytes": 671356, "num_examples": 1997}], "download_size": 2463485, "dataset_size": 16401659}}

2024-01-18T11:08:30+00:00

TAGS #task_categories-token-classification #task_ids-part-of-speech #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Portuguese #license-cc-by-4.0 #region-us

Dataset Card for Mac-Morpho =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: Mac-Morpho homepage * Repository: Mac-Morpho repository * Paper: Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese * Point of Contact: Erick R Fonseca ### Dataset Summary Mac-Morpho is a corpus of Brazilian Portuguese texts annotated with part-of-speech tags. Its first version was released in 2003 [1], and since then, two revisions have been made in order to improve the quality of the resource [2, 3]. The corpus is available for download split into train, development and test sections. These are 76%, 4% and 20% of the corpus total, respectively (the reason for the unusual numbers is that the corpus was first split into 80%/20% train/test, and then 5% of the train section was set aside for development). This split was used in [3], and new POS tagging research with Mac-Morpho is encouraged to follow it in order to make consistent comparisons possible. [1] Aluísio, S., Pelizzoni, J., Marchi, A.R., de Oliveira, L., Manenti, R., Marquiafável, V. 2003. An account of the challenge of tagging a reference corpus for brazilian portuguese. In: Proceedings of the 6th International Conference on Computational Processing of the Portuguese Language. PROPOR 2003 [2] Fonseca, E.R., Rosa, J.L.G. 2013. Mac-morpho revisited: Towards robust part-of-speech. In: Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology – STIL [3] Fonseca, E.R., Aluísio, Sandra Maria, Rosa, J.L.G. 2015. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese. Journal of the Brazilian Computer Society. ### Supported Tasks and Leaderboards ### Languages Portuguese Dataset Structure ----------------- ### Data Instances An example from the Mac-Morpho dataset looks as follows: ### Data Fields * 'id': id of the sample * 'tokens': the tokens of the example text * 'pos': the PoS tags of each token The PoS tags correspond to this list: ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: Train: 37948, Val: 1997, Test: 9987 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @jonatasgrosman for adding this dataset.

bda72bd36a4460f11eab7cdf2b4d71a5093fbcad

# Dataset Card for makhzan ## 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://matnsaz.net/en/makhzan - **Repository:** https://github.com/zeerakahmed/makhzan - **Paper:** [More Information Needed] - **Leaderboard:** [More Information Needed] - **Point of Contact:** Zeerak Ahmed ### Dataset Summary An Urdu text corpus for machine learning, natural language processing and linguistic analysis. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages ur ## Dataset Structure ### Data Instances ``` { "contains-non-urdu-languages": "No", "document_body": " <body> <section> <p>بنگلہ دیش کی عدالتِ عالیہ نے طلاق کے ایک مقدمے کا فیصلہ کرتے ہوئے علما کے فتووں کو غیر قانونی قرار دیا ہے۔ عدالت نے پارلیمنٹ سے یہ درخواست کی ہے کہ وہ جلد ایسا قانون وضع کرے کہ جس کے بعد فتویٰ بازی قابلِ دست اندازیِ پولیس جرم بن جائے۔ بنگلہ دیش کے علما نے اس فیصلے پر بھر پور ردِ عمل ظاہرکرتے ہوئے اس کے خلاف ملک گیر تحریک چلانے کا اعلان کیا ہے۔ اس ضمن میں علما کی ایک تنظیم ”اسلامک یونٹی الائنس“ نے متعلقہ ججوں کو مرتد یعنی دین سے منحرف اور دائرۂ اسلام سے خارج قرار دیا ہے۔</p> <p>فتوے کا لفظ دو موقعوں پر استعمال ہوتا ہے۔ ایک اس موقع پر جب کوئی صاحبِ علم شریعت کے کسی مئلے کے بارے میں اپنی رائے پیش کرتا ہے۔ دوسرے اس موقع پر جب کوئی عالمِ دین کسی خاص واقعے کے حوالے سے اپنا قانونی فیصلہ صادر کرتا ہے۔ ایک عرصے سے ہمارے علما کے ہاں اس دوسرے موقعِ استعمال کا غلبہ ہو گیا ہے۔ اس کا نتیجہ یہ نکلا ہے کہ اس لفظ کا رائے یا نقطۂ نظر کے مفہوم میں استعمال کم و بیش متروک ہو گیا ہے۔ چنانچہ اب فتوے کا مطلب ہی علما کی طرف سے کسی خاص مألے یا واقعے کے بارے میں حتمی فیصلے کا صدور سمجھا جاتا ہے۔ علما اسی حیثیت سے فتویٰ دیتے ہیں اور عوام الناس اسی اعتبار سے اسے قبول کرتے ہیں۔ اس صورتِ حال میں ہمارے نزدیک، چند مسائل پیدا ہوتے ہیں۔ اس سے پہلے کہ ہم مذکورہ فیصلے کے بارے میں اپنا تاثر بیان کریں، یہ ضروری معلوم ہوتا ہے کہ مختصر طور پر ان مسائل کا جائزہ لے لیا جائے۔</p> <p>پہلا مألہ یہ پیدا ہوتا ہے کہ قانون سازی اور شرعی فیصلوں کا اختیار ایسے لوگوں کے ہاتھ میں آجاتا ہے جو قانون کی رو سے اس کے مجاز ہی نہیں ہوتے۔ کسی میاں بیوی کے مابین طلاق کے مألے میں کیا طلاق واقع ہوئی ہے یا نہیں ہوئی؟ ان کا نکاح قائم ہے یا باطل ہو گیا ہے؟ رمضان یا عید کا چاند نظر آیا ہے یا نہیں آیا؟کوئی مسلمان اپنے کسی قول یا اقدام کی وجہ سے کہیں دائرۂ اسلام سے خارج اورنتیجۃً مسلم شہریت کے قانونی حقوق سے محروم تو نہیں ہو گیا؟ یہ اور اس نوعیت کے بہت سے دوسرے معاملات سر تا سر قانون اور عدالت سے متعلق ہوتے ہیں۔ علما کی فتویٰ سازی کے نتیجے میںیہ امور گویا حکومت اورعدلیہ کے ہاتھ سے نکل کر غیر متعلق افراد کے ہاتھوں میں آجاتے ہیں۔</p> <p>دوسرا مألہ یہ پیدا ہوتا ہے کہ قانون کی حاکمیت کا تصور مجروح ہوتا ہے اور لوگوں میں قانون سے روگردانی کے رجحانات کو تقویت ملتی ہے۔ اس کی وجہ یہ ہے کہ قانون اپنی روح میں نفاذ کا متقاضی ہوتا ہے۔ اگر اسے نفاذ سے محروم رکھا جائے تو اس کی حیثیت محض رائے اور نقطۂ نظر کی سی ہوتی ہے۔ غیر مجاز فرد سے صادر ہونے والا فتویٰ یا قانون حکومت کی قوتِ نافذہ سے محروم ہوتا ہے۔ اس کی خلاف ورزی پر کسی قسم کی سزا کا خوف نہیں ہوتا۔ چنانچہ فتویٰ اگر مخاطب کی پسند کے مطابق نہ ہو تو اکثر وہ اسے ماننے سے انکار کر دیتا ہے۔ اس طرح وہ فتویٰ یا قانون بے توقیر ہوتا ہے۔ ایسے ماحول میں رہنے والے شہریوں میں قانون ناپسندی کا رجحان فروغ پاتا ہے اور جیسے ہی انھیں موقع ملتا ہے وہ بے دریغ قانون کی خلاف ورزی کر ڈالتے ہیں۔</p> <p>تیسرامسئلہ یہ پیدا ہوتا ہے کہ اگرغیر مجاز افراد سے صادر ہونے والے فیصلوں کو نافذ کرنے کی کوشش کی جائے تو ملک میں بد نظمی اور انارکی کا شدید اندیشہ پیدا ہو جاتا ہے۔ جب غیر مجازافراد سے صادر ہونے والے قانونی فیصلوں کو حکومتی سرپرستی کے بغیر نافذ کرنے کی کوشش کی جاتی ہے تو اپنے عمل سے یہ اس بات کا اعلان ہوتا ہے کہ مرجعِ قانون و اقتدارتبدیل ہو چکا ہے۔ جب کوئی عالمِ دین مثال کے طور پر، یہ فتویٰ صادر کرتا ہے کہ سینما گھروں اور ٹی وی اسٹیشنوں کو مسمار کرنامسلمانوں کی ذمہ داری ہے، یا کسی خاص قوم کے خلاف جہاد فرض ہو چکا ہے، یا فلاں کی دی گئی طلاق واقع ہو گئی ہے اور فلاں کی نہیں ہوئی، یا فلاں شخص یا گروہ اپنا اسلامی تشخص کھو بیٹھا ہے تو وہ درحقیقت قانونی فیصلہ جاری کر رہا ہوتا ہے۔ دوسرے الفاظ میں، وہ ریاست کے اندر اپنی ایک الگ ریاست بنانے کا اعلان کر رہا ہوتا ہے۔ اس کا نتیجہ سوائے انتشار اور انارکی کے اور کچھ نہیں نکلتا۔ یہی وجہ ہے کہ جن علاقوں میں حکومت کی گرفت کمزور ہوتی ہے وہاں اس طرح کے فیصلوں کا نفاذ بھی ہو جاتا ہے اور حکومت منہ دیکھتی رہتی ہے۔</p> <p>چوتھا مسئلہ یہ پیدا ہوتا ہے کہ مختلف مذہبی مسالک کی وجہ سے ایک ہی معاملے میں مختلف اور متضاد فتوے منظرِ عام پر آتے ہیں۔ یہ تو ہمارے روز مرہ کی بات ہے کہ ایک ہی گروہ کو بعض علماے دین کافر قرار دیتے ہیں اور بعض مسلمان سمجھتے ہیں۔ کسی شخص کے منہ سے اگر ایک موقع پر طلاق کے الفاظ تین بار نکلتے ہیں تو بعض علما اس پر ایک طلاق کا حکم لگا کر رجوع کا حق باقی رکھتے ہیں اور بعض تین قرار دے کررجوع کو باطل قرار دیتے ہیں۔ یہ صورتِ حال ایک عام آدمی کے لیے نہایت دشواریاں پیدا کر دیتی ہے۔</p> <p>پانچواں مسئلہ یہ پیدا ہوتا ہے کہ حکمران اگر دین و شریعت سے کچھ خاص دلچسپی نہ رکھتے ہوں تو وہ اس صورتِ حال میں شریعت کی روشنی میں قانون سازی کی طرف متوجہ نہیں ہوتے۔ کام چل رہا ہے کے اصول پر وہ اس طریقِ قانون سازی سے سمجھوتاکیے رہتے ہیں۔ اس کا نتیجہ یہ نکلتا ہے کہ حکومتی ادارے ضروری قانون سازی کے بارے میں بے پروائی کا رویہ اختیار کرتے ہیں اور قوانین اپنے فطری ارتقا سے محروم رہتے ہیں۔</p> <p>چھٹا مألہ یہ پیدا ہوتا ہے کہ رائج الوقت قانون اور عدالتوں کی توہین کے امکانات پیدا ہو جاتے ہیں۔ جب کسی مسئلے میں عدالتیں اپنا فیصلہ سنائیں اور علما اسے باطل قرار دیتے ہوئے اس کے برعکس اپنا فیصلہ صادر کریں تو اس سے عدالتوں کا وقار مجروح ہوتا ہے۔ اس کا مطلب یہ ہوتا ہے کہ کوئی شہری عدلیہ کو چیلنج کرنے کے لیے کھڑا ہو گیا ہے۔</p> <p>ان مسائل کے تناظر میں بنگلہ دیش کی عدالتِ عالیہ کا فیصلہ ہمارے نزدیک، امت کی تاریخ میں ایک عظیم فیصلہ ہے۔ جناب جاوید احمد صاحب غامدی نے اسے بجا طور پر صدی کا بہترین فیصلہ قرار دیا ہے۔ بنگلہ دیش کی عدالت اگر علما کے فتووں اور قانونی فیصلوں پر پابندی لگانے کے بجائے، ان کے اظہارِ رائے پر پابندی عائدکرتی تو ہم اسے صدی کا بدترین فیصلہ قرار دیتے اور انھی صفحات میں بے خوفِ لومۃ و لائم اس پر نقد کر رہے ہوتے۔</p> <p>موجودہ زمانے میں امتِ مسلمہ کا ایک بڑا المیہ یہ ہے کہ اس کے علما اپنی اصل ذمہ داری کو ادا کرنے کے بجائے ان ذمہ داریوں کو ادا کرنے پر مصر ہیں جن کے نہ وہ مکلف ہیں اور نہ اہل ہیں۔ قرآن و سنت کی رو سے علما کی اصل ذمہ داری دعوت و تبلیغ، انذار و تبشیر اور تعلیم و تحقیق ہے۔ ان کا کام سیاست نہیں، بلکہ سیاست دانوں کو دین کی رہنمائی سے آگاہی ہے؛ ان کا کام حکومت نہیں، بلکہ حکمرانوں کی اصلاح کی کوشش ہے؛ ان کا کام جہاد و قتال نہیں، بلکہ جہادکی تعلیم اور جذبۂ جہاد کی بیداری ہے؛ اسی طرح ان کا کام قانون سازی اور فتویٰ بازی نہیں بلکہ تحقیق و اجتہاد ہے۔ گویا انھیں قرآنِ مجیدکامفہوم سمجھنے، سنتِ ثابتہ کا مدعا متعین کرنے اور قولِ پیغمبر کا منشامعلوم کرنے کے لیے تحقیق کرنی ہے اور جن امور میں قرآن و سنت خاموش ہیں ان میں اپنی عقل و بصیرت سے اجتہادی آراقائم کرنی ہیں۔ ان کی کسی تحقیق یا اجتہاد کو جب عدلیہ یا پارلیمنٹ قبول کرے گی تو وہ قانون قرار پائے گا۔ اس سے پہلے اس کی حیثیت محض ایک رائے کی ہوگی۔ اس لیے اسے اسی حیثیت سے پیش کیا جائے گا۔</p> <p>اس کا مطلب یہ ہے کہ کوئی حکم نہیں لگایا جائے گا، کوئی فیصلہ نہیں سنایا جائے گا، کوئی فتویٰ نہیں دیا جائے گا، بلکہ طالبِ علمانہ لب و لہجے میں محض علم و استدلال کی بنا پر اپنا نقطۂ نظر پیش کیا جائے گا۔ یہ نہیں کہا جائے گا کہ فلاں شخص کافر ہے، بلکہ اس کی اگر ضرورت پیش آئے تو یہ کہا جائے گا کہ فلاں شخص کا فلاں عقیدہ کفر ہے۔ یہ نہیں کہا جائے گا کہ فلاں آدمی دائرۂ اسلام سے خارج ہو گیا ہے، بلکہ یہ کہا جائے گا کہ فلاں آدمی کا فلاں نقطۂ نظر اسلام کے دائرے میں نہیں آتا۔ یہ نہیں کہا جائے گا فلاں آدمی مشرک ہے، بلکہ یہ کہا جائے گا فلاں نظریہ یا فلاں طرزِ عمل شرک ہے۔ یہ نہیں کہا جائے گا کہ زید کی طرف سے دی گئی ایک وقت کی تین طلاقیں واقع ہو گئی ہیں، بلکہ یہ کہا جائے گا کہ ایک وقت کی تین طلاقیں واقع ہو نی چاہییں۔</p> <p>حکم لگانا، فیصلہ سنانا، قانون وضع کرنا اورفتویٰ جاری کرنا درحقیقت، عدلیہ اور حکومت کا کام ہے کسی عالمِ دین یا کسی اور غیر مجاز فرد کی طرف سے اس کام کو انجام دینے کی کوشش سراسر تجاوز ہے۔ خلافتِ راشدہ کے زمانے میں اس اصول کو ہمیشہ ملحوظ رکھا گیا۔ شاہ ولی اللہ محدث دہلوی اپنی کتاب ”ازالتہ الخفا ء“ میں لکھتے ہیں:</p> <blockquote> <p>”اس زمانے تک وعظ اور فتویٰ خلیفہ کی رائے پر موقوف تھا۔ خلیفہ کے حکم کے بغیر نہ وعظ کہتے تھے اور نہ فتویٰ دیتے تھے۔ بعد میں خلیفہ کے حکم کے بغیر وعظ کہنے اور فتویٰ دینے لگے اور فتویٰ کے معاملے میں جماعت (مجلسِ شوریٰ) کے مشورہ کی جو صورت پہلے تھی وہ باقی نہ رہی——- (اس زمانے میں) جب کوئی اختلافی صورت نمودار ہوتی، خلیفہ کے سامنے معاملہ پیش کرتے، خلیفہ اہلِ علم و تقویٰ سے مشورہ کرنے کے بعد ایک رائے قائم کرتا اور وہی سب لوگوں کی رائے بن جاتی۔ حضرت عثمان کی شہادت کے بعد ہر عالم بطورِ خود فتویٰ دینے لگا اور اس طرح مسلمانوں میں اختلاف برپا ہوا۔“ (بحوالہ ”اسلامی ریاست میں فقہی اختلافات کا حل“، مولاناامین احسن اصلاحی، ص۳۲)</p> </blockquote> </section> </body> ", "file_id": "0001.xml", "metadata": " <meta> <title>بنگلہ دیش کی عدالت کا تاریخی فیصلہ</title> <author> <name>سید منظور الحسن</name> <gender>Male</gender> </author> <publication> <name>Mahnama Ishraq February 2001</name> <year>2001</year> <city>Lahore</city> <link>https://www.javedahmedghamidi.org/#!/ishraq/5adb7341b7dd1138372db999?articleId=5adb7452b7dd1138372dd6fb&amp;year=2001&amp;decade=2000</link> <copyright-holder>Al-Mawrid</copyright-holder> </publication> <num-words>1694</num-words> <contains-non-urdu-languages>No</contains-non-urdu-languages> </meta> ", "num-words": 1694, "title": "بنگلہ دیش کی عدالت کا تاریخی فیصلہ" } ``` ### Data Fields ```file_id (str)```: Document file_id corresponding to filename in repository. ```metadata(str)```: XML formatted string containing metadata on the document such as the document's title, information about the author and publication, as well as other potentially useful facts such as the number of Urdu words in the document and whether the document contains text in any other languages. ```title (str)```: Title of the document. ```num-words (int)```: Number of words in document. ```contains-non-urdu-languages (str)```: ```Yes``` if document contains words other than urdu, ```No``` otherwise. ```document_body```: XML formatted body of the document. Details below: The document is divided into ```<section>``` elements. In general the rule is that a clear visual demarkation in the original text (such as a page break, or a horizontal rule) is used to indicate a section break. A heading does not automatically create a new section. Each paragraph is a ```<p>``` element. Headings are wrapped in an ```<heading>``` element. Blockquotes are wrapped in a ```<blockquote>``` element. Blockquotes may themselves contain other elements. Lists are wrapped in an ```<list>```. Individual items in each list are wrapped in an ```<li>``` element. Poetic verses are wrapped in a ```<verse>``` element. Each verse is on a separate line but is not wrapped in an individual element. Tables are wrapped in a ```<table>``` element. A table is divided into rows marked by ```<tr>``` and columns marked by ```<td>```. Text not in the Urdu language is wrapped in an ```<annotation>``` tag (more below). ```<p>, <heading>, <li>, <td>``` and ```<annotation>``` tags are inline with the text (i.e. there is no new line character before and after the tag). Other tags have a new line after the opening and before the closing tag. Due to the use of XML syntax, ```<```, ```>``` and ```&``` characters have been escaped as ```&lt```;, ```&gt```;, and ```&amp```; respectively. This includes the use of these characters in URLs inside metadata. ### Data Splits All the data is in one split ```train``` ## Dataset Creation ### Curation Rationale All text in this repository has been selected for quality of language, upholding high editorial standards. Given the poor quality of most published Urdu text in digital form, this selection criteria allows the use of this text for natural language processing, and machine learning applications without the need to address fundamental quality issues in the text. We have made efforts to ensure this text is as broadly representative as possible. Specifically we have attempted to select for as many authors as possible, and diversity in the gender of the author, as well as years and city of publication. This effort is imperfect, and we appreciate any attempts at pointing us to resources that can help diversify this text further. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Makhzan has been started with generous initial donations of text from two renowned journals  Bunyad, from the Gurmani Center of Literature and Languages at the Lahore University of Management Sciences (LUMS), and Ishraq, from the Al-Mawrid Institute. This choice of sources allowed us to get a diversity of voices even in a small initial corpus, while ensuring the highest editorial standards available in published Urdu text. As a result your models can also maintain high linguistic standards. ### Annotations #### Annotation process Text is structured and annotated using XML syntax. The ontology of elements used is loosely based around HTML, with simplifications made when HTML's specificity is not needed, and additions made to express common occurences in this corpus that would be useful for linguistic analysis. The semantic tagging of text is editorial in nature, which is to say that another person semantically tagging the text may do so differently. Effort has been made however to ensure consistency, and to retain the original meaning of the text while making it easy to parse through linguistically different pieces of text for analysis. Annotations have been made inline using an ```<annotation>``` element. A language (lang) attribute is added to the ```<annotation>``` element to indicate text in other languages (such as quoted text or technical vocabulary presented in other languages and scripts). The attribute value a two-character ISO 639-1 code. So the resultant annotation for an Arabic quote for example, will be ```<annotation lang="ar"></annotation>```. A type (type) attributed is added to indicate text that is not in a language per se but is not Urdu text. URLs for example are wrapped in an ```<annotation type="url">``` tag. #### 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 A few of the files do not have valid XML and cannot be loaded. This issue is tracked [here](https://github.com/zeerakahmed/makhzan/issues/28) ## Additional Information ### Dataset Curators Zeerak Ahmed ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{makhzan, title={Maḵẖzan}, howpublished = "\url{https://github.com/zeerakahmed/makhzan/}", } ``` ### Contributions Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset.

makhzan

2022-03-02T23:29:22+00:00

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2024-01-18T11:08:32+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Urdu #license-other #region-us

# Dataset Card for makhzan ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: - Leaderboard: - Point of Contact: Zeerak Ahmed ### Dataset Summary An Urdu text corpus for machine learning, natural language processing and linguistic analysis. ### Supported Tasks and Leaderboards ### Languages ur ## Dataset Structure ### Data Instances ### Data Fields : Document file_id corresponding to filename in repository. : XML formatted string containing metadata on the document such as the document's title, information about the author and publication, as well as other potentially useful facts such as the number of Urdu words in the document and whether the document contains text in any other languages. : Title of the document. : Number of words in document. : if document contains words other than urdu, otherwise. : XML formatted body of the document. Details below: The document is divided into elements. In general the rule is that a clear visual demarkation in the original text (such as a page break, or a horizontal rule) is used to indicate a section break. A heading does not automatically create a new section. Each paragraph is a element. Headings are wrapped in an element. Blockquotes are wrapped in a element. Blockquotes may themselves contain other elements. Lists are wrapped in an . Individual items in each list are wrapped in an element. Poetic verses are wrapped in a element. Each verse is on a separate line but is not wrapped in an individual element. Tables are wrapped in a element. A table is divided into rows marked by and columns marked by . Text not in the Urdu language is wrapped in an tag (more below). and tags are inline with the text (i.e. there is no new line character before and after the tag). Other tags have a new line after the opening and before the closing tag. Due to the use of XML syntax, , and characters have been escaped as ;, ;, and ; respectively. This includes the use of these characters in URLs inside metadata. ### Data Splits All the data is in one split ## Dataset Creation ### Curation Rationale All text in this repository has been selected for quality of language, upholding high editorial standards. Given the poor quality of most published Urdu text in digital form, this selection criteria allows the use of this text for natural language processing, and machine learning applications without the need to address fundamental quality issues in the text. We have made efforts to ensure this text is as broadly representative as possible. Specifically we have attempted to select for as many authors as possible, and diversity in the gender of the author, as well as years and city of publication. This effort is imperfect, and we appreciate any attempts at pointing us to resources that can help diversify this text further. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? Makhzan has been started with generous initial donations of text from two renowned journals  Bunyad, from the Gurmani Center of Literature and Languages at the Lahore University of Management Sciences (LUMS), and Ishraq, from the Al-Mawrid Institute. This choice of sources allowed us to get a diversity of voices even in a small initial corpus, while ensuring the highest editorial standards available in published Urdu text. As a result your models can also maintain high linguistic standards. ### Annotations #### Annotation process Text is structured and annotated using XML syntax. The ontology of elements used is loosely based around HTML, with simplifications made when HTML's specificity is not needed, and additions made to express common occurences in this corpus that would be useful for linguistic analysis. The semantic tagging of text is editorial in nature, which is to say that another person semantically tagging the text may do so differently. Effort has been made however to ensure consistency, and to retain the original meaning of the text while making it easy to parse through linguistically different pieces of text for analysis. Annotations have been made inline using an element. A language (lang) attribute is added to the element to indicate text in other languages (such as quoted text or technical vocabulary presented in other languages and scripts). The attribute value a two-character ISO 639-1 code. So the resultant annotation for an Arabic quote for example, will be . A type (type) attributed is added to indicate text that is not in a language per se but is not Urdu text. URLs for example are wrapped in an tag. #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations A few of the files do not have valid XML and cannot be loaded. This issue is tracked here ## Additional Information ### Dataset Curators Zeerak Ahmed ### Licensing Information ### Contributions Thanks to @arkhalid for adding this dataset.

9bd2e574063961eed8a503e92349f41ef9b7041d

# Dataset Card for MasakhaNER ## 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:** [homepage](https://github.com/masakhane-io/masakhane-ner) - **Repository:** [github](https://github.com/masakhane-io/masakhane-ner) - **Paper:** [paper](https://arxiv.org/abs/2103.11811) - **Point of Contact:** [Masakhane](https://www.masakhane.io/) or [email protected] ### Dataset Summary MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages. Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] . MasakhaNER is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for ten African languages: - Amharic - Hausa - Igbo - Kinyarwanda - Luganda - Luo - Nigerian-Pidgin - Swahili - Wolof - Yoruba The train/validation/test sets are available for all the ten languages. For more details see https://arxiv.org/abs/2103.11811 ### Supported Tasks and Leaderboards [More Information Needed] - `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data. ### Languages There are ten languages available : - Amharic (amh) - Hausa (hau) - Igbo (ibo) - Kinyarwanda (kin) - Luganda (kin) - Luo (luo) - Nigerian-Pidgin (pcm) - Swahili (swa) - Wolof (wol) - Yoruba (yor) ## Dataset Structure ### Data Instances The examples look like this for Yorùbá: ``` from datasets import load_dataset data = load_dataset('masakhaner', 'yor') # Please, specify the language code # A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [B-DATE, I-DATE, 0, 0, 0, 0, 0, B-PER, I-PER, I-PER, O, O, O, O], 'tokens': ['Wákàtí', 'méje', 'ti', 'ré', 'kọjá', 'lọ', 'tí', 'Luis', 'Carlos', 'Díaz', 'ti', 'di', 'awati', '.'] } ``` ### 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: ``` "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE", ``` In the NER tags, 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 dates & time (DATE). It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked. ### Data Splits For all languages, there are three splits. The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits. The splits have the following sizes : | Language | train | validation | test | |-----------------|------:|-----------:|-----:| | Amharic | 1750 | 250 | 500 | | Hausa | 1903 | 272 | 545 | | Igbo | 2233 | 319 | 638 | | Kinyarwanda | 2110 | 301 | 604 | | Luganda | 2003 | 200 | 401 | | Luo | 644 | 92 | 185 | | Nigerian-Pidgin | 2100 | 300 | 600 | | Swahili | 2104 | 300 | 602 | | Wolof | 1871 | 267 | 536 | | Yoruba | 2124 | 303 | 608 | ## Dataset Creation ### Curation Rationale The dataset was introduced to introduce new resources to ten languages that were under-served for natural language processing. [More Information Needed] ### Source Data The source of the data is from the news domain, details can be found here https://arxiv.org/abs/2103.11811 #### Initial Data Collection and Normalization The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable. #### Who are the source language producers? The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above. ### Annotations #### Annotation process Details can be found here https://arxiv.org/abs/2103.11811 #### Who are the annotators? Annotators were recruited from [Masakhane](https://www.masakhane.io/) ### Personal and Sensitive Information The data is sourced from newspaper source and only contains mentions of public figures or individuals ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains. ## Additional Information ### Dataset Curators ### Licensing Information The licensing status of the data is CC 4.0 Non-Commercial ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @article{Adelani2021MasakhaNERNE, title={MasakhaNER: Named Entity Recognition for African Languages}, author={D. Adelani and Jade Abbott and Graham Neubig and Daniel D'Souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and S. Muhammad and Chris C. Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and J. Alabi and Seid Muhie Yimam and Tajuddeen R. Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and V. Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin P. Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and C. Chukwuneke and N. Odu and Eric Peter Wairagala and S. Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane Mboup and D. Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima Diop and A. Diallo and Adewale Akinfaderin and T. Marengereke and Salomey Osei}, journal={ArXiv}, year={2021}, volume={abs/2103.11811} } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.

masakhaner

2022-03-02T23:29:22+00:00

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2024-01-18T11:08:34+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Amharic #language-Hausa #language-Igbo #language-Ganda #language-Luo (Kenya and Tanzania) #language-Nigerian Pidgin #language-Kinyarwanda #language-Swahili (macrolanguage) #language-Wolof #language-Yoruba #license-unknown #arxiv-2103.11811 #region-us

Dataset Card for MasakhaNER =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: homepage * Repository: github * Paper: paper * Point of Contact: Masakhane or didelani@URL ### Dataset Summary MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages. Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] . MasakhaNER is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for ten African languages: * Amharic * Hausa * Igbo * Kinyarwanda * Luganda * Luo * Nigerian-Pidgin * Swahili * Wolof * Yoruba The train/validation/test sets are available for all the ten languages. For more details see URL ### Supported Tasks and Leaderboards * 'named-entity-recognition': The performance in this task is measured with F1 (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data. ### Languages There are ten languages available : * Amharic (amh) * Hausa (hau) * Igbo (ibo) * Kinyarwanda (kin) * Luganda (kin) * Luo (luo) * Nigerian-Pidgin (pcm) * Swahili (swa) * Wolof (wol) * Yoruba (yor) Dataset Structure ----------------- ### Data Instances The examples look like this for Yorùbá: ### 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: In the NER tags, 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 dates & time (DATE). It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked. ### Data Splits For all languages, there are three splits. The original splits were named 'train', 'dev' and 'test' and they correspond to the 'train', 'validation' and 'test' splits. The splits have the following sizes : Dataset Creation ---------------- ### Curation Rationale The dataset was introduced to introduce new resources to ten languages that were under-served for natural language processing. ### Source Data The source of the data is from the news domain, details can be found here URL #### Initial Data Collection and Normalization The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable. #### Who are the source language producers? The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above. ### Annotations #### Annotation process Details can be found here URL #### Who are the annotators? Annotators were recruited from Masakhane ### Personal and Sensitive Information The data is sourced from newspaper source and only contains mentions of public figures or individuals Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains. Additional Information ---------------------- ### Dataset Curators ### Licensing Information The licensing status of the data is CC 4.0 Non-Commercial Provide the BibTex-formatted reference for the dataset. For example: ### Contributions Thanks to @dadelani for adding this dataset.

3ed8be297f648ac5f1829e86bab96f6ac1834442

# Dataset Card for "math_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://github.com/deepmind/mathematics_dataset](https://github.com/deepmind/mathematics_dataset) - **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:** 130.65 GB - **Size of the generated dataset:** 9.08 GB - **Total amount of disk used:** 139.73 GB ### Dataset Summary Mathematics database. This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models. Original paper: Analysing Mathematical Reasoning Abilities of Neural Models (Saxton, Grefenstette, Hill, Kohli). Example usage: train_examples, val_examples = datasets.load_dataset( 'math_dataset/arithmetic__mul', split=['train', 'test'], as_supervised=True) ### 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 #### algebra__linear_1d - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 92.60 MB - **Total amount of disk used:** 2.43 GB An example of 'train' looks as follows. ``` ``` #### algebra__linear_1d_composed - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 200.58 MB - **Total amount of disk used:** 2.53 GB An example of 'train' looks as follows. ``` ``` #### algebra__linear_2d - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 127.41 MB - **Total amount of disk used:** 2.46 GB An example of 'train' looks as follows. ``` ``` #### algebra__linear_2d_composed - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 235.59 MB - **Total amount of disk used:** 2.57 GB An example of 'train' looks as follows. ``` ``` #### algebra__polynomial_roots - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 164.01 MB - **Total amount of disk used:** 2.50 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### algebra__linear_1d - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__linear_1d_composed - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__linear_2d - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__linear_2d_composed - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__polynomial_roots - `question`: a `string` feature. - `answer`: a `string` feature. ### Data Splits | name | train |test | |---------------------------|------:|----:| |algebra__linear_1d |1999998|10000| |algebra__linear_1d_composed|1999998|10000| |algebra__linear_2d |1999998|10000| |algebra__linear_2d_composed|1999998|10000| |algebra__polynomial_roots |1999998|10000| ## 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{2019arXiv, author = {Saxton, Grefenstette, Hill, Kohli}, title = {Analysing Mathematical Reasoning Abilities of Neural Models}, year = {2019}, journal = {arXiv:1904.01557} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

math_dataset

2022-03-02T23:29:22+00:00

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"num_examples": 1999998}], "download_size": 2333082954, "dataset_size": 216963167}]}

2024-01-18T11:08:35+00:00

TAGS #language-English #region-us

Dataset Card for "math\_dataset" ================================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 130.65 GB * Size of the generated dataset: 9.08 GB * Total amount of disk used: 139.73 GB ### Dataset Summary Mathematics database. This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models. Original paper: Analysing Mathematical Reasoning Abilities of Neural Models (Saxton, Grefenstette, Hill, Kohli). Example usage: train\_examples, val\_examples = datasets.load\_dataset( 'math\_dataset/arithmetic\_\_mul', split=['train', 'test'], as\_supervised=True) ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### algebra\_\_linear\_1d * Size of downloaded dataset files: 2.33 GB * Size of the generated dataset: 92.60 MB * Total amount of disk used: 2.43 GB An example of 'train' looks as follows. #### algebra\_\_linear\_1d\_composed * Size of downloaded dataset files: 2.33 GB * Size of the generated dataset: 200.58 MB * Total amount of disk used: 2.53 GB An example of 'train' looks as follows. #### algebra\_\_linear\_2d * Size of downloaded dataset files: 2.33 GB * Size of the generated dataset: 127.41 MB * Total amount of disk used: 2.46 GB An example of 'train' looks as follows. #### algebra\_\_linear\_2d\_composed * Size of downloaded dataset files: 2.33 GB * Size of the generated dataset: 235.59 MB * Total amount of disk used: 2.57 GB An example of 'train' looks as follows. #### algebra\_\_polynomial\_roots * Size of downloaded dataset files: 2.33 GB * Size of the generated dataset: 164.01 MB * Total amount of disk used: 2.50 GB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### algebra\_\_linear\_1d * 'question': a 'string' feature. * 'answer': a 'string' feature. #### algebra\_\_linear\_1d\_composed * 'question': a 'string' feature. * 'answer': a 'string' feature. #### algebra\_\_linear\_2d * 'question': a 'string' feature. * 'answer': a 'string' feature. #### algebra\_\_linear\_2d\_composed * 'question': a 'string' feature. * 'answer': a 'string' feature. #### algebra\_\_polynomial\_roots * 'question': a 'string' feature. * 'answer': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patrickvonplaten, @lewtun, @thomwolf for adding this dataset.

c4f1cc784c04c4957b50c97858f23893b633eea6

# Dataset Card for MathQA ## 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://math-qa.github.io/math-QA/](https://math-qa.github.io/math-QA/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://aclanthology.org/N19-1245/) - **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:** 7.30 MB - **Size of the generated dataset:** 22.96 MB - **Total amount of disk used:** 30.27 MB ### Dataset Summary We introduce a large-scale dataset of math word problems. Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset with fully-specified operational programs. AQuA-RAT has provided the questions, options, rationale, and the correct options. ### 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:** 7.30 MB - **Size of the generated dataset:** 22.96 MB - **Total amount of disk used:** 30.27 MB An example of 'train' looks as follows. ``` { "Problem": "a multiple choice test consists of 4 questions , and each question has 5 answer choices . in how many r ways can the test be completed if every question is unanswered ?", "Rationale": "\"5 choices for each of the 4 questions , thus total r of 5 * 5 * 5 * 5 = 5 ^ 4 = 625 ways to answer all of them . answer : c .\"", "annotated_formula": "power(5, 4)", "category": "general", "correct": "c", "linear_formula": "power(n1,n0)|", "options": "a ) 24 , b ) 120 , c ) 625 , d ) 720 , e ) 1024" } ``` ### Data Fields The data fields are the same among all splits. #### default - `Problem`: a `string` feature. - `Rationale`: a `string` feature. - `options`: a `string` feature. - `correct`: a `string` feature. - `annotated_formula`: a `string` feature. - `linear_formula`: a `string` feature. - `category`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|29837| 4475|2985| ## 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 dataset is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @inproceedings{amini-etal-2019-mathqa, title = "{M}ath{QA}: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms", author = "Amini, Aida and Gabriel, Saadia and Lin, Shanchuan and Koncel-Kedziorski, Rik and Choi, Yejin and Hajishirzi, Hannaneh", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N19-1245", doi = "10.18653/v1/N19-1245", pages = "2357--2367", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

math_qa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|aqua_rat"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mathqa", "pretty_name": "MathQA", "dataset_info": {"features": [{"name": "Problem", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "options", "dtype": "string"}, {"name": "correct", "dtype": "string"}, {"name": "annotated_formula", "dtype": "string"}, {"name": "linear_formula", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1844184, "num_examples": 2985}, {"name": "train", "num_bytes": 18368826, "num_examples": 29837}, {"name": "validation", "num_bytes": 2752969, "num_examples": 4475}], "download_size": 7302821, "dataset_size": 22965979}}

2024-01-18T11:08:38+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|aqua_rat #language-English #license-apache-2.0 #region-us

Dataset Card for MathQA ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms * Point of Contact: * Size of downloaded dataset files: 7.30 MB * Size of the generated dataset: 22.96 MB * Total amount of disk used: 30.27 MB ### Dataset Summary We introduce a large-scale dataset of math word problems. Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset with fully-specified operational programs. AQuA-RAT has provided the questions, options, rationale, and the correct options. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 7.30 MB * Size of the generated dataset: 22.96 MB * Total amount of disk used: 30.27 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'Problem': a 'string' feature. * 'Rationale': a 'string' feature. * 'options': a 'string' feature. * 'correct': a 'string' feature. * 'annotated\_formula': a 'string' feature. * 'linear\_formula': a 'string' feature. * 'category': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is licensed under the Apache License, Version 2.0. ### Contributions Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.

949ede0ecd25f2825106f1dc5ccf971906733ade

# Dataset Card for "matinf" ## 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/WHUIR/MATINF](https://github.com/WHUIR/MATINF) - **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:** 0.00 MB - **Size of the generated dataset:** 795.00 MB - **Total amount of disk used:** 795.00 MB ### Dataset Summary MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF. ### 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 #### age_classification - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 48.39 MB - **Total amount of disk used:** 48.39 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "description": "\"6个月的时候去儿宝检查，医生说宝宝的分胯动作做的不好，说最好去儿童医院看看，但我家宝宝很好，感觉没有什么不正常啊，请教一下，分胯做的不好，有什么不好吗？\"...", "id": 88016, "label": 0, "question": "医生说宝宝的分胯动作不好" } ``` #### qa - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 268.69 MB - **Total amount of disk used:** 268.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "\"我一个同学的孩子就是发现了肾积水，治疗了一段时间，结果还是越来越多，没办法就打掉了。虽然舍不得，但是还是要忍痛割爱，不然以后孩子真的有问题，大人和孩子都受罪。不过，这个最后的决定还要你自己做，毕竟是你的宝宝。，、、、、\"...", "id": 536714, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` #### summarization - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 258.88 MB - **Total amount of disk used:** 258.88 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "description": "\"宝宝有中度HIE，但原因未查明，这是他出生后脸上红的几道，嘴唇深红近紫，请问这是像缺氧的表现吗？\"...", "id": 173649, "question": "宝宝脸上红的几道嘴唇深红近紫是像缺氧的表现吗？" } ``` #### topic_classification - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 219.04 MB - **Total amount of disk used:** 219.04 MB An example of 'train' looks as follows. ``` { "description": "媳妇怀孕五个月了经检查右侧肾积水、过了半月左侧也出现肾积水、她要拿掉孩子、怎么办？", "id": 536714, "label": 8, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` ### Data Fields The data fields are the same among all splits. #### age_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `0-1岁` (0), `1-2岁` (1), `2-3岁` (2). - `id`: a `int32` feature. #### qa - `question`: a `string` feature. - `answer`: a `string` feature. - `id`: a `int32` feature. #### summarization - `description`: a `string` feature. - `question`: a `string` feature. - `id`: a `int32` feature. #### topic_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `产褥期保健` (0), `儿童过敏` (1), `动作发育` (2), `婴幼保健` (3), `婴幼心理` (4). - `id`: a `int32` feature. ### Data Splits | name |train |validation| test | |--------------------|-----:|---------:|-----:| |age_classification |134852| 19323| 38318| |qa |747888| 106842|213681| |summarization |747888| 106842|213681| |topic_classification|613036| 87519|175363| ## 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-matinf, title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization", author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang", 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.330", pages = "3586--3596", } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.

matinf

2022-03-02T23:29:22+00:00

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2024-01-18T11:08:40+00:00

TAGS #region-us

Dataset Card for "matinf" ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 795.00 MB * Total amount of disk used: 795.00 MB ### Dataset Summary MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### age\_classification * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 48.39 MB * Total amount of disk used: 48.39 MB An example of 'validation' looks as follows. #### qa * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 268.69 MB * Total amount of disk used: 268.69 MB An example of 'train' looks as follows. #### summarization * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 258.88 MB * Total amount of disk used: 258.88 MB An example of 'train' looks as follows. #### topic\_classification * Size of downloaded dataset files: 0.00 MB * Size of the generated dataset: 219.04 MB * Total amount of disk used: 219.04 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### age\_classification * 'question': a 'string' feature. * 'description': a 'string' feature. * 'label': a classification label, with possible values including '0-1岁' (0), '1-2岁' (1), '2-3岁' (2). * 'id': a 'int32' feature. #### qa * 'question': a 'string' feature. * 'answer': a 'string' feature. * 'id': a 'int32' feature. #### summarization * 'description': a 'string' feature. * 'question': a 'string' feature. * 'id': a 'int32' feature. #### topic\_classification * 'question': a 'string' feature. * 'description': a 'string' feature. * 'label': a classification label, with possible values including '产褥期保健' (0), '儿童过敏' (1), '动作发育' (2), '婴幼保健' (3), '婴幼心理' (4). * 'id': a 'int32' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @JetRunner for adding this dataset.

4bb6404fdc6cacfda99d4ac4205087b89d32030c

# Dataset Card for Mostly Basic Python Problems (mbpp) ## Table of Contents - [Dataset Card for Mostly Basic Python Problems (mbpp)](#dataset-card-for-mostly-basic-python-problems-(mbpp)) - [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 - **Repository:** https://github.com/google-research/google-research/tree/master/mbpp - **Paper:** [Program Synthesis with Large Language Models](https://arxiv.org/abs/2108.07732) ### Dataset Summary The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. Released [here](https://github.com/google-research/google-research/tree/master/mbpp) as part of [Program Synthesis with Large Language Models, Austin et. al., 2021](https://arxiv.org/abs/2108.07732). ### Supported Tasks and Leaderboards This dataset is used to evaluate code generations. ### Languages English - Python code ## Dataset Structure ```python dataset_full = load_dataset("mbpp") DatasetDict({ test: Dataset({ features: ['task_id', 'text', 'code', 'test_list', 'test_setup_code', 'challenge_test_list'], num_rows: 974 }) }) dataset_sanitized = load_dataset("mbpp", "sanitized") DatasetDict({ test: Dataset({ features: ['source_file', 'task_id', 'prompt', 'code', 'test_imports', 'test_list'], num_rows: 427 }) }) ``` ### Data Instances #### mbpp - full ``` { 'task_id': 1, 'text': 'Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].', 'code': 'R = 3\r\nC = 3\r\ndef min_cost(cost, m, n): \r\n\ttc = [[0 for x in range(C)] for x in range(R)] \r\n\ttc[0][0] = cost[0][0] \r\n\tfor i in range(1, m+1): \r\n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \r\n\tfor j in range(1, n+1): \r\n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \r\n\tfor i in range(1, m+1): \r\n\t\tfor j in range(1, n+1): \r\n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \r\n\treturn tc[m][n]', 'test_list': [ 'assert min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8', 'assert min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12', 'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'], 'test_setup_code': '', 'challenge_test_list': [] } ``` #### mbpp - sanitized ``` { 'source_file': 'Benchmark Questions Verification V2.ipynb', 'task_id': 2, 'prompt': 'Write a function to find the shared elements from the given two lists.', 'code': 'def similar_elements(test_tup1, test_tup2):\n res = tuple(set(test_tup1) & set(test_tup2))\n return (res) ', 'test_imports': [], 'test_list': [ 'assert set(similar_elements((3, 4, 5, 6),(5, 7, 4, 10))) == set((4, 5))', 'assert set(similar_elements((1, 2, 3, 4),(5, 4, 3, 7))) == set((3, 4))', 'assert set(similar_elements((11, 12, 14, 13),(17, 15, 14, 13))) == set((13, 14))' ] } ``` ### Data Fields - `source_file`: unknown - `text`/`prompt`: description of programming task - `code`: solution for programming task - `test_setup_code`/`test_imports`: necessary code imports to execute tests - `test_list`: list of tests to verify solution - `challenge_test_list`: list of more challenging test to further probe solution ### Data Splits There are two version of the dataset (full and sanitized), each with four splits: - train - evaluation - test - prompt The `prompt` split corresponds to samples used for few-shot prompting and not for training. ## Dataset Creation See section 2.1 of original [paper](https://arxiv.org/abs/2108.07732). ### Curation Rationale In order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides. ### Source Data #### Initial Data Collection and Normalization The dataset was manually created from scratch. #### Who are the source language producers? The dataset was created with an internal crowdsourcing effort at Google. ### Annotations #### Annotation process The full dataset was created first and a subset then underwent a second round to improve the task descriptions. #### Who are the annotators? The dataset was created with an internal crowdsourcing effort at Google. ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases ### Other Known Limitations Since the task descriptions might not be expressive enough to solve the task. The `sanitized` split aims at addressing this issue by having a second round of annotators improve the dataset. ## Additional Information ### Dataset Curators Google Research ### Licensing Information CC-BY-4.0 ### Citation Information ``` @article{austin2021program, title={Program Synthesis with Large Language Models}, author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others}, journal={arXiv preprint arXiv:2108.07732}, year={2021} ``` ### Contributions Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.

mbpp

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "Mostly Basic Python Problems", "tags": ["code-generation"], "dataset_info": [{"config_name": "full", "features": [{"name": "task_id", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "test_list", "sequence": "string"}, {"name": "test_setup_code", "dtype": "string"}, {"name": "challenge_test_list", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 176879, "num_examples": 374}, {"name": "test", "num_bytes": 244104, "num_examples": 500}, {"name": "validation", "num_bytes": 42405, "num_examples": 90}, {"name": "prompt", "num_bytes": 4550, "num_examples": 10}], "download_size": 236069, "dataset_size": 467938}, {"config_name": "sanitized", "features": [{"name": "source_file", "dtype": "string"}, {"name": "task_id", "dtype": "int32"}, {"name": "prompt", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "test_imports", "sequence": "string"}, {"name": "test_list", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 63453, "num_examples": 120}, {"name": "test", "num_bytes": 132720, "num_examples": 257}, {"name": "validation", "num_bytes": 20050, "num_examples": 43}, {"name": "prompt", "num_bytes": 3407, "num_examples": 7}], "download_size": 115422, "dataset_size": 219630}], "configs": [{"config_name": "full", "data_files": [{"split": "train", "path": "full/train-*"}, {"split": "test", "path": "full/test-*"}, {"split": "validation", "path": "full/validation-*"}, {"split": "prompt", "path": "full/prompt-*"}], "default": true}, {"config_name": "sanitized", "data_files": [{"split": "train", "path": "sanitized/train-*"}, {"split": "test", "path": "sanitized/test-*"}, {"split": "validation", "path": "sanitized/validation-*"}, {"split": "prompt", "path": "sanitized/prompt-*"}]}]}

2024-01-04T14:26:37+00:00

TAGS #task_categories-text2text-generation #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-4.0 #code-generation #arxiv-2108.07732 #region-us

# Dataset Card for Mostly Basic Python Problems (mbpp) ## Table of Contents - Dataset Card for Mostly Basic Python Problems (mbpp)) - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Initial Data Collection and Normalization - Who are the source language producers? - Annotations - Annotation process - Who are the annotators? - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Repository: URL - Paper: Program Synthesis with Large Language Models ### Dataset Summary The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. Released here as part of Program Synthesis with Large Language Models, Austin et. al., 2021. ### Supported Tasks and Leaderboards This dataset is used to evaluate code generations. ### Languages English - Python code ## Dataset Structure ### Data Instances #### mbpp - full #### mbpp - sanitized ### Data Fields - 'source_file': unknown - 'text'/'prompt': description of programming task - 'code': solution for programming task - 'test_setup_code'/'test_imports': necessary code imports to execute tests - 'test_list': list of tests to verify solution - 'challenge_test_list': list of more challenging test to further probe solution ### Data Splits There are two version of the dataset (full and sanitized), each with four splits: - train - evaluation - test - prompt The 'prompt' split corresponds to samples used for few-shot prompting and not for training. ## Dataset Creation See section 2.1 of original paper. ### Curation Rationale In order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides. ### Source Data #### Initial Data Collection and Normalization The dataset was manually created from scratch. #### Who are the source language producers? The dataset was created with an internal crowdsourcing effort at Google. ### Annotations #### Annotation process The full dataset was created first and a subset then underwent a second round to improve the task descriptions. #### Who are the annotators? The dataset was created with an internal crowdsourcing effort at Google. ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases ### Other Known Limitations Since the task descriptions might not be expressive enough to solve the task. The 'sanitized' split aims at addressing this issue by having a second round of annotators improve the dataset. ## Additional Information ### Dataset Curators Google Research ### Licensing Information CC-BY-4.0 ### Contributions Thanks to @lvwerra for adding this dataset.

7a59adaeb35b9f744da81f2e56b727d8d5eeb935

# Dataset Card for mC4 ## Table of Contents - [Dataset Card for mC4](#dataset-card-for-mc4) - [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 multilingual 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 108 languages are available and are reported in the table below. Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script. | language code | language name | |:----------------|:---------------------| | af | Afrikaans | | am | Amharic | | ar | Arabic | | az | Azerbaijani | | be | Belarusian | | bg | Bulgarian | | bg-Latn | Bulgarian (Latin) | | bn | Bangla | | ca | Catalan | | ceb | Cebuano | | co | Corsican | | cs | Czech | | cy | Welsh | | da | Danish | | de | German | | el | Greek | | el-Latn | Greek (Latin) | | en | English | | eo | Esperanto | | es | Spanish | | et | Estonian | | eu | Basque | | fa | Persian | | fi | Finnish | | fil | Filipino | | fr | French | | fy | Western Frisian | | ga | Irish | | gd | Scottish Gaelic | | gl | Galician | | gu | Gujarati | | ha | Hausa | | haw | Hawaiian | | hi | Hindi | | hi-Latn | Hindi (Latin script) | | hmn | Hmong, Mong | | ht | Haitian | | hu | Hungarian | | hy | Armenian | | id | Indonesian | | ig | Igbo | | is | Icelandic | | it | Italian | | iw | former Hebrew | | ja | Japanese | | ja-Latn | Japanese (Latin) | | jv | Javanese | | ka | Georgian | | kk | Kazakh | | km | Khmer | | kn | Kannada | | ko | Korean | | ku | Kurdish | | ky | Kyrgyz | | la | Latin | | lb | Luxembourgish | | lo | Lao | | lt | Lithuanian | | lv | Latvian | | mg | Malagasy | | mi | Maori | | mk | Macedonian | | ml | Malayalam | | mn | Mongolian | | mr | Marathi | | ms | Malay | | mt | Maltese | | my | Burmese | | ne | Nepali | | nl | Dutch | | no | Norwegian | | ny | Nyanja | | pa | Punjabi | | pl | Polish | | ps | Pashto | | pt | Portuguese | | ro | Romanian | | ru | Russian | | ru-Latn | Russian (Latin) | | sd | Sindhi | | si | Sinhala | | sk | Slovak | | sl | Slovenian | | sm | Samoan | | sn | Shona | | so | Somali | | sq | Albanian | | sr | Serbian | | st | Southern Sotho | | su | Sundanese | | sv | Swedish | | sw | Swahili | | ta | Tamil | | te | Telugu | | tg | Tajik | | th | Thai | | tr | Turkish | | uk | Ukrainian | | und | Unknown language | | ur | Urdu | | uz | Uzbek | | vi | Vietnamese | | xh | Xhosa | | yi | Yiddish | | yo | Yoruba | | zh | Chinese | | zh-Latn | Chinese (Latin) | | zu | Zulu | You can load the mC4 subset of any language like this: ```python from datasets import load_dataset en_mc4 = load_dataset("mc4", "en") ``` And if you can even specify a list of languages: ```python from datasets import load_dataset mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"]) ``` ### Supported Tasks and Leaderboards mC4 is mainly intended to pretrain language models and word representations. ### Languages The dataset supports 108 languages. ## Dataset Structure ### Data Instances An example form the `en` config is: ``` {'timestamp': '2018-06-24T01:32:39Z', 'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County', 'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'} ``` ### 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 To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table: | config | train | validation | |:---------|:--------|:-------------| | af | ? | ? | | am | ? | ? | | ar | ? | ? | | az | ? | ? | | be | ? | ? | | bg | ? | ? | | bg-Latn | ? | ? | | bn | ? | ? | | ca | ? | ? | | ceb | ? | ? | | co | ? | ? | | cs | ? | ? | | cy | ? | ? | | da | ? | ? | | de | ? | ? | | el | ? | ? | | el-Latn | ? | ? | | en | ? | ? | | eo | ? | ? | | es | ? | ? | | et | ? | ? | | eu | ? | ? | | fa | ? | ? | | fi | ? | ? | | fil | ? | ? | | fr | ? | ? | | fy | ? | ? | | ga | ? | ? | | gd | ? | ? | | gl | ? | ? | | gu | ? | ? | | ha | ? | ? | | haw | ? | ? | | hi | ? | ? | | hi-Latn | ? | ? | | hmn | ? | ? | | ht | ? | ? | | hu | ? | ? | | hy | ? | ? | | id | ? | ? | | ig | ? | ? | | is | ? | ? | | it | ? | ? | | iw | ? | ? | | ja | ? | ? | | ja-Latn | ? | ? | | jv | ? | ? | | ka | ? | ? | | kk | ? | ? | | km | ? | ? | | kn | ? | ? | | ko | ? | ? | | ku | ? | ? | | ky | ? | ? | | la | ? | ? | | lb | ? | ? | | lo | ? | ? | | lt | ? | ? | | lv | ? | ? | | mg | ? | ? | | mi | ? | ? | | mk | ? | ? | | ml | ? | ? | | mn | ? | ? | | mr | ? | ? | | ms | ? | ? | | mt | ? | ? | | my | ? | ? | | ne | ? | ? | | nl | ? | ? | | no | ? | ? | | ny | ? | ? | | pa | ? | ? | | pl | ? | ? | | ps | ? | ? | | pt | ? | ? | | ro | ? | ? | | ru | ? | ? | | ru-Latn | ? | ? | | sd | ? | ? | | si | ? | ? | | sk | ? | ? | | sl | ? | ? | | sm | ? | ? | | sn | ? | ? | | so | ? | ? | | sq | ? | ? | | sr | ? | ? | | st | ? | ? | | su | ? | ? | | sv | ? | ? | | sw | ? | ? | | ta | ? | ? | | te | ? | ? | | tg | ? | ? | | th | ? | ? | | tr | ? | ? | | uk | ? | ? | | und | ? | ? | | ur | ? | ? | | uz | ? | ? | | vi | ? | ? | | xh | ? | ? | | yi | ? | ? | | yo | ? | ? | | zh | ? | ? | | zh-Latn | ? | ? | | zu | ? | ? | ## 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 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.

mc4

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M", "1M<n<10M", "10M<n<100M", "100M<n<1B", "1B<n<10B"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "mc4", "pretty_name": "mC4", "language_bcp47": ["bg-Latn", "el-Latn", "hi-Latn", "ja-Latn", "ru-Latn", "zh-Latn"]}

2022-10-28T15:36:33+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #size_categories-10M<n<100M #size_categories-100M<n<1B #size_categories-1B<n<10B #source_datasets-original #language-Afrikaans #language-Amharic #language-Arabic #language-Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Catalan #language-Cebuano #language-Corsican #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-Filipino #language-French #language-Western Frisian #language-Irish #language-Scottish Gaelic #language-Galician #language-Gujarati #language-Hausa #language-Hawaiian #language-Hebrew #language-Hindi #language-Hmong #language-Haitian #language-Hungarian #language-Armenian #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-iw #language-Japanese #language-Javanese #language-Georgian #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Kurdish #language-Kirghiz #language-Latin #language-Luxembourgish #language-Lao #language-Lithuanian #language-Latvian #language-Malagasy #language-Maori #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Burmese #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian #language-Nyanja #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Sindhi #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Southern Sotho #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkish #language-Ukrainian #language-Undetermined #language-Urdu #language-Uzbek #language-Vietnamese #language-Xhosa #language-Yiddish #language-Yoruba #language-Chinese #language-Zulu #license-odc-by #arxiv-1910.10683 #region-us

Dataset Card for mC4 ==================== Table of Contents ----------------- * Dataset Card for mC4 + Table of Contents + Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages + Dataset Structure - Data Instances - Data Fields - Data Splits + Dataset Creation - Curation Rationale - Source Data * Initial Data Collection and Normalization * Who are the source language producers? - Annotations * Annotation process * Who are the annotators? - Personal and Sensitive Information + Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations + Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions Dataset Description ------------------- * Homepage: URL * Paper: URL ### Dataset Summary A multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "URL". This is the version prepared by AllenAI, hosted at this address: URL 108 languages are available and are reported in the table below. Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script. You can load the mC4 subset of any language like this: And if you can even specify a list of languages: ### Supported Tasks and Leaderboards mC4 is mainly intended to pretrain language models and word representations. ### Languages The dataset supports 108 languages. Dataset Structure ----------------- ### Data Instances An example form the 'en' config is: ### 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 To build mC4, the authors used CLD3 to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### 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. ### Contributions Thanks to @dirkgr and @lhoestq for adding this dataset.

0930ef7851e349c65cfe87b058aaf60e331247e5

# Dataset Card for MC-TACO ## 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:** [MC-TACO](https://cogcomp.seas.upenn.edu/page/resource_view/125) - **Repository:** [Github repository](https://github.com/CogComp/MCTACO) - **Paper:** ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://arxiv.org/abs/1909.03065) - **Leaderboard:** [AI2 Leaderboard](https://leaderboard.allenai.org/mctaco) ### Dataset Summary MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible. ### Supported Tasks and Leaderboards The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no"). Performance is measured using two metrics: - Exact Match -- the average number of questions for which all the candidate answers are predicted correctly. - F1 -- is slightly more relaxed than EM. It measures the overlap between one’s predictions and the ground truth, by computing the geometric mean of Precision and Recall. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this: ``` { "sentence": "However, more recently, it has been suggested that it may date from earlier than Abdalonymus' death.", "question": "How often did Abdalonymus die?", "answer": "every two years", "label": "no", "category": "Frequency", } ``` ### Data Fields All fields are strings: - `sentence`: a sentence (or context) on which the question is based - `question`: a question querying some temporal commonsense knowledge - `answer`: a potential answer to the question (all lowercased) - `label`: whether the answer is a correct. "yes" indicates the answer is correct/plaussible, "no" otherwise - `category`: the temporal category the question belongs to (among "Event Ordering", "Event Duration", "Frequency", "Stationarity", and "Typical Time") ### Data Splits The development set contains 561 questions and 3,783 candidate answers. The test set contains 1,332 questions and 9,442 candidate answers. From the original repository: *Note that there is no training data, and we provide the dev set as the only source of supervision. The rationale is that we believe a successful system has to bring in a huge amount of world knowledge and derive commonsense understandings prior to the current task evaluation. We therefore believe that it is not reasonable to expect a system to be trained solely on this data, and we think of the development data as only providing a definition of the task.* ## Dataset Creation ### Curation Rationale MC-TACO is used as a testbed to study the temporal commonsense understanding on NLP systems. ### Source Data From the original paper: *The context sentences are randomly selected from [MultiRC](https://www.aclweb.org/anthology/N18-1023/) (from each of its 9 domains). For each sentence, we use crowdsourcing on Amazon Mechanical Turk to collect questions and candidate answers (both correct and wrong ones).* #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations From the original paper: *To ensure the quality of the results, we limit the annotations to native speakers and use qualification tryouts.* #### Annotation process The crowdsourced construction/annotation of the dataset follows 4 steps described in Section 3 of the [paper](https://arxiv.org/abs/1909.03065): question generation, question verification, candidate answer expansion and answer labeling. #### Who are the annotators? Paid crowdsourcers. ### 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 Unknwon ### Citation Information ``` @inproceedings{ZKNR19, author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth}, title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding }, booktitle = {EMNLP}, year = {2019}, } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

mc_taco

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mc-taco", "pretty_name": "MC-TACO", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "no", "1": "yes"}}}}, {"name": "category", "dtype": {"class_label": {"names": {"0": "Event Duration", "1": "Event Ordering", "2": "Frequency", "3": "Typical Time", "4": "Stationarity"}}}}], "config_name": "plain_text", "splits": [{"name": "test", "num_bytes": 1785553, "num_examples": 9442}, {"name": "validation", "num_bytes": 713023, "num_examples": 3783}], "download_size": 2385137, "dataset_size": 2498576}}

2024-01-18T11:08:44+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #arxiv-1909.03065 #region-us

# Dataset Card for MC-TACO ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: MC-TACO - Repository: Github repository - Paper: "Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding - Leaderboard: AI2 Leaderboard ### Dataset Summary MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible. ### Supported Tasks and Leaderboards The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no"). Performance is measured using two metrics: - Exact Match -- the average number of questions for which all the candidate answers are predicted correctly. - F1 -- is slightly more relaxed than EM. It measures the overlap between one’s predictions and the ground truth, by computing the geometric mean of Precision and Recall. ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. ## Dataset Structure ### Data Instances An example looks like this: ### Data Fields All fields are strings: - 'sentence': a sentence (or context) on which the question is based - 'question': a question querying some temporal commonsense knowledge - 'answer': a potential answer to the question (all lowercased) - 'label': whether the answer is a correct. "yes" indicates the answer is correct/plaussible, "no" otherwise - 'category': the temporal category the question belongs to (among "Event Ordering", "Event Duration", "Frequency", "Stationarity", and "Typical Time") ### Data Splits The development set contains 561 questions and 3,783 candidate answers. The test set contains 1,332 questions and 9,442 candidate answers. From the original repository: *Note that there is no training data, and we provide the dev set as the only source of supervision. The rationale is that we believe a successful system has to bring in a huge amount of world knowledge and derive commonsense understandings prior to the current task evaluation. We therefore believe that it is not reasonable to expect a system to be trained solely on this data, and we think of the development data as only providing a definition of the task.* ## Dataset Creation ### Curation Rationale MC-TACO is used as a testbed to study the temporal commonsense understanding on NLP systems. ### Source Data From the original paper: *The context sentences are randomly selected from MultiRC (from each of its 9 domains). For each sentence, we use crowdsourcing on Amazon Mechanical Turk to collect questions and candidate answers (both correct and wrong ones).* #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations From the original paper: *To ensure the quality of the results, we limit the annotations to native speakers and use qualification tryouts.* #### Annotation process The crowdsourced construction/annotation of the dataset follows 4 steps described in Section 3 of the paper: question generation, question verification, candidate answer expansion and answer labeling. #### Who are the annotators? Paid crowdsourcers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Unknwon ### Contributions Thanks to @VictorSanh for adding this dataset.

41aa009fb2c30fdf7da2590bfd77a4260acdce0d

# Dataset Card for Multi-Dimensional Gender Bias Classification ## 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:** [ParlAI MD Gender Project Page](https://parl.ai/projects/md_gender/) - **Repository:** [ParlAI Github MD Gender Repository](https://github.com/facebookresearch/ParlAI/tree/master/projects/md_gender) - **Paper:** [Multi-Dimensional Gender Bias Classification](https://www.aclweb.org/anthology/2020.emnlp-main.23.pdf) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [email protected] ### Dataset Summary The Multi-Dimensional Gender Bias Classification dataset is based on a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. It contains seven large scale datasets automatically annotated for gender information (there are eight in the original project but the Wikipedia set is not included in the HuggingFace distribution), one crowdsourced evaluation benchmark of utterance-level gender rewrites, a list of gendered names, and a list of gendered words in English. ### Supported Tasks and Leaderboards - `text-classification-other-gender-bias`: The dataset can be used to train a model for classification of various kinds of gender bias. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. Dinan et al's (2020) Transformer model achieved an average of 67.13% accuracy in binary gender prediction across the ABOUT, TO, and AS tasks. See the paper for more results. ### Languages The data is in English as spoken on the various sites where the data was collected. The associated BCP-47 code `en`. ## Dataset Structure ### Data Instances The following are examples of data instances from the various configs in the dataset. See the [MD Gender Bias dataset viewer](https://huggingface.co/datasets/viewer/?dataset=md_gender_bias) to explore more examples. An example from the `new_data` config: ``` {'class_type': 0, 'confidence': 'certain', 'episode_done': True, 'labels': [1], 'original': 'She designed monumental Loviisa war cemetery in 1920', 'text': 'He designed monumental Lovissa War Cemetery in 1920.', 'turker_gender': 4} ``` An example from the `funpedia` config: ``` {'gender': 2, 'persona': 'Humorous', 'text': 'Max Landis is a comic book writer who wrote Chronicle, American Ultra, and Victor Frankestein.', 'title': 'Max Landis'} ``` An example from the `image_chat` config: ``` {'caption': '<start> a young girl is holding a pink umbrella in her hand <eos>', 'female': True, 'id': '2923e28b6f588aff2d469ab2cccfac57', 'male': False} ``` An example from the `wizard` config: ``` {'chosen_topic': 'Krav Maga', 'gender': 2, 'text': 'Hello. I hope you might enjoy or know something about Krav Maga?'} ``` An example from the `convai2_inferred` config (the other `_inferred` configs have the same fields, with the exception of `yelp_inferred`, which does not have the `ternary_label` or `ternary_score` fields): ``` {'binary_label': 1, 'binary_score': 0.6521999835968018, 'ternary_label': 2, 'ternary_score': 0.4496000111103058, 'text': "hi , how are you doing ? i'm getting ready to do some cheetah chasing to stay in shape ."} ``` An example from the `gendered_words` config: ``` {'word_feminine': 'countrywoman', 'word_masculine': 'countryman'} ``` An example from the `name_genders` config: ``` {'assigned_gender': 1, 'count': 7065, 'name': 'Mary'} ``` ### Data Fields The following are the features for each of the configs. For the `new_data` config: - `text`: the text to be classified - `original`: the text before reformulation - `labels`: a `list` of classification labels, with possible values including `ABOUT:female`, `ABOUT:male`, `PARTNER:female`, `PARTNER:male`, `SELF:female`. - `class_type`: a classification label, with possible values including `about` (0), `partner` (1), `self` (2). - `turker_gender`: a classification label, with possible values including `man` (0), `woman` (1), `nonbinary` (2), `prefer not to say` (3), `no answer` (4). - `episode_done`: a boolean indicating whether the conversation was completed. - `confidence`: a string indicating the confidence of the annotator in response to the instance label being ABOUT/TO/AS a man or woman. Possible values are `certain`, `pretty sure`, and `unsure`. For the `funpedia` config: - `text`: the text to be classified. - `gender`: a classification label, with possible values including `gender-neutral` (0), `female` (1), `male` (2), indicating the gender of the person being talked about. - `persona`: a string describing the persona assigned to the user when talking about the entity. - `title`: a string naming the entity the text is about. For the `image_chat` config: - `caption`: a string description of the contents of the original image. - `female`: a boolean indicating whether the gender of the person being talked about is female, if the image contains a person. - `id`: a string indicating the id of the image. - `male`: a boolean indicating whether the gender of the person being talked about is male, if the image contains a person. For the `wizard` config: - `text`: the text to be classified. - `chosen_topic`: a string indicating the topic of the text. - `gender`: a classification label, with possible values including `gender-neutral` (0), `female` (1), `male` (2), indicating the gender of the person being talked about. For the `_inferred` configurations (again, except the `yelp_inferred` split, which does not have the `ternary_label` or `ternary_score` fields): - `text`: the text to be classified. - `binary_label`: a classification label, with possible values including `ABOUT:female`, `ABOUT:male`. - `binary_score`: a float indicating a score between 0 and 1. - `ternary_label`: a classification label, with possible values including `ABOUT:female`, `ABOUT:male`, `ABOUT:gender-neutral`. - `ternary_score`: a float indicating a score between 0 and 1. For the word list: - `word_masculine`: a string indicating the masculine version of the word. - `word_feminine`: a string indicating the feminine version of the word. For the gendered name list: - `assigned_gender`: an integer, 1 for female, 0 for male. - `count`: an integer. - `name`: a string of the name. ### Data Splits The different parts of the data can be accessed through the different configurations: - `gendered_words`: A list of common nouns with a masculine and feminine variant. - `new_data`: Sentences reformulated and annotated along all three axes. - `funpedia`, `wizard`: Sentences from Funpedia and Wizards of Wikipedia annotated with ABOUT gender with entity gender information. - `image_chat`: sentences about images annotated with ABOUT gender based on gender information from the entities in the image - `convai2_inferred`, `light_inferred`, `opensubtitles_inferred`, `yelp_inferred`: Data from several source datasets with ABOUT annotations inferred by a trined classifier. | Split | M | F | N | U | Dimension | | ---------- | ---- | --- | ---- | ---- | --------- | | Image Chat | 39K | 15K | 154K | - | ABOUT | | Funpedia | 19K | 3K | 1K | - | ABOUT | | Wizard | 6K | 1K | 1K | - | ABOUT | | Yelp | 1M | 1M | - | - | AS | | ConvAI2 | 22K | 22K | - | 86K | AS | | ConvAI2 | 22K | 22K | - | 86K | TO | | OpenSub | 149K | 69K | - | 131K | AS | | OpenSub | 95K | 45K | - | 209K | TO | | LIGHT | 13K | 8K | - | 83K | AS | | LIGHT | 13K | 8K | - | 83K | TO | | ---------- | ---- | --- | ---- | ---- | --------- | | MDGender | 384 | 401 | - | - | ABOUT | | MDGender | 396 | 371 | - | - | AS | | MDGender | 411 | 382 | - | - | TO | ## Dataset Creation ### Curation Rationale The curators chose to annotate the existing corpora to make their classifiers reliable on all dimensions (ABOUT/TO/AS) and across multiple domains. However, none of the existing datasets cover all three dimensions at the same time, and many of the gender labels are noisy. To enable reliable evaluation, the curators collected a specialized corpus, found in the `new_data` config, which acts as a gold-labeled dataset for the masculine and feminine classes. ### Source Data #### Initial Data Collection and Normalization For the `new_data` config, the curators collected conversations between two speakers. Each speaker was provided with a persona description containing gender information, then tasked with adopting that persona and having a conversation. They were also provided with small sections of a biography from Wikipedia as the conversation topic in order to encourage crowdworkers to discuss ABOUT/TO/AS gender information. To ensure there is ABOUT/TO/AS gender information contained in each utterance, the curators asked a second set of annotators to rewrite each utterance to make it very clear that they are speaking ABOUT a man or a woman, speaking AS a man or a woman, and speaking TO a man or a woman. #### Who are the source language producers? This dataset was collected from crowdworkers from Amazon’s Mechanical Turk. All workers are English-speaking and located in the United States. | Reported Gender | Percent of Total | | ----------------- | ---------------- | | Man | 67.38 | | Woman | 18.34 | | Non-binary | 0.21 | | Prefer not to say | 14.07 | ### Annotations #### Annotation process For the `new_data` config, annotators were asked to label how confident they are that someone else could predict the given gender label, allowing for flexibility between explicit genderedness (like the use of "he" or "she") and statistical genderedness. Many of the annotated datasets contain cases where the ABOUT, AS, TO labels are not provided (i.e. unknown). In such instances, the curators apply one of two strategies. They apply the imputation strategy for data for which the ABOUT label is unknown using a classifier trained only on other Wikipedia data for which this label is provided. Data without a TO or AS label was assigned one at random, choosing between masculine and feminine with equal probability. Details of how each of the eight training datasets was annotated are as follows: 1. Wikipedia- to annotate ABOUT, the curators used a Wikipedia dump and extract biography pages using named entity recognition. They labeled pages with a gender based on the number of gendered pronouns (he vs. she vs. they) and labeled each paragraph in the page with this label for the ABOUT dimension. 2. Funpedia- Funpedia ([Miller et al., 2017](https://www.aclweb.org/anthology/D17-2014/)) contains rephrased Wikipedia sentences in a more conversational way. The curators retained only biography related sentences and annotate similar to Wikipedia, to give ABOUT labels. 3. Wizard of Wikipedia- [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia/) contains two people discussing a topic in Wikipedia. The curators retain only the conversations on Wikipedia biographies and annotate to create ABOUT labels. 4. ImageChat- [ImageChat](https://klshuster.github.io/image_chat/) contains conversations discussing the contents of an image. The curators used the [Xu et al. image captioning system](https://github.com/AaronCCWong/Show-Attend-and-Tell) to identify the contents of an image and select gendered examples. 5. Yelp- The curators used the Yelp reviewer gender predictor developed by ([Subramanian et al., 2018](https://arxiv.org/pdf/1811.00552.pdf)) and retain reviews for which the classifier is very confident – this creates labels for the content creator of the review (AS). They impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. 6. ConvAI2- [ConvAI2](https://parl.ai/projects/convai2/) contains persona-based conversations. Many personas contain sentences such as 'I am a old woman' or 'My name is Bob' which allows annotators to annotate the gender of the speaker (AS) and addressee (TO) with some confidence. Many of the personas have unknown gender. The curators impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. 7. OpenSubtitles- [OpenSubtitles](http://www.opensubtitles.org/) contains subtitles for movies in different languages. The curators retained English subtitles that contain a character name or identity. They annotated the character’s gender using gender kinship terms such as daughter and gender probability distribution calculated by counting the masculine and feminine names of baby names in the United States. Using the character’s gender, they produced labels for the AS dimension. They produced labels for the TO dimension by taking the gender of the next character to speak if there is another utterance in the conversation; otherwise, they take the gender of the last character to speak. They impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. 8. LIGHT- [LIGHT](https://parl.ai/projects/light/) contains persona-based conversation. Similarly to ConvAI2, annotators labeled the gender of each persona, giving labels for the speaker (AS) and speaking partner (TO). The curators impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. #### Who are the annotators? This dataset was annotated by crowdworkers from Amazon’s Mechanical Turk. All workers are English-speaking and located in the United States. ### Personal and Sensitive Information For privacy reasons the curators did not associate the self-reported gender of the annotator with the labeled examples in the dataset and only report these statistics in aggregate. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended for applications such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness. ### Discussion of Biases Over two thirds of annotators identified as men, which may introduce biases into the dataset. Wikipedia is also well known to have gender bias in equity of biographical coverage and lexical bias in noun references to women (see the paper's appendix for citations). ### Other Known Limitations The limitations of the Multi-Dimensional Gender Bias Classification dataset have not yet been investigated, but the curators acknowledge that more work is required to address the intersectionality of gender identities, i.e., when gender non-additively interacts with other identity characteristics. The curators point out that negative gender stereotyping is known to be alternatively weakened or reinforced by the presence of social attributes like dialect, class and race and that these differences have been found to affect gender classification in images and sentences encoders. See the paper for references. ## Additional Information ### Dataset Curators Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, and Adina Williams at Facebook AI Research. Angela Fan is also affiliated with Laboratoire Lorrain d’Informatique et Applications (LORIA). ### Licensing Information The Multi-Dimensional Gender Bias Classification dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information ``` @inproceedings{dinan-etal-2020-multi, title = "Multi-Dimensional Gender Bias Classification", author = "Dinan, Emily and Fan, Angela and Wu, Ledell and Weston, Jason and Kiela, Douwe and Williams, Adina", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.23", doi = "10.18653/v1/2020.emnlp-main.23", pages = "314--331", abstract = "Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a novel, general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a new, crowdsourced evaluation benchmark. Distinguishing between gender bias along multiple dimensions enables us to train better and more fine-grained gender bias classifiers. We show our classifiers are valuable for a variety of applications, like controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness.", } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) and [@mcmillanmajora](https://github.com/mcmillanmajora)for adding this dataset.

md_gender_bias

2022-03-02T23:29:22+00:00

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2024-01-18T11:08:47+00:00

TAGS #task_categories-text-classification #annotations_creators-crowdsourced #annotations_creators-found #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #size_categories-n<1K #source_datasets-extended|other-convai2 #source_datasets-extended|other-light #source_datasets-extended|other-opensubtitles #source_datasets-extended|other-yelp #source_datasets-original #language-English #license-mit #gender-bias #arxiv-1811.00552 #region-us

Dataset Card for Multi-Dimensional Gender Bias Classification ============================================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: ParlAI MD Gender Project Page * Repository: ParlAI Github MD Gender Repository * Paper: Multi-Dimensional Gender Bias Classification * Leaderboard: * Point of Contact: edinan@URL ### Dataset Summary The Multi-Dimensional Gender Bias Classification dataset is based on a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. It contains seven large scale datasets automatically annotated for gender information (there are eight in the original project but the Wikipedia set is not included in the HuggingFace distribution), one crowdsourced evaluation benchmark of utterance-level gender rewrites, a list of gendered names, and a list of gendered words in English. ### Supported Tasks and Leaderboards * 'text-classification-other-gender-bias': The dataset can be used to train a model for classification of various kinds of gender bias. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. Dinan et al's (2020) Transformer model achieved an average of 67.13% accuracy in binary gender prediction across the ABOUT, TO, and AS tasks. See the paper for more results. ### Languages The data is in English as spoken on the various sites where the data was collected. The associated BCP-47 code 'en'. Dataset Structure ----------------- ### Data Instances The following are examples of data instances from the various configs in the dataset. See the MD Gender Bias dataset viewer to explore more examples. An example from the 'new\_data' config: An example from the 'funpedia' config: An example from the 'image\_chat' config: An example from the 'wizard' config: An example from the 'convai2\_inferred' config (the other '\_inferred' configs have the same fields, with the exception of 'yelp\_inferred', which does not have the 'ternary\_label' or 'ternary\_score' fields): An example from the 'gendered\_words' config: An example from the 'name\_genders' config: ### Data Fields The following are the features for each of the configs. For the 'new\_data' config: * 'text': the text to be classified * 'original': the text before reformulation * 'labels': a 'list' of classification labels, with possible values including 'ABOUT:female', 'ABOUT:male', 'PARTNER:female', 'PARTNER:male', 'SELF:female'. * 'class\_type': a classification label, with possible values including 'about' (0), 'partner' (1), 'self' (2). * 'turker\_gender': a classification label, with possible values including 'man' (0), 'woman' (1), 'nonbinary' (2), 'prefer not to say' (3), 'no answer' (4). * 'episode\_done': a boolean indicating whether the conversation was completed. * 'confidence': a string indicating the confidence of the annotator in response to the instance label being ABOUT/TO/AS a man or woman. Possible values are 'certain', 'pretty sure', and 'unsure'. For the 'funpedia' config: * 'text': the text to be classified. * 'gender': a classification label, with possible values including 'gender-neutral' (0), 'female' (1), 'male' (2), indicating the gender of the person being talked about. * 'persona': a string describing the persona assigned to the user when talking about the entity. * 'title': a string naming the entity the text is about. For the 'image\_chat' config: * 'caption': a string description of the contents of the original image. * 'female': a boolean indicating whether the gender of the person being talked about is female, if the image contains a person. * 'id': a string indicating the id of the image. * 'male': a boolean indicating whether the gender of the person being talked about is male, if the image contains a person. For the 'wizard' config: * 'text': the text to be classified. * 'chosen\_topic': a string indicating the topic of the text. * 'gender': a classification label, with possible values including 'gender-neutral' (0), 'female' (1), 'male' (2), indicating the gender of the person being talked about. For the '\_inferred' configurations (again, except the 'yelp\_inferred' split, which does not have the 'ternary\_label' or 'ternary\_score' fields): * 'text': the text to be classified. * 'binary\_label': a classification label, with possible values including 'ABOUT:female', 'ABOUT:male'. * 'binary\_score': a float indicating a score between 0 and 1. * 'ternary\_label': a classification label, with possible values including 'ABOUT:female', 'ABOUT:male', 'ABOUT:gender-neutral'. * 'ternary\_score': a float indicating a score between 0 and 1. For the word list: * 'word\_masculine': a string indicating the masculine version of the word. * 'word\_feminine': a string indicating the feminine version of the word. For the gendered name list: * 'assigned\_gender': an integer, 1 for female, 0 for male. * 'count': an integer. * 'name': a string of the name. ### Data Splits The different parts of the data can be accessed through the different configurations: * 'gendered\_words': A list of common nouns with a masculine and feminine variant. * 'new\_data': Sentences reformulated and annotated along all three axes. * 'funpedia', 'wizard': Sentences from Funpedia and Wizards of Wikipedia annotated with ABOUT gender with entity gender information. * 'image\_chat': sentences about images annotated with ABOUT gender based on gender information from the entities in the image * 'convai2\_inferred', 'light\_inferred', 'opensubtitles\_inferred', 'yelp\_inferred': Data from several source datasets with ABOUT annotations inferred by a trined classifier. Dataset Creation ---------------- ### Curation Rationale The curators chose to annotate the existing corpora to make their classifiers reliable on all dimensions (ABOUT/TO/AS) and across multiple domains. However, none of the existing datasets cover all three dimensions at the same time, and many of the gender labels are noisy. To enable reliable evaluation, the curators collected a specialized corpus, found in the 'new\_data' config, which acts as a gold-labeled dataset for the masculine and feminine classes. ### Source Data #### Initial Data Collection and Normalization For the 'new\_data' config, the curators collected conversations between two speakers. Each speaker was provided with a persona description containing gender information, then tasked with adopting that persona and having a conversation. They were also provided with small sections of a biography from Wikipedia as the conversation topic in order to encourage crowdworkers to discuss ABOUT/TO/AS gender information. To ensure there is ABOUT/TO/AS gender information contained in each utterance, the curators asked a second set of annotators to rewrite each utterance to make it very clear that they are speaking ABOUT a man or a woman, speaking AS a man or a woman, and speaking TO a man or a woman. #### Who are the source language producers? This dataset was collected from crowdworkers from Amazon’s Mechanical Turk. All workers are English-speaking and located in the United States. ### Annotations #### Annotation process For the 'new\_data' config, annotators were asked to label how confident they are that someone else could predict the given gender label, allowing for flexibility between explicit genderedness (like the use of "he" or "she") and statistical genderedness. Many of the annotated datasets contain cases where the ABOUT, AS, TO labels are not provided (i.e. unknown). In such instances, the curators apply one of two strategies. They apply the imputation strategy for data for which the ABOUT label is unknown using a classifier trained only on other Wikipedia data for which this label is provided. Data without a TO or AS label was assigned one at random, choosing between masculine and feminine with equal probability. Details of how each of the eight training datasets was annotated are as follows: 1. Wikipedia- to annotate ABOUT, the curators used a Wikipedia dump and extract biography pages using named entity recognition. They labeled pages with a gender based on the number of gendered pronouns (he vs. she vs. they) and labeled each paragraph in the page with this label for the ABOUT dimension. 2. Funpedia- Funpedia (Miller et al., 2017) contains rephrased Wikipedia sentences in a more conversational way. The curators retained only biography related sentences and annotate similar to Wikipedia, to give ABOUT labels. 3. Wizard of Wikipedia- Wizard of Wikipedia contains two people discussing a topic in Wikipedia. The curators retain only the conversations on Wikipedia biographies and annotate to create ABOUT labels. 4. ImageChat- ImageChat contains conversations discussing the contents of an image. The curators used the Xu et al. image captioning system to identify the contents of an image and select gendered examples. 5. Yelp- The curators used the Yelp reviewer gender predictor developed by (Subramanian et al., 2018) and retain reviews for which the classifier is very confident – this creates labels for the content creator of the review (AS). They impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. 6. ConvAI2- ConvAI2 contains persona-based conversations. Many personas contain sentences such as 'I am a old woman' or 'My name is Bob' which allows annotators to annotate the gender of the speaker (AS) and addressee (TO) with some confidence. Many of the personas have unknown gender. The curators impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. 7. OpenSubtitles- OpenSubtitles contains subtitles for movies in different languages. The curators retained English subtitles that contain a character name or identity. They annotated the character’s gender using gender kinship terms such as daughter and gender probability distribution calculated by counting the masculine and feminine names of baby names in the United States. Using the character’s gender, they produced labels for the AS dimension. They produced labels for the TO dimension by taking the gender of the next character to speak if there is another utterance in the conversation; otherwise, they take the gender of the last character to speak. They impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. 8. LIGHT- LIGHT contains persona-based conversation. Similarly to ConvAI2, annotators labeled the gender of each persona, giving labels for the speaker (AS) and speaking partner (TO). The curators impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4. #### Who are the annotators? This dataset was annotated by crowdworkers from Amazon’s Mechanical Turk. All workers are English-speaking and located in the United States. ### Personal and Sensitive Information For privacy reasons the curators did not associate the self-reported gender of the annotator with the labeled examples in the dataset and only report these statistics in aggregate. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset This dataset is intended for applications such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness. ### Discussion of Biases Over two thirds of annotators identified as men, which may introduce biases into the dataset. Wikipedia is also well known to have gender bias in equity of biographical coverage and lexical bias in noun references to women (see the paper's appendix for citations). ### Other Known Limitations The limitations of the Multi-Dimensional Gender Bias Classification dataset have not yet been investigated, but the curators acknowledge that more work is required to address the intersectionality of gender identities, i.e., when gender non-additively interacts with other identity characteristics. The curators point out that negative gender stereotyping is known to be alternatively weakened or reinforced by the presence of social attributes like dialect, class and race and that these differences have been found to affect gender classification in images and sentences encoders. See the paper for references. Additional Information ---------------------- ### Dataset Curators Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, and Adina Williams at Facebook AI Research. Angela Fan is also affiliated with Laboratoire Lorrain d’Informatique et Applications (LORIA). ### Licensing Information The Multi-Dimensional Gender Bias Classification dataset is licensed under the MIT License. ### Contributions Thanks to @yjernite and @mcmillanmajorafor adding this dataset.

e72e9aa807c8d0925408d06e8a19934872e50f62

# Dataset Card for MDD ## 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/1511.06931.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion), from various movie reviews sources such as MovieLens and OMDb. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The data is present in English language as written by users on OMDb and MovieLens websites. ## Dataset Structure ### Data Instances An instance from the `task3_qarecs` config's `train` split: ``` {'dialogue_turns': {'speaker': [0, 1, 0, 1, 0, 1], 'utterance': ["I really like Jaws, Bottle Rocket, Saving Private Ryan, Tommy Boy, The Muppet Movie, Face/Off, and Cool Hand Luke. I'm looking for a Documentary movie.", 'Beyond the Mat', 'Who is that directed by?', 'Barry W. Blaustein', 'I like Jon Fauer movies more. Do you know anything else?', 'Cinematographer Style']}} ``` An instance from the `task4_reddit` config's `cand-valid` split: ``` {'dialogue_turns': {'speaker': [0], 'utterance': ['MORTAL KOMBAT !']}} ``` ### Data Fields For all configurations: - `dialogue_turns`: a dictionary feature containing: - `speaker`: an integer with possible values including `0`, `1`, indicating which speaker wrote the utterance. - `utterance`: a `string` feature containing the text utterance. ### Data Splits The splits and corresponding sizes are: |config |train |test |validation|cand_valid|cand_test| |:--|------:|----:|---------:|----:|----:| |task1_qa|96185|9952|9968|-|-| |task2_recs|1000000|10000|10000|-|-| |task3_qarecs|952125|4915|5052|-|-| |task4_reddit|945198|10000|10000|10000|10000| The `cand_valid` and `cand_test` are negative candidates for the `task4_reddit` configuration which is used in ranking true positive against these candidates and hits@k (or another ranking metric) is reported. (See paper) ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The construction of the tasks depended on some existing datasets: 1) MovieLens. The data was downloaded from: http://grouplens.org/datasets/movielens/20m/ on May 27th, 2015. 2) OMDB. The data was downloaded from: http://beforethecode.com/projects/omdb/download.aspx on May 28th, 2015. 3) For `task4_reddit`, the data is a processed subset (movie subreddit only) of the data available at: https://www.reddit.com/r/datasets/comments/3bxlg7 #### Who are the source language producers? Users on MovieLens, OMDB website and reddit websites, among others. ### 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.

mdd

2022-03-02T23:29:22+00:00

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2024-01-18T11:08:49+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-3.0 #arxiv-1511.06931 #region-us

Dataset Card for MDD ==================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage:The bAbI project * Repository: * Paper: arXiv Paper * Leaderboard: * Point of Contact: ### Dataset Summary The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion), from various movie reviews sources such as MovieLens and OMDb. ### Supported Tasks and Leaderboards ### Languages The data is present in English language as written by users on OMDb and MovieLens websites. Dataset Structure ----------------- ### Data Instances An instance from the 'task3\_qarecs' config's 'train' split: An instance from the 'task4\_reddit' config's 'cand-valid' split: ### Data Fields For all configurations: * 'dialogue\_turns': a dictionary feature containing: + 'speaker': an integer with possible values including '0', '1', indicating which speaker wrote the utterance. + 'utterance': a 'string' feature containing the text utterance. ### Data Splits The splits and corresponding sizes are: The 'cand\_valid' and 'cand\_test' are negative candidates for the 'task4\_reddit' configuration which is used in ranking true positive against these candidates and hits@k (or another ranking metric) is reported. (See paper) Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The construction of the tasks depended on some existing datasets: 1. MovieLens. The data was downloaded from: URL on May 27th, 2015. 2. OMDB. The data was downloaded from: URL on May 28th, 2015. 3. For 'task4\_reddit', the data is a processed subset (movie subreddit only) of the data available at: URL #### Who are the source language producers? Users on MovieLens, OMDB website and reddit websites, among others. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations 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 ### Contributions Thanks to @gchhablani for adding this dataset.

ee7fa9ab95c90eb8aa392e1f05f7a44511cc741e

# Dataset Card for MedHop ## 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:** [QAngaroo](http://qangaroo.cs.ucl.ac.uk/) - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://arxiv.org/abs/1710.06481) - **Leaderboard:** [leaderboard](http://qangaroo.cs.ucl.ac.uk/leaderboard.html) - **Point of Contact:** [Johannes Welbl]([email protected]) ### 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 [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 [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

med_hop

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "medhop", "pretty_name": "MedHop", "tags": ["multi-hop"], "dataset_info": [{"config_name": "original", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "candidates", "sequence": "string"}, {"name": "supports", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 93937322, "num_examples": 1620}, {"name": "validation", "num_bytes": 16461640, "num_examples": 342}], "download_size": 339843061, "dataset_size": 110398962}, {"config_name": "masked", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "candidates", "sequence": "string"}, {"name": "supports", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 95813584, "num_examples": 1620}, {"name": "validation", "num_bytes": 16800570, "num_examples": 342}], "download_size": 339843061, "dataset_size": 112614154}]}

2024-01-18T11:08:50+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-sa-3.0 #multi-hop #arxiv-1710.06481 #region-us

# Dataset Card for MedHop ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: QAngaroo - Repository: [If the dataset is hosted on github or has a github homepage, add URL here]() - Paper: Constructing Datasets for Multi-hop Reading Comprehension Across Documents - Leaderboard: leaderboard - Point of Contact: Johannes Welbl ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patil-suraj for adding this dataset.

1b7eb6a1b85ce9849238b5aaac70d1f97a9f04b5

# Dataset Card for the MeDAL 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:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Repository:** https://github.com/BruceWen120/medal - **Paper:** https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/ - **Dataset (Kaggle):** https://www.kaggle.com/xhlulu/medal-emnlp - **Dataset (Zenodo):** https://zenodo.org/record/4265632 - **Pretrained model:** https://huggingface.co/xhlu/electra-medal - **Leaderboard:** [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) ### Dataset Summary A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate ### Supported Tasks and Leaderboards Medical abbreviation disambiguation ### Languages English (en) ## Dataset Structure Each file is a table consisting of three columns: * text: The normalized content of an abstract * location: The location (index) of each abbreviation that was substituted * label: The word at that was substituted at the given location ### Data Instances An example from the train split is: ``` {'abstract_id': 14145090, 'text': 'velvet antlers vas are commonly used in traditional chinese medicine and invigorant and contain many PET components for health promotion the velvet antler peptide svap is one of active components in vas based on structural study the svap interacts with tgfβ receptors and disrupts the tgfβ pathway we hypothesized that svap prevents cardiac fibrosis from pressure overload by blocking tgfβ signaling SDRs underwent TAC tac or a sham operation T3 one month rats received either svap mgkgday or vehicle for an additional one month tac surgery induced significant cardiac dysfunction FB activation and fibrosis these effects were improved by treatment with svap in the heart tissue tac remarkably increased the expression of tgfβ and connective tissue growth factor ctgf ROS species C2 and the phosphorylation C2 of smad and ERK kinases erk svap inhibited the increases in reactive oxygen species C2 ctgf expression and the phosphorylation of smad and erk but not tgfβ expression in cultured cardiac fibroblasts angiotensin ii ang ii had similar effects compared to tac surgery such as increases in αsmapositive CFs and collagen synthesis svap eliminated these effects by disrupting tgfβ IB to its receptors and blocking ang iitgfβ downstream signaling these results demonstrated that svap has antifibrotic effects by blocking the tgfβ pathway in CFs', 'location': [63], 'label': ['transverse aortic constriction']} ``` ### Data Fields The column types are: * text: content of the abstract as a string * location: index of the substitution as an integer * label: substitued word as a string ### Data Splits The following files are present: * `full_data.csv`: The full dataset with all 14M abstracts. * `train.csv`: The subset used to train the baseline and proposed models. * `valid.csv`: The subset used to validate the model during training for hyperparameter selection. * `test.csv`: The subset used to evaluate the model and report the results in the tables. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html). #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations Details on how the abbreviations were created can be found in section 2.2 (Dataset Creation) of the [ACL ClinicalNLP paper](https://aclanthology.org/2020.clinicalnlp-1.15.pdf). #### 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 Since the abstracts are written in English, the data is biased towards anglo-centric medical research. If you plan to use a model pre-trained on this dataset for a predominantly non-English community, it is important to verify whether there are negative biases present in your model, and ensure that they are correctly mitigated. For instance, you could fine-tune your dataset on a multilingual medical disambiguation dataset, or collect a dataset specific to your use case. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The ELECTRA model is licensed under [Apache 2.0](https://github.com/google-research/electra/blob/master/LICENSE). The license for the libraries used in this project (`transformers`, `pytorch`, etc.) can be found in their respective GitHub repository. Our model is released under a MIT license. The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html). By using this dataset, you are bound by the [terms and conditions](https://www.nlm.nih.gov/databases/download/terms_and_conditions_pubmed.html) specified by NLM: > INTRODUCTION > > Downloading data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions: No charges, usage fees or royalties are paid to NLM for this data. > > MEDLINE/PUBMED SPECIFIC TERMS > > NLM freely provides PubMed/MEDLINE data. Please note some PubMed/MEDLINE abstracts may be protected by copyright. > > GENERAL TERMS AND CONDITIONS > > * Users of the data agree to: > * acknowledge NLM as the source of the data by including the phrase "Courtesy of the U.S. National Library of Medicine" in a clear and conspicuous manner, > * properly use registration and/or trademark symbols when referring to NLM products, and > * not indicate or imply that NLM has endorsed its products/services/applications. > > * Users who republish or redistribute the data (services, products or raw data) agree to: > * maintain the most current version of all distributed data, or > * make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM. > > * These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data. > > * NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page. > > * NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates. ### Citation Information ``` @inproceedings{wen-etal-2020-medal, title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining", author = "Wen, Zhi and Lu, Xing Han and Reddy, Siva", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15", pages = "130--135", abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.", } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) and [@xhlulu](https://github.com/xhlulu)) for adding this dataset.

medal

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "paperswithcode_id": "medal", "pretty_name": "MeDAL", "tags": ["disambiguation"], "dataset_info": {"features": [{"name": "abstract_id", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "location", "sequence": "int32"}, {"name": "label", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3573399948, "num_examples": 3000000}, {"name": "test", "num_bytes": 1190766821, "num_examples": 1000000}, {"name": "validation", "num_bytes": 1191410723, "num_examples": 1000000}, {"name": "full", "num_bytes": 15536883723, "num_examples": 14393619}], "download_size": 21060929078, "dataset_size": 21492461215}}

2023-06-13T11:39:11+00:00

TAGS #task_categories-other #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-unknown #disambiguation #region-us

# Dataset Card for the MeDAL dataset ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - Repository: URL - Paper: URL - Dataset (Kaggle): URL - Dataset (Zenodo): URL - Pretrained model: URL - Leaderboard: - Point of Contact: ### Dataset Summary A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate ### Supported Tasks and Leaderboards Medical abbreviation disambiguation ### Languages English (en) ## Dataset Structure Each file is a table consisting of three columns: * text: The normalized content of an abstract * location: The location (index) of each abbreviation that was substituted * label: The word at that was substituted at the given location ### Data Instances An example from the train split is: ### Data Fields The column types are: * text: content of the abstract as a string * location: index of the substitution as an integer * label: substitued word as a string ### Data Splits The following files are present: * 'full_data.csv': The full dataset with all 14M abstracts. * 'URL': The subset used to train the baseline and proposed models. * 'URL': The subset used to validate the model during training for hyperparameter selection. * 'URL': The subset used to evaluate the model and report the results in the tables. ## Dataset Creation ### Curation Rationale ### Source Data The original dataset was retrieved and modified from the NLM website. #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations Details on how the abbreviations were created can be found in section 2.2 (Dataset Creation) of the ACL ClinicalNLP paper. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases Since the abstracts are written in English, the data is biased towards anglo-centric medical research. If you plan to use a model pre-trained on this dataset for a predominantly non-English community, it is important to verify whether there are negative biases present in your model, and ensure that they are correctly mitigated. For instance, you could fine-tune your dataset on a multilingual medical disambiguation dataset, or collect a dataset specific to your use case. ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The ELECTRA model is licensed under Apache 2.0. The license for the libraries used in this project ('transformers', 'pytorch', etc.) can be found in their respective GitHub repository. Our model is released under a MIT license. The original dataset was retrieved and modified from the NLM website. By using this dataset, you are bound by the terms and conditions specified by NLM: > INTRODUCTION > > Downloading data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions: No charges, usage fees or royalties are paid to NLM for this data. > > MEDLINE/PUBMED SPECIFIC TERMS > > NLM freely provides PubMed/MEDLINE data. Please note some PubMed/MEDLINE abstracts may be protected by copyright. > > GENERAL TERMS AND CONDITIONS > > * Users of the data agree to: > * acknowledge NLM as the source of the data by including the phrase "Courtesy of the U.S. National Library of Medicine" in a clear and conspicuous manner, > * properly use registration and/or trademark symbols when referring to NLM products, and > * not indicate or imply that NLM has endorsed its products/services/applications. > > * Users who republish or redistribute the data (services, products or raw data) agree to: > * maintain the most current version of all distributed data, or > * make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM. > > * These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data. > > * NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page. > > * NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates. ### Contributions Thanks to @Narsil and @xhlulu) for adding this dataset.

1c598b66d82a3ba91e1cd1214e91d0888b1474d8

# Dataset Card for MedDialog ## 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:** https://github.com/UCSD-AI4H/Medical-Dialogue-System - **Paper:** [MedDialog: Two Large-scale Medical Dialogue Datasets](https://arxiv.org/abs/2004.03329) - **Point of Contact:** [Pengtao Xie](mailto:[email protected]) ### Dataset Summary The MedDialog dataset (Chinese) contains conversations (in Chinese) between doctors and patients. It has 1.1 million dialogues and 4 million utterances. The data is continuously growing and more dialogues will be added. The raw dialogues are from haodf.com. All copyrights of the data belong to haodf.com. The MedDialog dataset (English) contains conversations (in English) between doctors and patients. It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com. Directions for using the pre-trained model using BERT using PyTorch is available in the Homepage. ### Supported Tasks and Leaderboards Closed domain qa ### Languages Monolingual. The datasets are in English (EN) and Chinese (ZH) ## Dataset Structure ### Data Instances There are 4 configurations: - Raw data: - en - zh - Processed data: - processed.en - processed.zh #### en Each consultation consists of the below: - ID - URL - Description of patient’s medical condition - Dialogue The dataset is built from [icliniq.com](https://www.icliniq.com/), [healthcaremagic.com](https://www.healthcaremagic.com/), [healthtap.com](https://www.healthtap.com/) and all copyrights of the data belong to these websites. #### zh Each consultation consists of the below: - ID - URL - Description of patient’s medical condition - Dialogue - (Optional) Diagnosis and suggestions. The dataset is built from [Haodf.com](https://www.haodf.com/) and all copyrights of the data belong to [Haodf.com](https://www.haodf.com/). One example for chinese is ``` { {'dialogue_id': 2, 'dialogue_turns': [{'speaker': '病人', 'utterance': '孩子哭闹时，鸡鸡旁边会肿起，情绪平静时肿块会消失，去一个私人诊所看过，说是疝气.如果确定是疝气，是不是一定要手术治疗？我孩子只有1岁10月，自愈的可能性大吗？如果一定要手术，这么小的孩子风险大吗？术后的恢复困难吗？谢谢.'}, {'speaker': '医生', 'utterance': '南方医的B超说得不清楚，可能是鞘膜积液，可到我医院复查一个B超。'}], 'dialogue_url': 'https://www.haodf.com/doctorteam/flow_team_6477251152.htm', 'file_name': '2020.txt'}, } ``` #### processed.en ``` { 'description': 'throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.', 'utterances': [ 'patient: throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.', "doctor: during this pandemic. throat pain can be from a strep throat infection (antibiotics needed), a cold or influenza or other virus, or from some other cause such as allergies or irritants. usually, a person sees the doctor (call first) if the sore throat is bothersome, recurrent, or doesn't go away quickly. covid-19 infections tend to have cough, whereas strep throat usually lacks cough but has more throat pain. (3/21/20)" ] } ``` #### processed.zh ``` { 'utterances': [ '病人：强制性脊柱炎，晚上睡觉翻身时腰骶骨区域疼痛，其他身体任何部位均不疼痛。', '医生：应该没有问题，但最好把图像上传看看。' ] } ``` ### Data Fields For generating the QA only the below fields have been considered: - ID : Consultatation Identifier (restarts for each file) - URL: The url link of the extracted conversation - Dialogue : The conversation between the doctor and the patient. These are arranged as below in the prepared dataset. Each item will be represented with these parameters. - "file_name": string - signifies the file from which the conversation was extracted - "dialogue_id": int32 - the dialogue id - "dialogue_url": string - url of the conversation - "dialogue_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english) #### processed.en - `description` (str): Description of the dialog. - `utterances` (list of str): Dialog utterances between patient and doctor. #### processed.zh - `utterances` (list of str): Dialog utterances between patient and doctor. ### Data Splits There are no data splits on the original raw data. The "train" split for each language contains: - en: 229674 examples - zh: 1921127 examples For processed configurations, data is split into train, validation and test, with the following number of examples: | | train | validation | test | |--------------|--------:|-----------:|-------:| | processed.en | 482 | 60 | 61 | | processed.zh | 2725989 | 340748 | 340754 | ## Dataset Creation ### Curation Rationale Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. ### 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 The authors claim that: - They scraped the data from the following websites: - MedDialog-EN: data was crawled from https://www.icliniq.com/ and https://www.healthcaremagic.com/ - MedDialog-CN: data was crawled from https://www.haodf.com/ - All copyrights of the data belong to the corresponding websites The [terms and conditions](https://www.icliniq.com/p/terms) (last updated on: 11th April 2022) of www.icliniq.com website state: > No person (including a User, Doctor, Alternative Medicine Practitioner, or Wellness Professional) shall copy, transfer, download, republish, sell, duplicate, or "scrape", for commercial or any other purpose whatsoever, the contents or information made available on the Platform including Directory Listing Services, academic articles, and queries, in whole or in part, in any medium whatsoever. The [terms and conditions](https://www.healthcaremagic.com/tc) (last updated: August 17, 2012) of www.healthcaremagic.com website stipulate: > You are prohibited from republishing, selling, duplicating, or "scraping" for commercial or any other purpose whatsoever any of the data or other information contained therein, in whole or in part, in any medium whatsoever. ### Citation Information ``` @article{chen2020meddiag, title={MedDialog: a large-scale medical dialogue dataset}, author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao}, journal={arXiv preprint arXiv:2004.03329}, year={2020} } ``` ### Contributions Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset.

medical_dialog

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["expert-generated", "found"], "language": ["en", "zh"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["closed-domain-qa"], "paperswithcode_id": "meddialog", "pretty_name": "MedDialog", "config_names": ["en", "zh"], "dataset_info": [{"config_name": "en", "features": [{"name": "file_name", "dtype": "string"}, {"name": "dialogue_id", "dtype": "int32"}, {"name": "dialogue_url", "dtype": "string"}, {"name": "dialogue_turns", "sequence": [{"name": "speaker", "dtype": {"class_label": {"names": {"0": "Patient", "1": "Doctor"}}}}, {"name": "utterance", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 290274759, "num_examples": 229674}], "download_size": 0, "dataset_size": 290274759}, {"config_name": "zh", "features": [{"name": "file_name", "dtype": "string"}, {"name": "dialogue_id", "dtype": "int32"}, {"name": "dialogue_url", "dtype": "string"}, {"name": "dialogue_turns", "sequence": [{"name": "speaker", "dtype": {"class_label": {"names": {"0": "\u75c5\u4eba", "1": "\u533b\u751f"}}}}, {"name": "utterance", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1092063621, "num_examples": 1921127}], "download_size": 0, "dataset_size": 1092063621}, {"config_name": "processed.en", "features": [{"name": "description", "dtype": "string"}, {"name": "utterances", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 370745, "num_examples": 482}, {"name": "validation", "num_bytes": 52145, "num_examples": 60}, {"name": "test", "num_bytes": 46514, "num_examples": 61}], "download_size": 524214, "dataset_size": 469404}, {"config_name": "processed.zh", "features": [{"name": "utterances", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1571262099, "num_examples": 2725989}, {"name": "validation", "num_bytes": 197117565, "num_examples": 340748}, {"name": "test", "num_bytes": 196526738, "num_examples": 340754}], "download_size": 2082354155, "dataset_size": 1964906402}], "viewer": false}

2023-09-18T08:07:35+00:00

TAGS #task_categories-question-answering #task_ids-closed-domain-qa #annotations_creators-found #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #language-Chinese #license-unknown #arxiv-2004.03329 #region-us

Dataset Card for MedDialog ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: URL * Paper: MedDialog: Two Large-scale Medical Dialogue Datasets * Point of Contact: Pengtao Xie ### Dataset Summary The MedDialog dataset (Chinese) contains conversations (in Chinese) between doctors and patients. It has 1.1 million dialogues and 4 million utterances. The data is continuously growing and more dialogues will be added. The raw dialogues are from URL. All copyrights of the data belong to URL. The MedDialog dataset (English) contains conversations (in English) between doctors and patients. It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from URL and URL. All copyrights of the data belong to URL and URL. Directions for using the pre-trained model using BERT using PyTorch is available in the Homepage. ### Supported Tasks and Leaderboards Closed domain qa ### Languages Monolingual. The datasets are in English (EN) and Chinese (ZH) Dataset Structure ----------------- ### Data Instances There are 4 configurations: * Raw data: + en + zh * Processed data: + URL + URL #### en Each consultation consists of the below: * ID * URL * Description of patient’s medical condition * Dialogue The dataset is built from URL, URL, URL and all copyrights of the data belong to these websites. #### zh Each consultation consists of the below: * ID * URL * Description of patient’s medical condition * Dialogue * (Optional) Diagnosis and suggestions. The dataset is built from URL and all copyrights of the data belong to URL. One example for chinese is #### URL #### URL ### Data Fields For generating the QA only the below fields have been considered: * ID : Consultatation Identifier (restarts for each file) * URL: The url link of the extracted conversation * Dialogue : The conversation between the doctor and the patient. These are arranged as below in the prepared dataset. Each item will be represented with these parameters. * "file\_name": string - signifies the file from which the conversation was extracted * "dialogue\_id": int32 - the dialogue id * "dialogue\_url": string - url of the conversation * "dialogue\_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english) #### URL * 'description' (str): Description of the dialog. * 'utterances' (list of str): Dialog utterances between patient and doctor. #### URL * 'utterances' (list of str): Dialog utterances between patient and doctor. ### Data Splits There are no data splits on the original raw data. The "train" split for each language contains: * en: 229674 examples * zh: 1921127 examples For processed configurations, data is split into train, validation and test, with the following number of examples: Dataset Creation ---------------- ### Curation Rationale Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The authors claim that: * They scraped the data from the following websites: + MedDialog-EN: data was crawled from URL and URL + MedDialog-CN: data was crawled from URL * All copyrights of the data belong to the corresponding websites The terms and conditions (last updated on: 11th April 2022) of URL website state: > > No person (including a User, Doctor, Alternative Medicine Practitioner, or Wellness Professional) shall copy, transfer, download, republish, sell, duplicate, or "scrape", for commercial or any other purpose whatsoever, the contents or information made available on the Platform including Directory Listing Services, academic articles, and queries, in whole or in part, in any medium whatsoever. > > > The terms and conditions (last updated: August 17, 2012) of URL website stipulate: > > You are prohibited from republishing, selling, duplicating, or "scraping" for commercial or any other purpose whatsoever any of the data or other information contained therein, in whole or in part, in any medium whatsoever. > > > ### Contributions Thanks to @vrindaprabhu for adding this dataset.

d91d9da9b2a843fc09ab9d48568d2a93bf58bd0d

# Dataset Card for [medical_questions_pairs] ## 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:** [Medical questions pairs repository](https://github.com/curai/medical-question-pair-dataset) - **Paper:** [Effective Transfer Learning for Identifying Similar Questions:Matching User Questions to COVID-19 FAQs](https://arxiv.org/abs/2008.13546) ### Dataset Summary This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. Doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of [HealthTap](https://github.com/durakkerem/Medical-Question-Answer-Datasets). Each question results in one similar and one different pair through the following instructions provided to the labelers: - Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response. e.g. "I'm a 22-y-o female" could become "My 26 year old daughter" - Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words. The first instruction generates a positive question pair (similar) and the second generates a negative question pair (different). With the above instructions, the task was intentionally framed such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial. ### Supported Tasks and Leaderboards - `text-classification` : The dataset can be used to train a model to identify similar and non similar medical question pairs. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances The dataset contains dr_id, question_1, question_2, label. 11 different doctors were used for this task so dr_id ranges from 1 to 11. The label is 1 if the question pair is similar and 0 otherwise. ### Data Fields - `dr_id`: 11 different doctors were used for this task so dr_id ranges from 1 to 11 - `question_1`: Original Question - `question_2`: Rewritten Question maintaining the same intent like Original Question - `label`: The label is 1 if the question pair is similar and 0 otherwise. ### Data Splits The dataset as of now consists of only one split(train) but can be split seperately based on the requirement | | train | |----------------------------|------:| | Non similar Question Pairs | 1524 | | Similar Question Pairs | 1524 | ## Dataset Creation Doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of [HealthTap](https://github.com/durakkerem/Medical-Question-Answer-Datasets). Each question results in one similar and one different pair through the following instructions provided to the labelers: - Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response. e.g. "I'm a 22-y-o female" could become "My 26 year old daughter" - Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words. The first instruction generates a positive question pair (similar) and the second generates a negative question pair (different). With the above instructions, the task was intentionally framed such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial. ### Curation Rationale [More Information Needed] ### Source Data 1524 patient-asked questions randomly sampled from the publicly available crawl of [HealthTap](https://github.com/durakkerem/Medical-Question-Answer-Datasets) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process Doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of [HealthTap](https://github.com/durakkerem/Medical-Question-Answer-Datasets). Each question results in one similar and one different pair through the following instructions provided to the labelers: - Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response. e.g. "I'm a 22-y-o female" could become "My 26 year old daughter" - Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words. The first instruction generates a positive question pair (similar) and the second generates a negative question pair (different). With the above instructions, the task was intentionally framed such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial. #### Who are the annotators? **Curai's doctors** ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### 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 ``` @misc{mccreery2020effective, title={Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs}, author={Clara H. McCreery and Namit Katariya and Anitha Kannan and Manish Chablani and Xavier Amatriain}, year={2020}, eprint={2008.13546}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` ### Contributions Thanks to [@tuner007](https://github.com/tuner007) for adding this dataset.

medical_questions_pairs

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "pretty_name": "MedicalQuestionsPairs", "dataset_info": {"features": [{"name": "dr_id", "dtype": "int32"}, {"name": "question_1", "dtype": "string"}, {"name": "question_2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": 0, "1": 1}}}}], "splits": [{"name": "train", "num_bytes": 701642, "num_examples": 3048}], "download_size": 313704, "dataset_size": 701642}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-01-04T14:27:42+00:00

TAGS #task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #arxiv-2008.13546 #region-us

Dataset Card for [medical\_questions\_pairs] ============================================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: Medical questions pairs repository * Paper: Effective Transfer Learning for Identifying Similar Questions:Matching User Questions to COVID-19 FAQs ### Dataset Summary This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. Doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of HealthTap. Each question results in one similar and one different pair through the following instructions provided to the labelers: * Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response. e.g. "I'm a 22-y-o female" could become "My 26 year old daughter" * Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words. The first instruction generates a positive question pair (similar) and the second generates a negative question pair (different). With the above instructions, the task was intentionally framed such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial. ### Supported Tasks and Leaderboards * 'text-classification' : The dataset can be used to train a model to identify similar and non similar medical question pairs. ### Languages The text in the dataset is in English. Dataset Structure ----------------- ### Data Instances The dataset contains dr\_id, question\_1, question\_2, label. 11 different doctors were used for this task so dr\_id ranges from 1 to 11. The label is 1 if the question pair is similar and 0 otherwise. ### Data Fields * 'dr\_id': 11 different doctors were used for this task so dr\_id ranges from 1 to 11 * 'question\_1': Original Question * 'question\_2': Rewritten Question maintaining the same intent like Original Question * 'label': The label is 1 if the question pair is similar and 0 otherwise. ### Data Splits The dataset as of now consists of only one split(train) but can be split seperately based on the requirement Dataset Creation ---------------- Doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of HealthTap. Each question results in one similar and one different pair through the following instructions provided to the labelers: * Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response. e.g. "I'm a 22-y-o female" could become "My 26 year old daughter" * Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words. The first instruction generates a positive question pair (similar) and the second generates a negative question pair (different). With the above instructions, the task was intentionally framed such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial. ### Curation Rationale ### Source Data 1524 patient-asked questions randomly sampled from the publicly available crawl of HealthTap #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process Doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of HealthTap. Each question results in one similar and one different pair through the following instructions provided to the labelers: * Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response. e.g. "I'm a 22-y-o female" could become "My 26 year old daughter" * Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words. The first instruction generates a positive question pair (similar) and the second generates a negative question pair (different). With the above instructions, the task was intentionally framed such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial. #### Who are the annotators? Curai's doctors ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @tuner007 for adding this dataset.

76b2db67f218ce7a92da597c01df17031f37a6d0

# Dataset Card for MENYO-20k ## 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/uds-lsv/menyo-20k_MT/ - **Paper:** [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Languages are English and Yoruba. ## Dataset Structure ### Data Instances An instance example: ``` {'translation': {'en': 'Unit 1: What is Creative Commons?', 'yo': 'Ìdá 1: Kín ni Creative Commons?' } } ``` ### Data Fields - `translation`: - `en`: English sentence. - `yo`: Yoruba sentence. ### Data Splits Training, validation and test splits are available. ## 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 The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use. The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE ### Citation Information If you use this dataset, please cite this paper: ``` @inproceedings{adelani-etal-2021-effect, title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation", author = "Adelani, David and Ruiter, Dana and Alabi, Jesujoba and Adebonojo, Damilola and Ayeni, Adesina and Adeyemi, Mofe and Awokoya, Ayodele Esther and Espa{\~n}a-Bonet, Cristina", booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)", month = aug, year = "2021", address = "Virtual", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2021.mtsummit-research.6", pages = "61--75", abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.", } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.

menyo20k_mt

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "found"], "language_creators": ["found"], "language": ["en", "yo"], "license": ["cc-by-nc-4.0"], "multilinguality": ["translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "menyo-20k", "pretty_name": "MENYO-20k", "dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "yo"]}}}], "config_name": "menyo20k_mt", "splits": [{"name": "train", "num_bytes": 2551345, "num_examples": 10070}, {"name": "validation", "num_bytes": 870011, "num_examples": 3397}, {"name": "test", "num_bytes": 1905432, "num_examples": 6633}], "download_size": 5206234, "dataset_size": 5326788}}

2024-01-18T11:08:52+00:00

TAGS #task_categories-translation #annotations_creators-expert-generated #annotations_creators-found #language_creators-found #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-English #language-Yoruba #license-cc-by-nc-4.0 #arxiv-2103.08647 #region-us

# Dataset Card for MENYO-20k ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - Repository: URL - Paper: The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation - Leaderboard: - Point of Contact: ### Dataset Summary MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain). ### Supported Tasks and Leaderboards ### Languages Languages are English and Yoruba. ## Dataset Structure ### Data Instances An instance example: ### Data Fields - 'translation': - 'en': English sentence. - 'yo': Yoruba sentence. ### Data Splits Training, validation and test splits are available. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use. The dataset is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License: URL If you use this dataset, please cite this paper: ### Contributions Thanks to @yvonnegitau for adding this dataset.

4beb4cc7bac98e927c902865f03ac823966743ab

# Dataset Card for MetaLWOz ## 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:** [MetaLWOz Project Website](https://www.microsoft.com/en-us/research/project/metalwoz/) - **Paper:** [Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer](https://ieeexplore.ieee.org/abstract/document/9053599), and [Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation](https://arxiv.org/pdf/2003.01680.pdf) - **Point of Contact:** [Hannes Schulz](https://www.microsoft.com/en-us/research/people/haschulz/) ### Dataset Summary MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long. ### Supported Tasks and Leaderboards This dataset supports a range of task. - **Generative dialogue modeling** or `dialogue-modeling`: This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast -adaptation models fall into the research areas of transfer learning and meta learning. The text of the dialogues can be used to train a sequence model on the utterances. Example of sample input/output is given in section [Data Instances](#data-instances) ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances A data instance is a full multi-turn dialogue between two crowd-workers, one had the role of being a `bot`, and the other one was the `user`. Both were given a `domain` and a `task`. Each turn has a single utterance, e.g.: ``` Domain: Ski User Task: You want to know if there are good ski hills an hour’s drive from your current location. Bot Task: Tell the user that there are no ski hills in their immediate location. Bot: Hello how may I help you? User: Is there any good ski hills an hour’s drive from my current location? Bot: I’m sorry to inform you that there are no ski hills in your immediate location User: Can you help me find the nearest? Bot: Absolutely! It looks like you’re about 3 hours away from Bear Mountain. That seems to be the closest. User: Hmm.. sounds good Bot: Alright! I can help you get your lift tickets now!When will you be going? User: Awesome! please get me a ticket for 10pax Bot: You’ve got it. Anything else I can help you with? User: None. Thanks again! Bot: No problem! ``` Example of input/output for this dialog: ``` Input: dialog history = Hello how may I help you?; Is there any good ski hills an hour’s drive from my current location?; I’m sorry to inform you that there are no ski hills in your immediate location Output: user response = Can you help me find the nearest? ``` ### Data Fields Each dialogue instance has the following fields: - `id`: a unique ID identifying the dialog. - `user_id`: a unique ID identifying the user. - `bot_id`: a unique ID identifying the bot. - `domain`: a unique ID identifying the domain. Provides a mapping to tasks dataset. - `task_id`: a unique ID identifying the task. Provides a mapping to tasks dataset. - `turns`: the sequence of utterances alternating between `bot` and `user`, starting with a prompt from `bot`. Each task instance has following fields: - `task_id`: a unique ID identifying the task. - `domain`: a unique ID identifying the domain. - `bot_prompt`: The task specification for bot. - `bot_role`: The domain oriented role of bot. - `user_prompt`: The task specification for user. - `user_role`: The domain oriented role of user. ### Data Splits The dataset is split into a `train` and `test` split with the following sizes: | | Training MetaLWOz | Evaluation MetaLWOz | Combined | | ----- | ------ | ----- | ---- | | Total Domains | 47 | 4 | 51 | | Total Tasks | 226 | 14 | 240 | | Total Dialogs | 37884 | 2319 | 40203 | Below are the various statistics of the dataset: | Statistic | Mean | Minimum | Maximum | | ----- | ------ | ----- | ---- | | Number of tasks per domain | 4.8 | 3 | 11 | | Number of dialogs per domain | 806.0 | 288 | 1990 | | Number of dialogs per task | 167.6 | 32 | 285 | | Number of turns per dialog | 11.4 | 10 | 46 | ## 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 The dataset v1 version is created by team of researchers from Microsoft Research (Montreal, Canada) ### Licensing Information The dataset is released under [Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view) ### Citation Information You can cite the following for the various versions of MetaLWOz: Version 1.0 ``` @InProceedings{shalyminov2020fast, author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes}, title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer}, booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2020}, month = {April}, url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a -hybrid-generative-retrieval-transformer/}, } ``` ### Contributions Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.

meta_woz

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["dialogue-modeling"], "paperswithcode_id": "metalwoz", "pretty_name": "Meta-Learning Wizard-of-Oz", "license_details": "Microsoft Research Data License Agreement", "dataset_info": [{"config_name": "dialogues", "features": [{"name": "id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "bot_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "task_id", "dtype": "string"}, {"name": "turns", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 19999218, "num_examples": 37884}, {"name": "test", "num_bytes": 1284287, "num_examples": 2319}], "download_size": 8629863, "dataset_size": 21283505}, {"config_name": "tasks", "features": [{"name": "task_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "bot_prompt", "dtype": "string"}, {"name": "bot_role", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}, {"name": "user_role", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 73768, "num_examples": 227}, {"name": "test", "num_bytes": 4351, "num_examples": 14}], "download_size": 8629863, "dataset_size": 78119}]}

2024-01-18T11:08:54+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #arxiv-2003.01680 #region-us

Dataset Card for MetaLWOz ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: MetaLWOz Project Website * Paper: Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer, and Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation * Point of Contact: Hannes Schulz ### Dataset Summary MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long. ### Supported Tasks and Leaderboards This dataset supports a range of task. * Generative dialogue modeling or 'dialogue-modeling': This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast -adaptation models fall into the research areas of transfer learning and meta learning. The text of the dialogues can be used to train a sequence model on the utterances. Example of sample input/output is given in section Data Instances ### Languages The text in the dataset is in English ('en'). Dataset Structure ----------------- ### Data Instances A data instance is a full multi-turn dialogue between two crowd-workers, one had the role of being a 'bot', and the other one was the 'user'. Both were given a 'domain' and a 'task'. Each turn has a single utterance, e.g.: Example of input/output for this dialog: ### Data Fields Each dialogue instance has the following fields: * 'id': a unique ID identifying the dialog. * 'user\_id': a unique ID identifying the user. * 'bot\_id': a unique ID identifying the bot. * 'domain': a unique ID identifying the domain. Provides a mapping to tasks dataset. * 'task\_id': a unique ID identifying the task. Provides a mapping to tasks dataset. * 'turns': the sequence of utterances alternating between 'bot' and 'user', starting with a prompt from 'bot'. Each task instance has following fields: * 'task\_id': a unique ID identifying the task. * 'domain': a unique ID identifying the domain. * 'bot\_prompt': The task specification for bot. * 'bot\_role': The domain oriented role of bot. * 'user\_prompt': The task specification for user. * 'user\_role': The domain oriented role of user. ### Data Splits The dataset is split into a 'train' and 'test' split with the following sizes: Below are the various statistics of the dataset: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The dataset v1 version is created by team of researchers from Microsoft Research (Montreal, Canada) ### Licensing Information The dataset is released under Microsoft Research Data License Agreement You can cite the following for the various versions of MetaLWOz: Version 1.0 ### Contributions Thanks to @pacman100 for adding this dataset.

103efb8ccbd7dc149905dbd3ed578ec955d65884

# Dataset Card for #MeTooMA 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://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU - **Repository:** https://github.com/midas-research/MeTooMA - **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary - The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories. - This dataset includes more data points and has more labels than any of the previous datasets that contain social media posts about sexual abuse discloures. Please refer to the Related Datasets of the publication for a detailed information about this. - Due to Twitters development policies, the authors provide only the tweet IDs and corresponding labels, other data can be fetched via Twitter API. - The data has been labelled by experts, with the majority taken into the account for deciding the final label. - The authors provide these labels for each of the tweets. - Relevance - Directed Hate - Generalized Hate - Sarcasm - Allegation - Justification - Refutation - Support - Oppose - The definitions for each task/label is in the main publication. - Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data extracted from this dataset. - The language of all the tweets in this dataset is English - Time period: October 2018 - December 2018 - Suggested Use Cases of this dataset: - Evaluating usage of linguistic acts such as: hate-spech and sarcasm in the incontext of public sexual abuse discloures. - Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations. - Identifying how influential people were potrayed on public platform in the events of mass social movements. - Polarization analysis based on graph simulations of social nodes of users involved in the #MeToo movement. ### Supported Tasks and Leaderboards Multi Label and Multi-Class Classification ### Languages English ## Dataset Structure - The dataset is structured into CSV format with TweetID and accompanying labels. - Train and Test sets are split into respective files. ### Data Instances Tweet ID and the appropriate labels ### Data Fields Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID ### Data Splits - Train: 7979 - Test: 1996 ## Dataset Creation ### Curation Rationale - Twitter was the major source of all the public discloures of sexual abuse incidents during the #MeToo movement. - People expressed their opinions over issues which were previously missing from the social media space. - This provides an option to study the linguistic behaviours of social media users in an informal setting, therefore the authors decide to curate this annotated dataset. - The authors expect this dataset would be of great interest and use to both computational and socio-linguists. - For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media. ### Source Data - Source of all the data points in this dataset is Twitter social media platform. #### Initial Data Collection and Normalization - All the tweets are mined from Twitter with initial search paramters identified using keywords from the #MeToo movement. - Redundant keywords were removed based on manual inspection. - Public streaming APIs of Twitter were used for querying with the selected keywords. - Based on text de-duplication and cosine similarity score, the set of tweets were pruned. - Non english tweets were removed. - The final set was labelled by experts with the majority label taken into the account for deciding the final label. - Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292 #### Who are the source language producers? Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292 ### Annotations #### Annotation process - The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature. - The annotators are domain experts having degress in advanced clinical psychology and gender studies. - They were provided a guidelines document with instructions about each task and its definitions, labels and examples. - They studied the document, worked a few examples to get used to this annotation task. - They also provided feedback for improving the class definitions. - The annotation process is not mutually exclusive, implying that presence of one label does not mean the absence of the other one. #### Who are the annotators? - The annotators are domain experts having a degree in clinical psychology and gender studies. - Please refer to the accompnaying paper for a detailed annotation process. ### Personal and Sensitive Information - Considering Twitters policy for distribution of data, only Tweet ID and applicable labels are shared for the public use. - It is highly encouraged to use this dataset for scientific purposes only. - This dataset collection completely follows the Twitter mandated guidelines for distribution and usage. ## Considerations for Using the Data ### Social Impact of Dataset - The authors of this dataset do not intend to conduct a population centric analysis of #MeToo movement on Twitter. - The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these should be used to assist already existing human intervention tools and therapies. - Enough care has been taken to ensure that this work comes of as trying to target a specific person for their personal stance of issues pertaining to the #MeToo movement. - The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner. - Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset and social impact of this work. ### Discussion of Biases - The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of community affected by sexual abuse. - Any work undertaken on this dataset should aim to minimize the bias against minority groups which might amplified in cases of sudden outburst of public reactions over sensitive social media discussions. ### Other Known Limitations - Considering privacy concerns, social media practitioners should be aware of making automated interventions to aid the victims of sexual abuse as some people might not prefer to disclose their notions. - Concerned social media users might also repeal their social information, if they found out that their information is being used for computational purposes, hence it is important seek subtle individual consent before trying to profile authors involved in online discussions to uphold personal privacy. ## Additional Information Please refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU ### Dataset Curators - 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] (http://midas.iiitd.edu.in) appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus. - If interested in commercial use of the corpus, send email to [email protected]. - 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 - 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 social media data. - if interested in a collaborative research project. ### Licensing Information [More Information Needed] ### Citation Information Please cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292 ``` @article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={&lt;p&gt;In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.&lt;/p&#38;gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} } ``` ### Contributions Thanks to [@akash418](https://github.com/akash418) for adding this dataset.

metooma

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification", "text-retrieval"], "task_ids": ["multi-class-classification", "multi-label-classification"], "paperswithcode_id": "metooma", "pretty_name": "#MeTooMA dataset", "dataset_info": {"features": [{"name": "TweetId", "dtype": "string"}, {"name": "Text_Only_Informative", "dtype": {"class_label": {"names": {"0": "Text Non Informative", "1": "Text Informative"}}}}, {"name": "Image_Only_Informative", "dtype": {"class_label": {"names": {"0": "Image Non Informative", "1": "Image Informative"}}}}, {"name": "Directed_Hate", "dtype": {"class_label": {"names": {"0": "Directed Hate Absent", "1": "Directed Hate Present"}}}}, {"name": "Generalized_Hate", "dtype": {"class_label": {"names": {"0": "Generalized Hate Absent", "1": "Generalized Hate Present"}}}}, {"name": "Sarcasm", "dtype": {"class_label": {"names": {"0": "Sarcasm Absent", "1": "Sarcasm Present"}}}}, {"name": "Allegation", "dtype": {"class_label": {"names": {"0": "Allegation Absent", "1": "Allegation Present"}}}}, {"name": "Justification", "dtype": {"class_label": {"names": {"0": "Justification Absent", "1": "Justification Present"}}}}, {"name": "Refutation", "dtype": {"class_label": {"names": {"0": "Refutation Absent", "1": "Refutation Present"}}}}, {"name": "Support", "dtype": {"class_label": {"names": {"0": "Support Absent", "1": "Support Present"}}}}, {"name": "Oppose", "dtype": {"class_label": {"names": {"0": "Oppose Absent", "1": "Oppose Present"}}}}], "splits": [{"name": "train", "num_bytes": 821738, "num_examples": 7978}, {"name": "test", "num_bytes": 205489, "num_examples": 1995}], "download_size": 408889, "dataset_size": 1027227}}

2024-01-18T11:08:57+00:00

TAGS #task_categories-text-classification #task_categories-text-retrieval #task_ids-multi-class-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc0-1.0 #region-us

# Dataset Card for #MeTooMA dataset ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL/URL - Point of Contact: ### Dataset Summary - The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories. - This dataset includes more data points and has more labels than any of the previous datasets that contain social media posts about sexual abuse discloures. Please refer to the Related Datasets of the publication for a detailed information about this. - Due to Twitters development policies, the authors provide only the tweet IDs and corresponding labels, other data can be fetched via Twitter API. - The data has been labelled by experts, with the majority taken into the account for deciding the final label. - The authors provide these labels for each of the tweets. - Relevance - Directed Hate - Generalized Hate - Sarcasm - Allegation - Justification - Refutation - Support - Oppose - The definitions for each task/label is in the main publication. - Please refer to the accompanying paper URL for statistical analysis on the textual data extracted from this dataset. - The language of all the tweets in this dataset is English - Time period: October 2018 - December 2018 - Suggested Use Cases of this dataset: - Evaluating usage of linguistic acts such as: hate-spech and sarcasm in the incontext of public sexual abuse discloures. - Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations. - Identifying how influential people were potrayed on public platform in the events of mass social movements. - Polarization analysis based on graph simulations of social nodes of users involved in the #MeToo movement. ### Supported Tasks and Leaderboards Multi Label and Multi-Class Classification ### Languages English ## Dataset Structure - The dataset is structured into CSV format with TweetID and accompanying labels. - Train and Test sets are split into respective files. ### Data Instances Tweet ID and the appropriate labels ### Data Fields Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID ### Data Splits - Train: 7979 - Test: 1996 ## Dataset Creation ### Curation Rationale - Twitter was the major source of all the public discloures of sexual abuse incidents during the #MeToo movement. - People expressed their opinions over issues which were previously missing from the social media space. - This provides an option to study the linguistic behaviours of social media users in an informal setting, therefore the authors decide to curate this annotated dataset. - The authors expect this dataset would be of great interest and use to both computational and socio-linguists. - For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media. ### Source Data - Source of all the data points in this dataset is Twitter social media platform. #### Initial Data Collection and Normalization - All the tweets are mined from Twitter with initial search paramters identified using keywords from the #MeToo movement. - Redundant keywords were removed based on manual inspection. - Public streaming APIs of Twitter were used for querying with the selected keywords. - Based on text de-duplication and cosine similarity score, the set of tweets were pruned. - Non english tweets were removed. - The final set was labelled by experts with the majority label taken into the account for deciding the final label. - Please refer to this paper for detailed information: URL/URL #### Who are the source language producers? Please refer to this paper for detailed information: URL/URL ### Annotations #### Annotation process - The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature. - The annotators are domain experts having degress in advanced clinical psychology and gender studies. - They were provided a guidelines document with instructions about each task and its definitions, labels and examples. - They studied the document, worked a few examples to get used to this annotation task. - They also provided feedback for improving the class definitions. - The annotation process is not mutually exclusive, implying that presence of one label does not mean the absence of the other one. #### Who are the annotators? - The annotators are domain experts having a degree in clinical psychology and gender studies. - Please refer to the accompnaying paper for a detailed annotation process. ### Personal and Sensitive Information - Considering Twitters policy for distribution of data, only Tweet ID and applicable labels are shared for the public use. - It is highly encouraged to use this dataset for scientific purposes only. - This dataset collection completely follows the Twitter mandated guidelines for distribution and usage. ## Considerations for Using the Data ### Social Impact of Dataset - The authors of this dataset do not intend to conduct a population centric analysis of #MeToo movement on Twitter. - The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these should be used to assist already existing human intervention tools and therapies. - Enough care has been taken to ensure that this work comes of as trying to target a specific person for their personal stance of issues pertaining to the #MeToo movement. - The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner. - Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset and social impact of this work. ### Discussion of Biases - The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of community affected by sexual abuse. - Any work undertaken on this dataset should aim to minimize the bias against minority groups which might amplified in cases of sudden outburst of public reactions over sensitive social media discussions. ### Other Known Limitations - Considering privacy concerns, social media practitioners should be aware of making automated interventions to aid the victims of sexual abuse as some people might not prefer to disclose their notions. - Concerned social media users might also repeal their social information, if they found out that their information is being used for computational purposes, hence it is important seek subtle individual consent before trying to profile authors involved in online discussions to uphold personal privacy. ## Additional Information Please refer to this link: URL ### Dataset Curators - 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] (URL) appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus. - If interested in commercial use of the corpus, send email to midas@URL. - 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 - 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 social media data. - if interested in a collaborative research project. ### Licensing Information Please cite the following publication if you make use of the dataset: URL ### Contributions Thanks to @akash418 for adding this dataset.

bae1927bc97c785b022377025899d06323bcc2f1

# Dataset Card for MetRec ## 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:** [Metrec](https://github.com/zaidalyafeai/MetRec) - **Repository:** [Metrec repository](https://github.com/zaidalyafeai/MetRec) - **Paper:** [MetRec: A dataset for meter classification of arabic poetry](https://www.sciencedirect.com/science/article/pii/S2352340920313792) - **Point of Contact:** [Zaid Alyafeai](mailto:[email protected]) ### Dataset Summary The dataset contains the verses and their corresponding meter classes. Meter classes are represented as numbers from 0 to 13. The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification. The train dataset contains 47,124 records and the test dataset contains 8,316 records. ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340920313792). A benchmark is acheived on this [paper](https://www.sciencedirect.com/science/article/pii/S016786552030204X). ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A typical data point comprises a label which is out of 13 classes and a verse part of poem. ### Data Fields [N/A] ### Data Splits The data is split into a training and testing. The split is organized as the following | | train | test | |------------|-------:|------:| | data split | 47,124 | 8,316 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization The dataset was collected from [Aldiwan](https://www.aldiwan.net/). #### Who are the source language producers? The poems are from different poets. ### 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 [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] ``` @article{metrec2020, title={MetRec: A dataset for meter classification of arabic poetry}, author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan}, journal={Data in Brief}, year={2020}, publisher={Elsevier} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset.

metrec

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "paperswithcode_id": "metrec", "pretty_name": "MetRec", "tags": ["poetry-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "saree", "1": "kamel", "2": "mutakareb", "3": "mutadarak", "4": "munsareh", "5": "madeed", "6": "mujtath", "7": "ramal", "8": "baseet", "9": "khafeef", "10": "taweel", "11": "wafer", "12": "hazaj", "13": "rajaz"}}}}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 5874919, "num_examples": 47124}, {"name": "test", "num_bytes": 1037577, "num_examples": 8316}], "download_size": 2267882, "dataset_size": 6912496}}

2024-01-18T11:08:58+00:00

TAGS #task_categories-text-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Arabic #license-unknown #poetry-classification #region-us

Dataset Card for MetRec ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: Metrec * Repository: Metrec repository * Paper: MetRec: A dataset for meter classification of arabic poetry * Point of Contact: Zaid Alyafeai ### Dataset Summary The dataset contains the verses and their corresponding meter classes. Meter classes are represented as numbers from 0 to 13. The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification. The train dataset contains 47,124 records and the test dataset contains 8,316 records. ### Supported Tasks and Leaderboards The dataset was published on this paper. A benchmark is acheived on this paper. ### Languages The dataset is based on Arabic. Dataset Structure ----------------- ### Data Instances A typical data point comprises a label which is out of 13 classes and a verse part of poem. ### Data Fields [N/A] ### Data Splits The data is split into a training and testing. The split is organized as the following Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The dataset was collected from Aldiwan. #### Who are the source language producers? The poems are from different poets. ### Annotations The dataset does not contain any additional annotations. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @zaidalyafeai for adding this dataset.

f138dd789c15a3b25eee181a4dc4d330741c4c39

# Dataset Card for MIAM ## 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:** [N/A] - **Repository:** [N/A] - **Paper:** [N/A] - **Leaderboard:** [N/A] - **Point of Contact:** [N/A] ### Dataset Summary Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. Datasets are in English, French, German, Italian and Spanish. They cover a variety of domains including spontaneous speech, scripted scenarios, and joint task completion. All datasets contain dialogue act labels. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English, French, German, Italian, Spanish. ## Dataset Structure ### Data Instances #### Dihana Corpus For the `dihana` configuration one example from the dataset is: ``` { 'Speaker': 'U', 'Utterance': 'Hola , quería obtener el horario para ir a Valencia', 'Dialogue_Act': 9, # 'Pregunta' ('Request') 'Dialogue_ID': '0', 'File_ID': 'B209_BA5c3', } ``` #### iLISTEN Corpus For the `ilisten` configuration one example from the dataset is: ``` { 'Speaker': 'T_11_U11', 'Utterance': 'ok, grazie per le informazioni', 'Dialogue_Act': 6, # 'KIND-ATTITUDE_SMALL-TALK' 'Dialogue_ID': '0', } ``` #### LORIA Corpus For the `loria` configuration one example from the dataset is: ``` { 'Speaker': 'Samir', 'Utterance': 'Merci de votre visite, bonne chance, et à la prochaine !', 'Dialogue_Act': 21, # 'quit' 'Dialogue_ID': '5', 'File_ID': 'Dial_20111128_113927', } ``` #### HCRC MapTask Corpus For the `maptask` configuration one example from the dataset is: ``` { 'Speaker': 'f', 'Utterance': 'is it underneath the rope bridge or to the left', 'Dialogue_Act': 6, # 'query_w' 'Dialogue_ID': '0', 'File_ID': 'q4ec1', } ``` #### VERBMOBIL For the `vm2` configuration one example from the dataset is: ``` { 'Utterance': 'ja was sind viereinhalb Stunden Bahngerüttel gegen siebzig Minuten Turbulenzen im Flugzeug', 'Utterance': 'Utterance', 'Dialogue_Act': 'Dialogue_Act', # 'INFORM' 'Speaker': 'A', 'Dialogue_ID': '66', } ``` ### Data Fields For the `dihana` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'Afirmacion' (0) [Feedback_positive], 'Apertura' (1) [Opening], 'Cierre' (2) [Closing], 'Confirmacion' (3) [Acknowledge], 'Espera' (4) [Hold], 'Indefinida' (5) [Undefined], 'Negacion' (6) [Feedback_negative], 'No_entendido' (7) [Request_clarify], 'Nueva_consulta' (8) [New_request], 'Pregunta' (9) [Request] or 'Respuesta' (10) [Reply]. - `Dialogue_ID`: identifier of the dialogue as a string. - `File_ID`: identifier of the source file as a string. For the `ilisten` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'AGREE' (0), 'ANSWER' (1), 'CLOSING' (2), 'ENCOURAGE-SORRY' (3), 'GENERIC-ANSWER' (4), 'INFO-REQUEST' (5), 'KIND-ATTITUDE_SMALL-TALK' (6), 'OFFER-GIVE-INFO' (7), 'OPENING' (8), 'PERSUASION-SUGGEST' (9), 'QUESTION' (10), 'REJECT' (11), 'SOLICITATION-REQ_CLARIFICATION' (12), 'STATEMENT' (13) or 'TALK-ABOUT-SELF' (14). - `Dialogue_ID`: identifier of the dialogue as a string. For the `loria` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'ack' (0), 'ask' (1), 'find_mold' (2), 'find_plans' (3), 'first_step' (4), 'greet' (5), 'help' (6), 'inform' (7), 'inform_engine' (8), 'inform_job' (9), 'inform_material_space' (10), 'informer_conditioner' (11), 'informer_decoration' (12), 'informer_elcomps' (13), 'informer_end_manufacturing' (14), 'kindAtt' (15), 'manufacturing_reqs' (16), 'next_step' (17), 'no' (18), 'other' (19), 'quality_control' (20), 'quit' (21), 'reqRep' (22), 'security_policies' (23), 'staff_enterprise' (24), 'staff_job' (25), 'studies_enterprise' (26), 'studies_job' (27), 'todo_failure' (28), 'todo_irreparable' (29), 'yes' (30) - `Dialogue_ID`: identifier of the dialogue as a string. - `File_ID`: identifier of the source file as a string. For the `maptask` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'acknowledge' (0), 'align' (1), 'check' (2), 'clarify' (3), 'explain' (4), 'instruct' (5), 'query_w' (6), 'query_yn' (7), 'ready' (8), 'reply_n' (9), 'reply_w' (10) or 'reply_y' (11). - `Dialogue_ID`: identifier of the dialogue as a string. - `File_ID`: identifier of the source file as a string. For the `vm2` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialogue act label of the utterance. It can be one of 'ACCEPT' (0), 'BACKCHANNEL' (1), 'BYE' (2), 'CLARIFY' (3), 'CLOSE' (4), 'COMMIT' (5), 'CONFIRM' (6), 'DEFER' (7), 'DELIBERATE' (8), 'DEVIATE_SCENARIO' (9), 'EXCLUDE' (10), 'EXPLAINED_REJECT' (11), 'FEEDBACK' (12), 'FEEDBACK_NEGATIVE' (13), 'FEEDBACK_POSITIVE' (14), 'GIVE_REASON' (15), 'GREET' (16), 'INFORM' (17), 'INIT' (18), 'INTRODUCE' (19), 'NOT_CLASSIFIABLE' (20), 'OFFER' (21), 'POLITENESS_FORMULA' (22), 'REJECT' (23), 'REQUEST' (24), 'REQUEST_CLARIFY' (25), 'REQUEST_COMMENT' (26), 'REQUEST_COMMIT' (27), 'REQUEST_SUGGEST' (28), 'SUGGEST' (29), 'THANK' (30). - `Speaker`: Speaker as a string. - `Dialogue_ID`: identifier of the dialogue as a string. ### Data Splits | Dataset name | Train | Valid | Test | | ------------ | ----- | ----- | ---- | | dihana | 19063 | 2123 | 2361 | | ilisten | 1986 | 230 | 971 | | loria | 8465 | 942 | 1047 | | maptask | 25382 | 5221 | 5335 | | vm2 | 25060 | 2860 | 2855 | ## 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 Anonymous. ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{colombo-etal-2021-code, title = "Code-switched inspired losses for spoken dialog representations", author = "Colombo, Pierre and Chapuis, Emile and Labeau, Matthieu and Clavel, Chlo{\'e}", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.656", doi = "10.18653/v1/2021.emnlp-main.656", pages = "8320--8337", abstract = "Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (\textit{i.e} multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings.", } ``` ### Contributions Thanks to [@eusip](https://github.com/eusip) and [@PierreColombo](https://github.com/PierreColombo) for adding this dataset.

miam

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["de", "en", "es", "fr", "it"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask", "text-classification"], "task_ids": ["dialogue-modeling", "language-modeling", "masked-language-modeling"], "pretty_name": "MIAM", "config_names": ["dihana", "ilisten", "loria", "maptask", "vm2"], "tags": ["dialogue-act-classification"], "dataset_info": [{"config_name": "dihana", "features": [{"name": "Speaker", "dtype": "string"}, {"name": "Utterance", "dtype": "string"}, {"name": "Dialogue_Act", "dtype": "string"}, {"name": "Dialogue_ID", "dtype": "string"}, {"name": "File_ID", "dtype": "string"}, {"name": "Label", "dtype": {"class_label": {"names": {"0": "Afirmacion", "1": "Apertura", "2": "Cierre", "3": "Confirmacion", "4": "Espera", "5": "Indefinida", "6": "Negacion", "7": "No_entendido", "8": "Nueva_consulta", "9": "Pregunta", "10": "Respuesta"}}}}, {"name": "Idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 1946735, "num_examples": 19063}, {"name": "validation", "num_bytes": 216498, "num_examples": 2123}, {"name": "test", "num_bytes": 238446, "num_examples": 2361}], "download_size": 1777267, "dataset_size": 2401679}, {"config_name": "ilisten", "features": [{"name": "Speaker", "dtype": "string"}, {"name": "Utterance", "dtype": "string"}, {"name": "Dialogue_Act", "dtype": "string"}, {"name": "Dialogue_ID", "dtype": "string"}, {"name": "Label", "dtype": {"class_label": {"names": {"0": "AGREE", "1": "ANSWER", "2": "CLOSING", "3": "ENCOURAGE-SORRY", "4": "GENERIC-ANSWER", "5": "INFO-REQUEST", "6": "KIND-ATTITUDE_SMALL-TALK", "7": "OFFER-GIVE-INFO", "8": "OPENING", "9": "PERSUASION-SUGGEST", "10": "QUESTION", "11": "REJECT", "12": "SOLICITATION-REQ_CLARIFICATION", "13": "STATEMENT", "14": "TALK-ABOUT-SELF"}}}}, {"name": "Idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 244336, "num_examples": 1986}, {"name": "validation", "num_bytes": 33988, "num_examples": 230}, {"name": "test", "num_bytes": 145376, "num_examples": 971}], "download_size": 349993, "dataset_size": 423700}, {"config_name": "loria", "features": [{"name": "Speaker", "dtype": "string"}, {"name": "Utterance", "dtype": "string"}, {"name": "Dialogue_Act", "dtype": "string"}, {"name": "Dialogue_ID", "dtype": "string"}, {"name": "File_ID", "dtype": "string"}, {"name": "Label", "dtype": {"class_label": {"names": {"0": "ack", "1": "ask", "2": "find_mold", "3": "find_plans", "4": "first_step", "5": "greet", "6": "help", "7": "inform", "8": "inform_engine", "9": "inform_job", "10": "inform_material_space", "11": "informer_conditioner", "12": "informer_decoration", "13": "informer_elcomps", "14": "informer_end_manufacturing", "15": "kindAtt", "16": "manufacturing_reqs", "17": "next_step", "18": "no", "19": "other", "20": "quality_control", "21": "quit", "22": "reqRep", "23": "security_policies", "24": "staff_enterprise", "25": "staff_job", "26": "studies_enterprise", "27": "studies_job", "28": "todo_failure", "29": "todo_irreparable", "30": "yes"}}}}, {"name": "Idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 1208730, "num_examples": 8465}, {"name": "validation", "num_bytes": 133829, "num_examples": 942}, {"name": "test", "num_bytes": 149855, "num_examples": 1047}], "download_size": 1221132, "dataset_size": 1492414}, {"config_name": "maptask", "features": [{"name": "Speaker", "dtype": "string"}, {"name": "Utterance", "dtype": "string"}, {"name": "Dialogue_Act", "dtype": "string"}, {"name": "Dialogue_ID", "dtype": "string"}, {"name": "File_ID", "dtype": "string"}, {"name": "Label", "dtype": {"class_label": {"names": {"0": "acknowledge", "1": "align", "2": "check", "3": "clarify", "4": "explain", "5": "instruct", "6": "query_w", "7": "query_yn", "8": "ready", "9": "reply_n", "10": "reply_w", "11": "reply_y"}}}}, {"name": "Idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 1910120, "num_examples": 25382}, {"name": "validation", "num_bytes": 389879, "num_examples": 5221}, {"name": "test", "num_bytes": 396947, "num_examples": 5335}], "download_size": 1729021, "dataset_size": 2696946}, {"config_name": "vm2", "features": [{"name": "Utterance", "dtype": "string"}, {"name": "Dialogue_Act", "dtype": "string"}, {"name": "Speaker", "dtype": "string"}, {"name": "Dialogue_ID", "dtype": "string"}, {"name": "Label", "dtype": {"class_label": {"names": {"0": "ACCEPT", "1": "BACKCHANNEL", "2": "BYE", "3": "CLARIFY", "4": "CLOSE", "5": "COMMIT", "6": "CONFIRM", "7": "DEFER", "8": "DELIBERATE", "9": "DEVIATE_SCENARIO", "10": "EXCLUDE", "11": "EXPLAINED_REJECT", "12": "FEEDBACK", "13": "FEEDBACK_NEGATIVE", "14": "FEEDBACK_POSITIVE", "15": "GIVE_REASON", "16": "GREET", "17": "INFORM", "18": "INIT", "19": "INTRODUCE", "20": "NOT_CLASSIFIABLE", "21": "OFFER", "22": "POLITENESS_FORMULA", "23": "REJECT", "24": "REQUEST", "25": "REQUEST_CLARIFY", "26": "REQUEST_COMMENT", "27": "REQUEST_COMMIT", "28": "REQUEST_SUGGEST", "29": "SUGGEST", "30": "THANK"}}}}, {"name": "Idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 1869254, "num_examples": 25060}, {"name": "validation", "num_bytes": 209390, "num_examples": 2860}, {"name": "test", "num_bytes": 209032, "num_examples": 2855}], "download_size": 1641453, "dataset_size": 2287676}]}

2024-01-18T11:09:00+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_categories-text-classification #task_ids-dialogue-modeling #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-German #language-English #language-Spanish #language-French #language-Italian #license-cc-by-sa-4.0 #dialogue-act-classification #region-us

Dataset Card for MIAM ===================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: [N/A] * Repository: [N/A] * Paper: [N/A] * Leaderboard: [N/A] * Point of Contact: [N/A] ### Dataset Summary Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. Datasets are in English, French, German, Italian and Spanish. They cover a variety of domains including spontaneous speech, scripted scenarios, and joint task completion. All datasets contain dialogue act labels. ### Supported Tasks and Leaderboards ### Languages English, French, German, Italian, Spanish. Dataset Structure ----------------- ### Data Instances #### Dihana Corpus For the 'dihana' configuration one example from the dataset is: #### iLISTEN Corpus For the 'ilisten' configuration one example from the dataset is: #### LORIA Corpus For the 'loria' configuration one example from the dataset is: #### HCRC MapTask Corpus For the 'maptask' configuration one example from the dataset is: #### VERBMOBIL For the 'vm2' configuration one example from the dataset is: ### Data Fields For the 'dihana' configuration, the different fields are: * 'Speaker': identifier of the speaker as a string. * 'Utterance': Utterance as a string. * 'Dialogue\_Act': Dialog act label of the utterance. It can be one of 'Afirmacion' (0) [Feedback\_positive], 'Apertura' (1) [Opening], 'Cierre' (2) [Closing], 'Confirmacion' (3) [Acknowledge], 'Espera' (4) [Hold], 'Indefinida' (5) [Undefined], 'Negacion' (6) [Feedback\_negative], 'No\_entendido' (7) [Request\_clarify], 'Nueva\_consulta' (8) [New\_request], 'Pregunta' (9) [Request] or 'Respuesta' (10) [Reply]. * 'Dialogue\_ID': identifier of the dialogue as a string. * 'File\_ID': identifier of the source file as a string. For the 'ilisten' configuration, the different fields are: * 'Speaker': identifier of the speaker as a string. * 'Utterance': Utterance as a string. * 'Dialogue\_Act': Dialog act label of the utterance. It can be one of 'AGREE' (0), 'ANSWER' (1), 'CLOSING' (2), 'ENCOURAGE-SORRY' (3), 'GENERIC-ANSWER' (4), 'INFO-REQUEST' (5), 'KIND-ATTITUDE\_SMALL-TALK' (6), 'OFFER-GIVE-INFO' (7), 'OPENING' (8), 'PERSUASION-SUGGEST' (9), 'QUESTION' (10), 'REJECT' (11), 'SOLICITATION-REQ\_CLARIFICATION' (12), 'STATEMENT' (13) or 'TALK-ABOUT-SELF' (14). * 'Dialogue\_ID': identifier of the dialogue as a string. For the 'loria' configuration, the different fields are: * 'Speaker': identifier of the speaker as a string. * 'Utterance': Utterance as a string. * 'Dialogue\_Act': Dialog act label of the utterance. It can be one of 'ack' (0), 'ask' (1), 'find\_mold' (2), 'find\_plans' (3), 'first\_step' (4), 'greet' (5), 'help' (6), 'inform' (7), 'inform\_engine' (8), 'inform\_job' (9), 'inform\_material\_space' (10), 'informer\_conditioner' (11), 'informer\_decoration' (12), 'informer\_elcomps' (13), 'informer\_end\_manufacturing' (14), 'kindAtt' (15), 'manufacturing\_reqs' (16), 'next\_step' (17), 'no' (18), 'other' (19), 'quality\_control' (20), 'quit' (21), 'reqRep' (22), 'security\_policies' (23), 'staff\_enterprise' (24), 'staff\_job' (25), 'studies\_enterprise' (26), 'studies\_job' (27), 'todo\_failure' (28), 'todo\_irreparable' (29), 'yes' (30) * 'Dialogue\_ID': identifier of the dialogue as a string. * 'File\_ID': identifier of the source file as a string. For the 'maptask' configuration, the different fields are: * 'Speaker': identifier of the speaker as a string. * 'Utterance': Utterance as a string. * 'Dialogue\_Act': Dialog act label of the utterance. It can be one of 'acknowledge' (0), 'align' (1), 'check' (2), 'clarify' (3), 'explain' (4), 'instruct' (5), 'query\_w' (6), 'query\_yn' (7), 'ready' (8), 'reply\_n' (9), 'reply\_w' (10) or 'reply\_y' (11). * 'Dialogue\_ID': identifier of the dialogue as a string. * 'File\_ID': identifier of the source file as a string. For the 'vm2' configuration, the different fields are: * 'Utterance': Utterance as a string. * 'Dialogue\_Act': Dialogue act label of the utterance. It can be one of 'ACCEPT' (0), 'BACKCHANNEL' (1), 'BYE' (2), 'CLARIFY' (3), 'CLOSE' (4), 'COMMIT' (5), 'CONFIRM' (6), 'DEFER' (7), 'DELIBERATE' (8), 'DEVIATE\_SCENARIO' (9), 'EXCLUDE' (10), 'EXPLAINED\_REJECT' (11), 'FEEDBACK' (12), 'FEEDBACK\_NEGATIVE' (13), 'FEEDBACK\_POSITIVE' (14), 'GIVE\_REASON' (15), 'GREET' (16), 'INFORM' (17), 'INIT' (18), 'INTRODUCE' (19), 'NOT\_CLASSIFIABLE' (20), 'OFFER' (21), 'POLITENESS\_FORMULA' (22), 'REJECT' (23), 'REQUEST' (24), 'REQUEST\_CLARIFY' (25), 'REQUEST\_COMMENT' (26), 'REQUEST\_COMMIT' (27), 'REQUEST\_SUGGEST' (28), 'SUGGEST' (29), 'THANK' (30). * 'Speaker': Speaker as a string. * 'Dialogue\_ID': identifier of the dialogue as a string. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators Anonymous. ### Licensing Information This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. ### Contributions Thanks to @eusip and @PierreColombo for adding this dataset.

eac2a1185ca1aad94fab1bf78fded80fc53b2492

# Dataset Card for CVIT MKB ## 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:** [Link](http://preon.iiit.ac.in/~jerin/bhasha/) - **Repository:** - **Paper:** [ARXIV](https://arxiv.org/abs/2007.07691) - **Leaderboard:** - **Point of Contact:** [email]([email protected]) ### Dataset Summary Indian Prime Minister's speeches - Mann Ki Baat, on All India Radio, translated into many languages. ### Supported Tasks and Leaderboards [MORE INFORMATION NEEDED] ### Languages Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English ## Dataset Structure ### Data Instances [MORE INFORMATION NEEDED] ### Data Fields - `src_tag`: `string` text in source language - `tgt_tag`: `string` translation of source language in target language ### 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 #### 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 The datasets and pretrained models provided here are licensed under Creative Commons Attribution-ShareAlike 4.0 International License. ### Citation Information ``` @misc{siripragada2020multilingual, title={A Multilingual Parallel Corpora Collection Effort for Indian Languages}, author={Shashank Siripragada and Jerin Philip and Vinay P. Namboodiri and C V Jawahar}, year={2020}, eprint={2007.07691}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.

mkb

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language": ["bn", "en", "gu", "hi", "ml", "mr", "or", "pa", "ta", "te", "ur"], "license": ["cc-by-4.0"], "multilinguality": ["translation"], "size_categories": ["1K<n<10K", "n<1K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "CVIT MKB", "config_names": ["bn-en", "bn-gu", "bn-hi", "bn-ml", "bn-mr", "bn-or", "bn-ta", "bn-te", "bn-ur", "en-gu", "en-hi", "en-ml", "en-mr", "en-or", "en-ta", "en-te", "en-ur", "gu-hi", "gu-ml", "gu-mr", "gu-or", "gu-ta", "gu-te", "gu-ur", "hi-ml", "hi-mr", "hi-or", "hi-ta", "hi-te", "hi-ur", "ml-mr", "ml-or", "ml-ta", "ml-te", "ml-ur", "mr-or", "mr-ta", "mr-te", "mr-ur", "or-ta", "or-te", "or-ur", "ta-te", "ta-ur", "te-ur"], "dataset_info": [{"config_name": "or-ur", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["or", "ur"]}}}], "splits": [{"name": "train", 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2024-01-18T11:09:02+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #multilinguality-translation #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-Bengali #language-English #language-Gujarati #language-Hindi #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #language-Urdu #license-cc-by-4.0 #arxiv-2007.07691 #region-us

# Dataset Card for CVIT MKB ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Link - Repository: - Paper: ARXIV - Leaderboard: - Point of Contact: email ### Dataset Summary Indian Prime Minister's speeches - Mann Ki Baat, on All India Radio, translated into many languages. ### Supported Tasks and Leaderboards [MORE INFORMATION NEEDED] ### Languages Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English ## Dataset Structure ### Data Instances [MORE INFORMATION NEEDED] ### Data Fields - 'src_tag': 'string' text in source language - 'tgt_tag': 'string' translation of source language in target language ### 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 #### 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 The datasets and pretrained models provided here are licensed under Creative Commons Attribution-ShareAlike 4.0 International License. ### Contributions Thanks to @vasudevgupta7 for adding this dataset.

325131889721ae0ed885b76ecb8011369d75abad

# Dataset Card for MKQA: Multilingual Knowledge Questions & Answers ## 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/apple/ml-mkqa/) - [**Paper:**](https://arxiv.org/abs/2007.15207) ### Dataset Summary MKQA contains 10,000 queries sampled from the [Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions). For each query we collect new passage-independent answers. These queries and answers are then human translated into 25 Non-English languages. ### Supported Tasks and Leaderboards `question-answering` ### Languages | Language code | Language name | |---------------|---------------| | `ar` | `Arabic` | | `da` | `Danish` | | `de` | `German` | | `en` | `English` | | `es` | `Spanish` | | `fi` | `Finnish` | | `fr` | `French` | | `he` | `Hebrew` | | `hu` | `Hungarian` | | `it` | `Italian` | | `ja` | `Japanese` | | `ko` | `Korean` | | `km` | `Khmer` | | `ms` | `Malay` | | `nl` | `Dutch` | | `no` | `Norwegian` | | `pl` | `Polish` | | `pt` | `Portuguese` | | `ru` | `Russian` | | `sv` | `Swedish` | | `th` | `Thai` | | `tr` | `Turkish` | | `vi` | `Vietnamese` | | `zh_cn` | `Chinese (Simplified)` | | `zh_hk` | `Chinese (Hong kong)` | | `zh_tw` | `Chinese (Traditional)` | ## Dataset Structure ### Data Instances An example from the data set looks as follows: ``` { 'example_id': 563260143484355911, 'queries': { 'en': "who sings i hear you knocking but you can't come in", 'ru': "кто поет i hear you knocking but you can't come in", 'ja': '「 I hear you knocking」は誰が歌っていますか', 'zh_cn': "《i hear you knocking but you can't come in》是谁演唱的", ... }, 'query': "who sings i hear you knocking but you can't come in", 'answers': {'en': [{'type': 'entity', 'entity': 'Q545186', 'text': 'Dave Edmunds', 'aliases': []}], 'ru': [{'type': 'entity', 'entity': 'Q545186', 'text': 'Эдмундс, Дэйв', 'aliases': ['Эдмундс', 'Дэйв Эдмундс', 'Эдмундс Дэйв', 'Dave Edmunds']}], 'ja': [{'type': 'entity', 'entity': 'Q545186', 'text': 'デイヴ・エドモンズ', 'aliases': ['デーブ・エドモンズ', 'デイブ・エドモンズ']}], 'zh_cn': [{'type': 'entity', 'text': '戴维·埃德蒙兹 ', 'entity': 'Q545186'}], ... }, } ``` ### Data Fields Each example in the dataset contains the unique Natural Questions `example_id`, the original English `query`, and then `queries` and `answers` in 26 languages. Each answer is labelled with an answer type. The breakdown is: | Answer Type | Occurrence | |---------------|---------------| | `entity` | `4221` | | `long_answer` | `1815` | | `unanswerable` | `1427` | | `date` | `1174` | | `number` | `485` | | `number_with_unit` | `394` | | `short_phrase` | `346` | | `binary` | `138` | For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers. Detailed explanation of fields taken from [here](https://github.com/apple/ml-mkqa/#dataset) when `entity` field is not available it is set to an empty string ''. when `aliases` field is not available it is set to an empty list []. ### Data Splits - Train: 10000 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions) #### 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](https://github.com/apple/ml-mkqa#license) ### Citation Information ``` @misc{mkqa, title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering}, author = {Shayne Longpre and Yi Lu and Joachim Daiber}, year = {2020}, URL = {https://arxiv.org/pdf/2007.15207.pdf} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

mkqa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["ar", "da", "de", "en", "es", "fi", "fr", "he", "hu", "it", "ja", "km", "ko", "ms", "nl", "no", "pl", "pt", "ru", "sv", "th", "tr", "vi", "zh"], "license": ["cc-by-3.0"], "multilinguality": ["multilingual", "translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|natural_questions", "original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "mkqa", "pretty_name": "Multilingual Knowledge Questions and Answers", "dataset_info": {"features": [{"name": "example_id", "dtype": "string"}, {"name": "queries", "struct": [{"name": "ar", "dtype": "string"}, {"name": "da", "dtype": "string"}, {"name": "de", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "es", "dtype": "string"}, {"name": "fi", "dtype": "string"}, {"name": "fr", "dtype": "string"}, {"name": "he", "dtype": "string"}, {"name": "hu", "dtype": "string"}, {"name": "it", "dtype": "string"}, {"name": "ja", "dtype": "string"}, {"name": "ko", "dtype": "string"}, {"name": "km", "dtype": "string"}, {"name": "ms", "dtype": "string"}, {"name": "nl", "dtype": "string"}, {"name": "no", "dtype": "string"}, {"name": "pl", "dtype": "string"}, {"name": "pt", "dtype": "string"}, {"name": "ru", "dtype": "string"}, {"name": "sv", "dtype": "string"}, {"name": "th", "dtype": "string"}, {"name": "tr", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "zh_cn", "dtype": "string"}, {"name": "zh_hk", "dtype": "string"}, {"name": "zh_tw", "dtype": "string"}]}, {"name": "query", "dtype": "string"}, {"name": "answers", "struct": [{"name": "ar", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", 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"string"}, {"name": "aliases", "list": "string"}]}, {"name": "es", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "fi", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "fr", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "he", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "hu", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "it", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ja", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ko", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "km", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ms", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "nl", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "no", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "pl", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "pt", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "ru", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "sv", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "th", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "tr", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "vi", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "zh_cn", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "zh_hk", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}, {"name": "zh_tw", "list": [{"name": "type", "dtype": {"class_label": {"names": {"0": "entity", "1": "long_answer", "2": "unanswerable", "3": "date", "4": "number", "5": "number_with_unit", "6": "short_phrase", "7": "binary"}}}}, {"name": "entity", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aliases", "list": "string"}]}]}], "config_name": "mkqa", "splits": [{"name": "train", "num_bytes": 36005650, "num_examples": 10000}], "download_size": 11903948, "dataset_size": 36005650}}

2024-01-18T11:09:04+00:00

TAGS #task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-extended|natural_questions #source_datasets-original #language-Arabic #language-Danish #language-German #language-English #language-Spanish #language-Finnish #language-French #language-Hebrew #language-Hungarian #language-Italian #language-Japanese #language-Khmer #language-Korean #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Russian #language-Swedish #language-Thai #language-Turkish #language-Vietnamese #language-Chinese #license-cc-by-3.0 #arxiv-2007.15207 #region-us

Dataset Card for MKQA: Multilingual Knowledge Questions & Answers ================================================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Paper: ### Dataset Summary MKQA contains 10,000 queries sampled from the Google Natural Questions dataset. For each query we collect new passage-independent answers. These queries and answers are then human translated into 25 Non-English languages. ### Supported Tasks and Leaderboards 'question-answering' ### Languages Dataset Structure ----------------- ### Data Instances An example from the data set looks as follows: ### Data Fields Each example in the dataset contains the unique Natural Questions 'example\_id', the original English 'query', and then 'queries' and 'answers' in 26 languages. Each answer is labelled with an answer type. The breakdown is: For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers. Detailed explanation of fields taken from here when 'entity' field is not available it is set to an empty string ''. when 'aliases' field is not available it is set to an empty list []. ### Data Splits * Train: 10000 Dataset Creation ---------------- ### Curation Rationale ### Source Data Google Natural Questions dataset #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information CC BY-SA 3.0 ### Contributions Thanks to @cceyda for adding this dataset.

397ed406c1a7902140303e7faf60fff35b58d285

# Dataset Card for "mlqa" ## 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/facebookresearch/MLQA](https://github.com/facebookresearch/MLQA) - **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:** 4.15 GB - **Size of the generated dataset:** 910.01 MB - **Total amount of disk used:** 5.06 GB ### Dataset Summary MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. ## Dataset Structure ### Data Instances #### mlqa-translate-test.ar - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 5.48 MB - **Total amount of disk used:** 15.56 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.de - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 3.88 MB - **Total amount of disk used:** 13.96 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.es - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 3.92 MB - **Total amount of disk used:** 13.99 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.hi - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 4.61 MB - **Total amount of disk used:** 14.68 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.vi - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 6.00 MB - **Total amount of disk used:** 16.07 MB An example of 'test' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### mlqa-translate-test.ar - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.de - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.es - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.hi - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.vi - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. ### Data Splits | name |test| |----------------------|---:| |mlqa-translate-test.ar|5335| |mlqa-translate-test.de|4517| |mlqa-translate-test.es|5253| |mlqa-translate-test.hi|4918| |mlqa-translate-test.vi|5495| ## 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{lewis2019mlqa, title = {MLQA: Evaluating Cross-lingual Extractive Question Answering}, author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, journal = {arXiv preprint arXiv:1910.07475}, year = 2019, eid = {arXiv: 1910.07475} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@M-Salti](https://github.com/M-Salti), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

mlqa

2022-03-02T23:29:22+00:00

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2024-01-18T11:09:06+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-German #language-Spanish #language-Arabic #language-Chinese #language-Vietnamese #language-Hindi #license-cc-by-sa-3.0 #region-us

Dataset Card for "mlqa" ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 4.15 GB * Size of the generated dataset: 910.01 MB * Total amount of disk used: 5.06 GB ### Dataset Summary ``` MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average. ``` ### Supported Tasks and Leaderboards ### Languages MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. Dataset Structure ----------------- ### Data Instances #### URL * Size of downloaded dataset files: 10.08 MB * Size of the generated dataset: 5.48 MB * Total amount of disk used: 15.56 MB An example of 'test' looks as follows. #### URL * Size of downloaded dataset files: 10.08 MB * Size of the generated dataset: 3.88 MB * Total amount of disk used: 13.96 MB An example of 'test' looks as follows. #### URL * Size of downloaded dataset files: 10.08 MB * Size of the generated dataset: 3.92 MB * Total amount of disk used: 13.99 MB An example of 'test' looks as follows. #### URL * Size of downloaded dataset files: 10.08 MB * Size of the generated dataset: 4.61 MB * Total amount of disk used: 14.68 MB An example of 'test' looks as follows. #### URL * Size of downloaded dataset files: 10.08 MB * Size of the generated dataset: 6.00 MB * Total amount of disk used: 16.07 MB An example of 'test' looks as follows. ### Data Fields The data fields are the same among all splits. #### URL * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. * 'id': a 'string' feature. #### URL * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. * 'id': a 'string' feature. #### URL * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. * 'id': a 'string' feature. #### URL * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. * 'id': a 'string' feature. #### URL * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. * 'id': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.

0f8114e036015f81e877e8a1950ce2713d7afe8d

# Dataset Card for MLSUM ## 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/recitalAI/MLSUM - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.647/ - **Point of Contact:** [email]([email protected]) - **Size of downloaded dataset files:** 1.83 GB - **Size of the generated dataset:** 4.86 GB - **Total amount of disk used:** 6.69 GB ### Dataset Summary We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. ### 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 #### de - **Size of downloaded dataset files:** 346.58 MB - **Size of the generated dataset:** 940.93 MB - **Total amount of disk used:** 1.29 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### es - **Size of downloaded dataset files:** 513.31 MB - **Size of the generated dataset:** 1.34 GB - **Total amount of disk used:** 1.85 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### fr - **Size of downloaded dataset files:** 619.99 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.23 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### ru - **Size of downloaded dataset files:** 106.22 MB - **Size of the generated dataset:** 276.17 MB - **Total amount of disk used:** 382.39 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### tu - **Size of downloaded dataset files:** 247.50 MB - **Size of the generated dataset:** 694.99 MB - **Total amount of disk used:** 942.48 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` ### Data Fields The data fields are the same among all splits. #### de - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### es - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### fr - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### ru - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### tu - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. ### Data Splits |name|train |validation|test | |----|-----:|---------:|----:| |de |220887| 11394|10701| |es |266367| 10358|13920| |fr |392902| 16059|15828| |ru | 25556| 750| 757| |tu |249277| 11565|12775| ## 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 Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See https://github.com/recitalAI/MLSUM#mlsum ### Citation Information ``` @article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} } ``` ### Contributions Thanks to [@RachelKer](https://github.com/RachelKer), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

mlsum

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["de", "es", "fr", "ru", "tr"], "license": ["other"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["extended|cnn_dailymail", "original"], "task_categories": ["summarization", "translation", "text-classification"], "task_ids": ["news-articles-summarization", "multi-class-classification", "multi-label-classification", "topic-classification"], "paperswithcode_id": "mlsum", "pretty_name": "MLSUM", "config_names": ["de", "es", "fr", "ru", "tu"], "dataset_info": [{"config_name": "de", "features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 846959840, "num_examples": 220887}, {"name": "validation", "num_bytes": 47119541, "num_examples": 11394}, {"name": "test", "num_bytes": 46847612, "num_examples": 10701}], "download_size": 1005814154, "dataset_size": 940926993}, {"config_name": "es", "features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1214558302, "num_examples": 266367}, {"name": "validation", "num_bytes": 50643400, "num_examples": 10358}, {"name": "test", "num_bytes": 71263665, "num_examples": 13920}], "download_size": 1456211154, "dataset_size": 1336465367}, {"config_name": "fr", "features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1471965014, "num_examples": 392902}, {"name": "validation", "num_bytes": 70413212, "num_examples": 16059}, {"name": "test", "num_bytes": 69660288, "num_examples": 15828}], "download_size": 1849565564, "dataset_size": 1612038514}, {"config_name": "ru", "features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 257389497, "num_examples": 25556}, {"name": "validation", "num_bytes": 9128497, "num_examples": 750}, {"name": "test", "num_bytes": 9656398, "num_examples": 757}], "download_size": 766226107, "dataset_size": 276174392}, {"config_name": "tu", "features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 641622783, "num_examples": 249277}, {"name": "validation", "num_bytes": 25530661, "num_examples": 11565}, {"name": "test", "num_bytes": 27830212, "num_examples": 12775}], "download_size": 942308960, "dataset_size": 694983656}]}

2024-01-18T11:09:09+00:00

TAGS #task_categories-summarization #task_categories-translation #task_categories-text-classification #task_ids-news-articles-summarization #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-extended|cnn_dailymail #source_datasets-original #language-German #language-Spanish #language-French #language-Russian #language-Turkish #license-other #region-us

Dataset Card for MLSUM ====================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Repository: URL * Paper: URL * Point of Contact: email * Size of downloaded dataset files: 1.83 GB * Size of the generated dataset: 4.86 GB * Total amount of disk used: 6.69 GB ### Dataset Summary We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### de * Size of downloaded dataset files: 346.58 MB * Size of the generated dataset: 940.93 MB * Total amount of disk used: 1.29 GB An example of 'validation' looks as follows. #### es * Size of downloaded dataset files: 513.31 MB * Size of the generated dataset: 1.34 GB * Total amount of disk used: 1.85 GB An example of 'validation' looks as follows. #### fr * Size of downloaded dataset files: 619.99 MB * Size of the generated dataset: 1.61 GB * Total amount of disk used: 2.23 GB An example of 'validation' looks as follows. #### ru * Size of downloaded dataset files: 106.22 MB * Size of the generated dataset: 276.17 MB * Total amount of disk used: 382.39 MB An example of 'train' looks as follows. #### tu * Size of downloaded dataset files: 247.50 MB * Size of the generated dataset: 694.99 MB * Total amount of disk used: 942.48 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### de * 'text': a 'string' feature. * 'summary': a 'string' feature. * 'topic': a 'string' feature. * 'url': a 'string' feature. * 'title': a 'string' feature. * 'date': a 'string' feature. #### es * 'text': a 'string' feature. * 'summary': a 'string' feature. * 'topic': a 'string' feature. * 'url': a 'string' feature. * 'title': a 'string' feature. * 'date': a 'string' feature. #### fr * 'text': a 'string' feature. * 'summary': a 'string' feature. * 'topic': a 'string' feature. * 'url': a 'string' feature. * 'title': a 'string' feature. * 'date': a 'string' feature. #### ru * 'text': a 'string' feature. * 'summary': a 'string' feature. * 'topic': a 'string' feature. * 'url': a 'string' feature. * 'title': a 'string' feature. * 'date': a 'string' feature. #### tu * 'text': a 'string' feature. * 'summary': a 'string' feature. * 'topic': a 'string' feature. * 'url': a 'string' feature. * 'title': a 'string' feature. * 'date': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See URL ### Contributions Thanks to @RachelKer, @albertvillanova, @thomwolf for adding this dataset.

b06aab39e05f7bcd9635d18ed25d06eae523c574

# Dataset Card for MNIST ## 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://yann.lecun.com/exdb/mnist/ - **Repository:** - **Paper:** MNIST handwritten digit database by Yann LeCun, Corinna Cortes, and CJ Burges - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class. Half of the image were drawn by Census Bureau employees and the other half by high school students (this split is evenly distributed in the training and testing sets). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-mnist). ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its label: ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x276021F6DD8>, 'label': 5 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the 28x28 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`: an integer between 0 and 9 representing the digit. ### Data Splits The data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images. ## Dataset Creation ### Curation Rationale The MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was built by grouping the images form the high school students. The goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set. ### Source Data #### Initial Data Collection and Normalization The original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. #### Who are the source language producers? Half of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable. ### Annotations #### Annotation process The images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them. #### Who are the annotators? Same as the source data creators. ### 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 Chris Burges, Corinna Cortes and Yann LeCun ### Licensing Information MIT Licence ### Citation Information ``` @article{lecun2010mnist, title={MNIST handwritten digit database}, author={LeCun, Yann and Cortes, Corinna and Burges, CJ}, journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist}, volume={2}, year={2010} } ``` ### Contributions Thanks to [@sgugger](https://github.com/sgugger) for adding this dataset.

mnist

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-nist"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "mnist", "pretty_name": "MNIST", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"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"}}}}], "config_name": "mnist", "splits": [{"name": "train", "num_bytes": 17470848, "num_examples": 60000}, {"name": "test", "num_bytes": 2916440, "num_examples": 10000}], "download_size": 11594722, "dataset_size": 20387288}}

2024-01-18T11:09:11+00:00

TAGS #task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-nist #language-English #license-mit #region-us

# Dataset Card for MNIST ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: MNIST handwritten digit database by Yann LeCun, Corinna Cortes, and CJ Burges - Leaderboard: - Point of Contact: ### Dataset Summary The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class. Half of the image were drawn by Census Bureau employees and the other half by high school students (this split is evenly distributed in the training and testing sets). ### Supported Tasks and Leaderboards - 'image-classification': The goal of this task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The leaderboard is available here. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its label: ### Data Fields - 'image': A 'PIL.Image.Image' object containing the 28x28 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': an integer between 0 and 9 representing the digit. ### Data Splits The data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images. ## Dataset Creation ### Curation Rationale The MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was built by grouping the images form the high school students. The goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set. ### Source Data #### Initial Data Collection and Normalization The original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. #### Who are the source language producers? Half of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable. ### Annotations #### Annotation process The images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them. #### Who are the annotators? Same as the source data creators. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators Chris Burges, Corinna Cortes and Yann LeCun ### Licensing Information MIT Licence ### Contributions Thanks to @sgugger for adding this dataset.

b74c288b77c85973d884cc5250b9c9ba6975eb4b

# Dataset Card for Mocha ## 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:** [Mocha](https://allennlp.org/mocha) - **Repository:** [https://github.com/anthonywchen/MOCHA](https://github.com/anthonywchen/MOCHA) - **Paper:** [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://www.aclweb.org/anthology/2020.emnlp-main.528/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. MOCHA pairs reading comprehension instances, which consists of a passage, question, and reference, with candidates and human judgement scores. ### Data Fields - `constituent_dataset`: the original QA dataset which the data instance came from. - `id` - `context`: the passage content. - `question`: the question related to the passage content. - `reference`: the correct answer for the question. - `candidate`: the answer generated from the `reference` by `source` - `score`: the human judgement score for the `candidate`. Not included in test split, defaults to `-1` - `metadata`: Not included in minimal pairs split. - `scores`: list of scores from difference judges, averaged out to get final `score`. defaults to `[]` - `source`: the generative model to generate the `candidate` In minimal pairs, we'll have an additional candidate for robust evaluation. - `candidate2` - `score2` ### Data Splits Dataset Split | Number of Instances in Split --------------|-------------------------------------------- Train | 31,069 Validation | 4,009 Test | 6,321 Minimal Pairs | 200 ## 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](https://creativecommons.org/licenses/by-sa/4.0/legalcode) ### Citation Information ```bitex @inproceedings{Chen2020MOCHAAD, author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner}, title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics}, booktitle={EMNLP}, year={2020} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.

mocha

2022-03-02T23:29:22+00:00

{"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": [], "paperswithcode_id": "mocha", "pretty_name": "MOCHA", "tags": ["generative-reading-comprehension-metric"], "dataset_info": {"features": [{"name": "constituent_dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "candidate", "dtype": "string"}, {"name": "score", "dtype": "float32"}, {"name": "metadata", "struct": [{"name": "scores", "sequence": "int32"}, {"name": "source", "dtype": "string"}]}, {"name": "candidate2", "dtype": "string"}, {"name": "score2", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 33292592, "num_examples": 31069}, {"name": "validation", "num_bytes": 4236883, "num_examples": 4009}, {"name": "test", "num_bytes": 6767409, "num_examples": 6321}, {"name": "minimal_pairs", "num_bytes": 193560, "num_examples": 200}], "download_size": 14452311, "dataset_size": 44490444}}

2024-01-18T11:09:13+00:00

TAGS #task_categories-question-answering #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-sa-4.0 #generative-reading-comprehension-metric #region-us

Dataset Card for Mocha ====================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: Mocha * Repository: URL * Paper: MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics * Leaderboard: * Point of Contact: ### Dataset Summary Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics. ### Supported Tasks and Leaderboards ### Languages English Dataset Structure ----------------- ### Data Instances MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. MOCHA pairs reading comprehension instances, which consists of a passage, question, and reference, with candidates and human judgement scores. ### Data Fields * 'constituent\_dataset': the original QA dataset which the data instance came from. * 'id' * 'context': the passage content. * 'question': the question related to the passage content. * 'reference': the correct answer for the question. * 'candidate': the answer generated from the 'reference' by 'source' * 'score': the human judgement score for the 'candidate'. Not included in test split, defaults to '-1' * 'metadata': Not included in minimal pairs split. + 'scores': list of scores from difference judges, averaged out to get final 'score'. defaults to '[]' + 'source': the generative model to generate the 'candidate' In minimal pairs, we'll have an additional candidate for robust evaluation. * 'candidate2' * 'score2' ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information CC BY-SA 4.0 ### Contributions Thanks to @mattbui for adding this dataset.

d64d9b8cd876056a5c24552afe3caf7e6fd26c8e

# Dataset Card for MOROCO ## 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/butnaruandrei/MOROCO) - **Repository:** [Github](https://github.com/butnaruandrei/MOROCO) - **Paper:** [Arxiv](https://arxiv.org/abs/1901.06543) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [email]([email protected]) ### Dataset Summary Introducing MOROCO - The **Mo**ldavian and **Ro**manian Dialectal **Co**rpus. The MOROCO data set contains Moldavian and Romanian samples of text collected from the news domain. The samples belong to one of the following six topics: (0) culture, (1) finance, (2) politics, (3) science, (4) sports, (5) tech. The corpus features a total of 33,564 samples labelled with one of the fore mentioned six categories. We are also including a train/validation/test split with 21,719/5,921/5,924 samples in each subset. ### Supported Tasks and Leaderboards [LiRo Benchmark and Leaderboard](https://eemlcommunity.github.io/ro_benchmark_leaderboard/site/) ### Languages The text dataset is in Romanian (`ro`) ## Dataset Structure ### Data Instances Below we have an example of sample from MOROCO: ``` {'id': , '48482', 'category': 2, 'sample': '“$NE$ cum am spus, nu este un sfârşit de drum . Vom continua lupta cu toate instrumentele şi cu toate mijloacele legale, parlamentare şi civice pe care le avem la dispoziţie . Evident că vom contesta la $NE$ această lege, au anunţat şi colegii de la $NE$ o astfel de contestaţie . Practic trebuie utilizat orice instrument pe care îl identificăm pentru a bloca intrarea în vigoare a acestei legi . Bineînţeles, şi preşedintele are punctul său de vedere . ( . . . ) $NE$ legi sunt împănate de motive de neconstituţionalitate . Colegii mei de la departamentul juridic lucrează în prezent pentru a definitiva textul contestaţiei”, a declarat $NE$ $NE$ citat de news . ro . Senatul a adoptat, marţi, în calitate de for decizional, $NE$ privind statutul judecătorilor şi procurorilor, cu 80 de voturi ”pentru” şi niciun vot ”împotrivă”, în condiţiile în care niciun partid din opoziţie nu a fost prezent în sală .', } ``` where 48482 is the sample ID, followed by the category ground truth label, and then the text representing the actual content to be classified by topic. Note: The category label has integer values ranging from 0 to 5. ### Data Fields - `id`: string, the unique indentifier of a sample - `category_label`: integer in the range [0, 5]; the category assigned to a sample. - `sample`: a string, news report to be classified / used in classification. ### Data Splits The train/validation/test split contains 21,719/5,921/5,924 samples tagged with the category assigned to each sample in the dataset. ## Dataset Creation ### Curation Rationale The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics. For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the [paper](https://arxiv.org/abs/1901.06543). ### Source Data #### Data Collection and Normalization For the data collection, five of the most popular news websites in Romania and the Republic of Moldova were targetted. Given that the data set was obtained through a web scraping technique, all the HTML tags needed to be removed, as well as replace consecutive white spaces with a single space. As part of the pre-processing, we remove named entities, such as country names, cities, public figures, etc. The named entities have been replaced with $NE$. The necessity to remove them, comes also from the scope of this dataset: categorization by topic. Thus, the authors decided to remove named entities in order to prevent classifiers from taking the decision based on features that are not truly indicative of the topics. #### Who are the source language producers? The original text comes from news websites from Romania and the Republic of Moldova. ### Annotations #### Annotation process As mentioned above, MOROCO is composed of text samples from the top five most popular news websites in Romania and the Republic of Moldova, respectively. Since there are topic tags in the news websites targetd, the text samples can be automatically labeled with the corresponding category. #### Who are the annotators? N/A ### Personal and Sensitive Information The textual data collected for MOROCO consists in news reports freely available on the Internet and of public interest. To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected. ## 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. In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language. ### Discussion of Biases The data included in MOROCO spans a well defined time frame of a few years. Part of the topics that were of interest then in the news landscape, might not show up nowadays or a few years from now in news websites. ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators Published and managed by Radu Tudor Ionescu and Andrei Butnaru. ### Licensing Information CC BY-SA 4.0 License ### Citation Information ``` @inproceedings{ Butnaru-ACL-2019, author = {Andrei M. Butnaru and Radu Tudor Ionescu}, title = "{MOROCO: The Moldavian and Romanian Dialectal Corpus}", booktitle = {Proceedings of ACL}, year = {2019}, pages={688--698}, } ``` ### Contributions Thanks to [@MihaelaGaman](https://github.com/MihaelaGaman) for adding this dataset.

moroco

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ro"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "paperswithcode_id": "moroco", "pretty_name": "MOROCO: The Moldavian and Romanian Dialectal Corpus", "language_bcp47": ["ro-MD"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "culture", "1": "finance", "2": "politics", "3": "science", "4": "sports", "5": "tech"}}}}, {"name": "sample", "dtype": "string"}], "config_name": "moroco", "splits": [{"name": "train", "num_bytes": 39314292, "num_examples": 21719}, {"name": "test", "num_bytes": 10877813, "num_examples": 5924}, {"name": "validation", "num_bytes": 10721304, "num_examples": 5921}], "download_size": 60711985, "dataset_size": 60913409}}

2024-01-18T11:09:14+00:00

TAGS #task_categories-text-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Romanian #license-cc-by-4.0 #arxiv-1901.06543 #region-us

# Dataset Card for MOROCO ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: Arxiv - Leaderboard: - Point of Contact: email ### Dataset Summary Introducing MOROCO - The Moldavian and Romanian Dialectal Corpus. The MOROCO data set contains Moldavian and Romanian samples of text collected from the news domain. The samples belong to one of the following six topics: (0) culture, (1) finance, (2) politics, (3) science, (4) sports, (5) tech. The corpus features a total of 33,564 samples labelled with one of the fore mentioned six categories. We are also including a train/validation/test split with 21,719/5,921/5,924 samples in each subset. ### Supported Tasks and Leaderboards LiRo Benchmark and Leaderboard ### Languages The text dataset is in Romanian ('ro') ## Dataset Structure ### Data Instances Below we have an example of sample from MOROCO: where 48482 is the sample ID, followed by the category ground truth label, and then the text representing the actual content to be classified by topic. Note: The category label has integer values ranging from 0 to 5. ### Data Fields - 'id': string, the unique indentifier of a sample - 'category_label': integer in the range [0, 5]; the category assigned to a sample. - 'sample': a string, news report to be classified / used in classification. ### Data Splits The train/validation/test split contains 21,719/5,921/5,924 samples tagged with the category assigned to each sample in the dataset. ## Dataset Creation ### Curation Rationale The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics. For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the paper. ### Source Data #### Data Collection and Normalization For the data collection, five of the most popular news websites in Romania and the Republic of Moldova were targetted. Given that the data set was obtained through a web scraping technique, all the HTML tags needed to be removed, as well as replace consecutive white spaces with a single space. As part of the pre-processing, we remove named entities, such as country names, cities, public figures, etc. The named entities have been replaced with $NE$. The necessity to remove them, comes also from the scope of this dataset: categorization by topic. Thus, the authors decided to remove named entities in order to prevent classifiers from taking the decision based on features that are not truly indicative of the topics. #### Who are the source language producers? The original text comes from news websites from Romania and the Republic of Moldova. ### Annotations #### Annotation process As mentioned above, MOROCO is composed of text samples from the top five most popular news websites in Romania and the Republic of Moldova, respectively. Since there are topic tags in the news websites targetd, the text samples can be automatically labeled with the corresponding category. #### Who are the annotators? N/A ### Personal and Sensitive Information The textual data collected for MOROCO consists in news reports freely available on the Internet and of public interest. To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected. ## 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. In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language. ### Discussion of Biases The data included in MOROCO spans a well defined time frame of a few years. Part of the topics that were of interest then in the news landscape, might not show up nowadays or a few years from now in news websites. ### Other Known Limitations ## Additional Information ### Dataset Curators Published and managed by Radu Tudor Ionescu and Andrei Butnaru. ### Licensing Information CC BY-SA 4.0 License ### Contributions Thanks to @MihaelaGaman for adding this dataset.

99460967dc08f1c41e12004777aaa60b1bfaaa6c

# Dataset Card for "movie_rationales" ## 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/jayded/eraserbenchmark - **Paper:** [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://aclanthology.org/2020.acl-main.408/) - **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:** 3.90 MB - **Size of the generated dataset:** 8.73 MB - **Total amount of disk used:** 12.62 MB ### Dataset Summary The movie rationale dataset contains human annotated rationales for movie reviews. ### 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:** 3.90 MB - **Size of the generated dataset:** 8.73 MB - **Total amount of disk used:** 12.62 MB An example of 'validation' looks as follows. ``` { "evidences": ["Fun movie"], "label": 1, "review": "Fun movie\n" } ``` ### Data Fields The data fields are the same among all splits. #### default - `review`: a `string` feature. - `label`: a classification label, with possible values including `NEG` (0), `POS` (1). - `evidences`: a `list` of `string` features. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 1600| 200| 199| ## 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{deyoung-etal-2020-eraser, title = "{ERASER}: {A} Benchmark to Evaluate Rationalized {NLP} Models", author = "DeYoung, Jay and Jain, Sarthak and Rajani, Nazneen Fatema and Lehman, Eric and Xiong, Caiming and Socher, Richard and Wallace, Byron C.", 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://aclanthology.org/2020.acl-main.408", doi = "10.18653/v1/2020.acl-main.408", pages = "4443--4458", } @InProceedings{zaidan-eisner-piatko-2008:nips, author = {Omar F. Zaidan and Jason Eisner and Christine Piatko}, title = {Machine Learning with Annotator Rationales to Reduce Annotation Cost}, booktitle = {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning}, month = {December}, year = {2008} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

movie_rationales

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "MovieRationales", "dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "NEG", "1": "POS"}}}}, {"name": "evidences", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 1046377, "num_examples": 199}, {"name": "train", "num_bytes": 6853624, "num_examples": 1600}, {"name": "validation", "num_bytes": 830417, "num_examples": 200}], "download_size": 3899487, "dataset_size": 8730418}}

2024-01-18T11:09:18+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us

Dataset Card for "movie\_rationales" ==================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Repository: URL * Paper: ERASER: A Benchmark to Evaluate Rationalized NLP Models * Point of Contact: * Size of downloaded dataset files: 3.90 MB * Size of the generated dataset: 8.73 MB * Total amount of disk used: 12.62 MB ### Dataset Summary The movie rationale dataset contains human annotated rationales for movie reviews. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 3.90 MB * Size of the generated dataset: 8.73 MB * Total amount of disk used: 12.62 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'review': a 'string' feature. * 'label': a classification label, with possible values including 'NEG' (0), 'POS' (1). * 'evidences': a 'list' of 'string' features. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.

f3178d9888471dfb2b67c93de14f0ddf499a8d9f

# Dataset Card for MRQA 2019 ## 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:** [MRQA 2019 Shared Task](https://mrqa.github.io/2019/shared.html) - **Repository:** [MRQA 2019 Github repository](https://github.com/mrqa/MRQA-Shared-Task-2019) - **Paper:** [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension ](https://arxiv.org/abs/1910.09753) - **Leaderboard:** [Shared task](https://mrqa.github.io/2019/shared.html) - **Point of Contact:** [[email protected]]([email protected]) ### Dataset Summary The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge. The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task. ### Supported Tasks and Leaderboards From the official repository: *The format of the task is extractive question answering. Given a question and context passage, systems must find the word or phrase in the document that best answers the question. While this format is somewhat restrictive, it allows us to leverage many existing datasets, and its simplicity helps us focus on out-of-domain generalization, instead of other important but orthogonal challenges.* *We have adapted several existing datasets from their original formats and settings to conform to our unified extractive setting. Most notably:* - *We provide only a single, length-limited context.* - *There are no unanswerable or non-span answer questions.* - *All questions have at least one accepted answer that is found exactly in the context.* *A span is judged to be an exact match if it matches the answer string after performing normalization consistent with the SQuAD dataset. Specifically:* - *The text is uncased.* - *All punctuation is stripped.* - *All articles `{a, an, the}` are removed.* - *All consecutive whitespace markers are compressed to just a single normal space `' '`.* Answers are evaluated using exact match and token-level F1 metrics. One can refer to the [mrqa_official_eval.py](https://github.com/mrqa/MRQA-Shared-Task-2019/blob/master/mrqa_official_eval.py) for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An examples looks like this: ``` { 'qid': 'f43c83e38d1e424ea00f8ad3c77ec999', 'subset': 'SQuAD' 'context': 'CBS broadcast Super Bowl 50 in the U.S., and charged an average of $5 million for a 30-second commercial during the game. The Super Bowl 50 halftime show was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively. It was the third-most watched U.S. broadcast ever.', 'context_tokens': { 'offsets': [0, 4, 14, 20, 25, 28, 31, 35, 39, 41, 45, 53, 56, 64, 67, 68, 70, 78, 82, 84, 94, 105, 112, 116, 120, 122, 126, 132, 137, 140, 149, 154, 158, 168, 171, 175, 183, 188, 194, 203, 208, 216, 222, 233, 241, 245, 251, 255, 257, 261, 271, 275, 281, 286, 292, 296, 302, 307, 314, 323, 328, 330, 342, 344, 347, 351, 355, 360, 361, 366, 374, 379, 389, 393], 'tokens': ['CBS', 'broadcast', 'Super', 'Bowl', '50', 'in', 'the', 'U.S.', ',', 'and', 'charged', 'an', 'average', 'of', '$', '5', 'million', 'for', 'a', '30-second', 'commercial', 'during', 'the', 'game', '.', 'The', 'Super', 'Bowl', '50', 'halftime', 'show', 'was', 'headlined', 'by', 'the', 'British', 'rock', 'group', 'Coldplay', 'with', 'special', 'guest', 'performers', 'Beyoncé', 'and', 'Bruno', 'Mars', ',', 'who', 'headlined', 'the', 'Super', 'Bowl', 'XLVII', 'and', 'Super', 'Bowl', 'XLVIII', 'halftime', 'shows', ',', 'respectively', '.', 'It', 'was', 'the', 'third', '-', 'most', 'watched', 'U.S.', 'broadcast', 'ever', '.'] }, 'question': "Who was the main performer at this year's halftime show?", 'question_tokens': { 'offsets': [0, 4, 8, 12, 17, 27, 30, 35, 39, 42, 51, 55], 'tokens': ['Who', 'was', 'the', 'main', 'performer', 'at', 'this', 'year', "'s", 'halftime', 'show', '?'] }, 'detected_answers': { 'char_spans': [ { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] }, { 'end': [201], 'start': [194] } ], 'text': ['Coldplay', 'Coldplay', 'Coldplay'], 'token_spans': [ { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] }, { 'end': [38], 'start': [38] } ] }, 'answers': ['Coldplay', 'Coldplay', 'Coldplay'], } ``` ### Data Fields - `subset`: which of the dataset does this examples come from? - `context`: This is the raw text of the supporting passage. Three special token types have been inserted: `[TLE]` precedes document titles, `[DOC]` denotes document breaks, and `[PAR]` denotes paragraph breaks. The maximum length of the context is 800 tokens. - `context_tokens`: A tokenized version of the supporting passage, using spaCy. Each token is a tuple of the token string and token character offset. The maximum number of tokens is 800. - `tokens`: list of tokens. - `offets`: list of offsets. - `qas`: A list of questions for the given context. - `qid`: A unique identifier for the question. The `qid` is unique across all datasets. - `question`: The raw text of the question. - `question_tokens`: A tokenized version of the question. The tokenizer and token format is the same as for the context. - `tokens`: list of tokens. - `offets`: list of offsets. - `detected_answers`: A list of answer spans for the given question that index into the context. For some datasets these spans have been automatically detected using searching heuristics. The same answer may appear multiple times in the text --- each of these occurrences is recorded. For example, if `42` is the answer, the context `"The answer is 42. 42 is the answer."`, has two occurrences marked. - `text`: The raw text of the detected answer. - `char_spans`: Inclusive (start, end) character spans (indexing into the raw context). - `start`: start (single element) - `end`: end (single element) - `token_spans`: Inclusive (start, end) token spans (indexing into the tokenized context). - `start`: start (single element) - `end`: end (single element) ### Data Splits **Training data** | Dataset | Number of Examples | | :-----: | :------: | | [SQuAD](https://arxiv.org/abs/1606.05250) | 86,588 | | [NewsQA](https://arxiv.org/abs/1611.09830) | 74,160 | | [TriviaQA](https://arxiv.org/abs/1705.03551)| 61,688 | | [SearchQA](https://arxiv.org/abs/1704.05179)| 117,384 | | [HotpotQA](https://arxiv.org/abs/1809.09600)| 72,928 | | [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 104,071 | **Development data** This in-domain data may be used for helping develop models. | Dataset | Examples | | :-----: | :------: | | [SQuAD](https://arxiv.org/abs/1606.05250) | 10,507 | | [NewsQA](https://arxiv.org/abs/1611.09830) | 4,212 | | [TriviaQA](https://arxiv.org/abs/1705.03551)| 7,785| | [SearchQA](https://arxiv.org/abs/1704.05179)| 16,980 | | [HotpotQA](https://arxiv.org/abs/1809.09600)| 5,904 | | [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 12,836 | **Test data** The final testing data only contain out-of-domain data. | Dataset | Examples | | :-----: | :------: | | [BioASQ](http://bioasq.org/) | 1,504 | | [DROP](https://arxiv.org/abs/1903.00161) | 1,503 | | [DuoRC](https://arxiv.org/abs/1804.07927)| 1,501 | | [RACE](https://arxiv.org/abs/1704.04683) | 674 | | [RelationExtraction](https://arxiv.org/abs/1706.04115) | 2,948| | [TextbookQA](http://ai2-website.s3.amazonaws.com/publications/CVPR17_TQA.pdf)| 1,503 | From the official repository: ***Note:** As previously mentioned, the out-of-domain dataset have been modified from their original settings to fit the unified MRQA Shared Task paradigm. At a high level, the following two major modifications have been made:* *1. All QA-context pairs are extractive. That is, the answer is selected from the context and not via, e.g., multiple-choice.* *2. All contexts are capped at a maximum of `800` tokens. As a result, for longer contexts like Wikipedia articles, we only consider examples where the answer appears in the first `800` tokens.* *As a result, some splits are harder than the original datasets (e.g., removal of multiple-choice in RACE), while some are easier (e.g., restricted context length in NaturalQuestions --- we use the short answer selection). Thus one should expect different performance ranges if comparing to previous work on these datasets.* ## Dataset Creation ### Curation Rationale From the official repository: *Both train and test datasets have the same format described above, but may differ in some of the following ways:* - *Passage distribution: Test examples may involve passages from different sources (e.g., science, news, novels, medical abstracts, etc) with pronounced syntactic and lexical differences.* - *Question distribution: Test examples may emphasize different styles of questions (e.g., entity-centric, relational, other tasks reformulated as QA, etc) which may come from different sources (e.g., crowdworkers, domain experts, exam writers, etc.)* - *Joint distribution: Test examples may vary according to the relationship of the question to the passage (e.g., collected independent vs. dependent of evidence, multi-hop, etc)* ### 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 Unknown ### Citation Information ``` @inproceedings{fisch2019mrqa, title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension}, author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen}, booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP}, year={2019}, } ``` ### Contributions Thanks to [@jimmycode](https://github.com/jimmycode), [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

mrqa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|drop", "extended|hotpot_qa", "extended|natural_questions", "extended|race", "extended|search_qa", "extended|squad", "extended|trivia_qa"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "mrqa-2019", "pretty_name": "MRQA 2019", "dataset_info": {"config_name": "plain_text", "features": [{"name": "subset", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "context_tokens", "sequence": [{"name": "tokens", "dtype": "string"}, {"name": "offsets", "dtype": "int32"}]}, {"name": "qid", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_tokens", "sequence": [{"name": "tokens", "dtype": "string"}, {"name": "offsets", "dtype": "int32"}]}, {"name": "detected_answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "char_spans", "sequence": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "token_spans", "sequence": [{"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}]}, {"name": "answers", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 4090677713, "num_examples": 516819}, {"name": "validation", "num_bytes": 484106546, "num_examples": 58221}, {"name": "test", "num_bytes": 57712097, "num_examples": 9633}], "download_size": 1679161250, "dataset_size": 4632496356}, "configs": [{"config_name": "plain_text", "data_files": [{"split": "train", "path": "plain_text/train-*"}, {"split": "validation", "path": "plain_text/validation-*"}, {"split": "test", "path": "plain_text/test-*"}], "default": true}]}

2024-01-24T10:52:34+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|drop #source_datasets-extended|hotpot_qa #source_datasets-extended|natural_questions #source_datasets-extended|race #source_datasets-extended|search_qa #source_datasets-extended|squad #source_datasets-extended|trivia_qa #language-English #license-unknown #arxiv-1910.09753 #arxiv-1606.05250 #arxiv-1611.09830 #arxiv-1705.03551 #arxiv-1704.05179 #arxiv-1809.09600 #arxiv-1903.00161 #arxiv-1804.07927 #arxiv-1704.04683 #arxiv-1706.04115 #region-us

Dataset Card for MRQA 2019 ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: MRQA 2019 Shared Task * Repository: MRQA 2019 Github repository * Paper: MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension * Leaderboard: Shared task * Point of Contact: mrforqa@URL ### Dataset Summary The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge. The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task. ### Supported Tasks and Leaderboards From the official repository: *The format of the task is extractive question answering. Given a question and context passage, systems must find the word or phrase in the document that best answers the question. While this format is somewhat restrictive, it allows us to leverage many existing datasets, and its simplicity helps us focus on out-of-domain generalization, instead of other important but orthogonal challenges.* *We have adapted several existing datasets from their original formats and settings to conform to our unified extractive setting. Most notably:* * *We provide only a single, length-limited context.* * *There are no unanswerable or non-span answer questions.* * *All questions have at least one accepted answer that is found exactly in the context.* *A span is judged to be an exact match if it matches the answer string after performing normalization consistent with the SQuAD dataset. Specifically:* * *The text is uncased.* * *All punctuation is stripped.* * *All articles '{a, an, the}' are removed.* * *All consecutive whitespace markers are compressed to just a single normal space '' ''.* Answers are evaluated using exact match and token-level F1 metrics. One can refer to the mrqa\_official\_eval.py for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. Dataset Structure ----------------- ### Data Instances An examples looks like this: ### Data Fields * 'subset': which of the dataset does this examples come from? * 'context': This is the raw text of the supporting passage. Three special token types have been inserted: '[TLE]' precedes document titles, '[DOC]' denotes document breaks, and '[PAR]' denotes paragraph breaks. The maximum length of the context is 800 tokens. * 'context\_tokens': A tokenized version of the supporting passage, using spaCy. Each token is a tuple of the token string and token character offset. The maximum number of tokens is 800. + 'tokens': list of tokens. + 'offets': list of offsets. * 'qas': A list of questions for the given context. * 'qid': A unique identifier for the question. The 'qid' is unique across all datasets. * 'question': The raw text of the question. * 'question\_tokens': A tokenized version of the question. The tokenizer and token format is the same as for the context. + 'tokens': list of tokens. + 'offets': list of offsets. * 'detected\_answers': A list of answer spans for the given question that index into the context. For some datasets these spans have been automatically detected using searching heuristics. The same answer may appear multiple times in the text --- each of these occurrences is recorded. For example, if '42' is the answer, the context '"The answer is 42. 42 is the answer."', has two occurrences marked. + 'text': The raw text of the detected answer. + 'char\_spans': Inclusive (start, end) character spans (indexing into the raw context). - 'start': start (single element) - 'end': end (single element) + 'token\_spans': Inclusive (start, end) token spans (indexing into the tokenized context). - 'start': start (single element) - 'end': end (single element) ### Data Splits Training data Development data This in-domain data may be used for helping develop models. Test data The final testing data only contain out-of-domain data. From the official repository: *Note: As previously mentioned, the out-of-domain dataset have been modified from their original settings to fit the unified MRQA Shared Task paradigm. At a high level, the following two major modifications have been made:* *1. All QA-context pairs are extractive. That is, the answer is selected from the context and not via, e.g., multiple-choice.* *2. All contexts are capped at a maximum of '800' tokens. As a result, for longer contexts like Wikipedia articles, we only consider examples where the answer appears in the first '800' tokens.* *As a result, some splits are harder than the original datasets (e.g., removal of multiple-choice in RACE), while some are easier (e.g., restricted context length in NaturalQuestions --- we use the short answer selection). Thus one should expect different performance ranges if comparing to previous work on these datasets.* Dataset Creation ---------------- ### Curation Rationale From the official repository: *Both train and test datasets have the same format described above, but may differ in some of the following ways:* * *Passage distribution: Test examples may involve passages from different sources (e.g., science, news, novels, medical abstracts, etc) with pronounced syntactic and lexical differences.* * *Question distribution: Test examples may emphasize different styles of questions (e.g., entity-centric, relational, other tasks reformulated as QA, etc) which may come from different sources (e.g., crowdworkers, domain experts, exam writers, etc.)* * *Joint distribution: Test examples may vary according to the relationship of the question to the passage (e.g., collected independent vs. dependent of evidence, multi-hop, etc)* ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Unknown ### Contributions Thanks to @jimmycode, @VictorSanh for adding this dataset.

a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a

# Dataset Card for "ms_marco" ## 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://microsoft.github.io/msmarco/](https://microsoft.github.io/msmarco/) - **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:** 1.55 GB - **Size of the generated dataset:** 4.72 GB - **Total amount of disk used:** 6.28 GB ### Dataset Summary Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search. The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational search. There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below. The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker. version v1.1 ### 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 #### v1.1 - **Size of downloaded dataset files:** 168.69 MB - **Size of the generated dataset:** 434.61 MB - **Total amount of disk used:** 603.31 MB An example of 'train' looks as follows. ``` ``` #### v2.1 - **Size of downloaded dataset files:** 1.38 GB - **Size of the generated dataset:** 4.29 GB - **Total amount of disk used:** 5.67 GB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### v1.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. #### v2.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. ### Data Splits |name|train |validation| test | |----|-----:|---------:|-----:| |v1.1| 82326| 10047| 9650| |v2.1|808731| 101093|101092| ## 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/NguyenRSGTMD16, author = {Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng}, title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset}, journal = {CoRR}, volume = {abs/1611.09268}, year = {2016}, url = {http://arxiv.org/abs/1611.09268}, archivePrefix = {arXiv}, eprint = {1611.09268}, timestamp = {Mon, 13 Aug 2018 16:49:03 +0200}, biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

ms_marco

2022-03-02T23:29:22+00:00

{"language": ["en"], "paperswithcode_id": "ms-marco", "pretty_name": "Microsoft Machine Reading Comprehension Dataset", "dataset_info": [{"config_name": "v1.1", "features": [{"name": "answers", "sequence": "string"}, {"name": "passages", "sequence": [{"name": "is_selected", "dtype": "int32"}, {"name": "passage_text", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "query", "dtype": "string"}, {"name": "query_id", "dtype": "int32"}, {"name": "query_type", "dtype": "string"}, {"name": "wellFormedAnswers", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 42665198, "num_examples": 10047}, {"name": "train", "num_bytes": 350516260, "num_examples": 82326}, {"name": "test", "num_bytes": 40977580, "num_examples": 9650}], "download_size": 217328153, "dataset_size": 434159038}, {"config_name": "v2.1", "features": [{"name": "answers", "sequence": "string"}, {"name": "passages", "sequence": [{"name": "is_selected", "dtype": "int32"}, {"name": "passage_text", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "query", "dtype": "string"}, {"name": "query_id", "dtype": "int32"}, {"name": "query_type", "dtype": "string"}, {"name": "wellFormedAnswers", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 413765365, "num_examples": 101093}, {"name": "train", "num_bytes": 3462807709, "num_examples": 808731}, {"name": "test", "num_bytes": 405691932, "num_examples": 101092}], "download_size": 2105722550, "dataset_size": 4282265006}], "configs": [{"config_name": "v1.1", "data_files": [{"split": "validation", "path": "v1.1/validation-*"}, {"split": "train", "path": "v1.1/train-*"}, {"split": "test", "path": "v1.1/test-*"}]}, {"config_name": "v2.1", "data_files": [{"split": "validation", "path": "v2.1/validation-*"}, {"split": "train", "path": "v2.1/train-*"}, {"split": "test", "path": "v2.1/test-*"}]}]}

2024-01-04T16:01:29+00:00

TAGS #language-English #arxiv-1611.09268 #region-us

Dataset Card for "ms\_marco" ============================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.55 GB * Size of the generated dataset: 4.72 GB * Total amount of disk used: 6.28 GB ### Dataset Summary Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search. The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational search. There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below. The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker. version v1.1 ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### v1.1 * Size of downloaded dataset files: 168.69 MB * Size of the generated dataset: 434.61 MB * Total amount of disk used: 603.31 MB An example of 'train' looks as follows. #### v2.1 * Size of downloaded dataset files: 1.38 GB * Size of the generated dataset: 4.29 GB * Total amount of disk used: 5.67 GB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### v1.1 * 'answers': a 'list' of 'string' features. * 'passages': a dictionary feature containing: + 'is\_selected': a 'int32' feature. + 'passage\_text': a 'string' feature. + 'url': a 'string' feature. * 'query': a 'string' feature. * 'query\_id': a 'int32' feature. * 'query\_type': a 'string' feature. * 'wellFormedAnswers': a 'list' of 'string' features. #### v2.1 * 'answers': a 'list' of 'string' features. * 'passages': a dictionary feature containing: + 'is\_selected': a 'int32' feature. + 'passage\_text': a 'string' feature. + 'url': a 'string' feature. * 'query': a 'string' feature. * 'query\_id': a 'int32' feature. * 'query\_type': a 'string' feature. * 'wellFormedAnswers': a 'list' of 'string' features. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @mariamabarham, @thomwolf, @lewtun for adding this dataset.

29fa6fbbf5cca442fe788d69796458badc44a34f

# Dataset Card for [ms_terms] ## 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:** [Microsoft Terminology Collection](https://www.microsoft.com/en-us/language/terminology) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products. It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary for language development in the nearly 100 languages available. Terminology is provided in .tbx format, an industry standard for terminology exchange. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Nearly 100 Languages. ## 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 [@leoxzhao](https://github.com/leoxzhao), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

ms_terms

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["af", "am", "ar", "as", "az", "be", "bg", "bn", "bs", "ca", "chr", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fil", "fr", "ga", "gd", "gl", "gu", "guc", "ha", "he", "hi", "hr", "hu", "hy", "id", "ig", "is", "it", "iu", "ja", "ka", "kk", "km", "kn", "knn", "ko", "ku", "ky", "lb", "lo", "lt", "lv", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "nb", "ne", "nl", "nn", "ory", "pa", "pl", "prs", "pst", "pt", "qu", "quc", "ro", "ru", "rw", "sd", "si", "sk", "sl", "sq", "sr", "st", "sv", "swh", "ta", "te", "tg", "th", "ti", "tk", "tn", "tr", "tt", "ug", "uk", "ur", "uz", "vi", "wo", "xh", "yo", "zh", "zu"], "license": ["ms-pl"], "multilinguality": ["multilingual", "translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "MsTerms", "language_bcp47": ["bn-IN", "bs-Latn", "es-MX", "fr-CA", "ms-BN", "pt-BR", "sr-BH", "sr-Latn", "zh-Hant-HK", "zh-Hant-TW"], "dataset_info": {"features": [{"name": "entry_id", "dtype": "string"}, {"name": "term_source", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "definition", "dtype": "string"}, {"name": "term_target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6995497, "num_examples": 33738}], "download_size": 0, "dataset_size": 6995497}}

2024-01-18T11:09:25+00:00

TAGS #task_categories-translation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-Afrikaans #language-Amharic #language-Arabic #language-Assamese #language-Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Bosnian #language-Catalan #language-Cherokee #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-Filipino #language-French #language-Irish #language-Scottish Gaelic #language-Galician #language-Gujarati #language-Wayuu #language-Hausa #language-Hebrew #language-Hindi #language-Croatian #language-Hungarian #language-Armenian #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-Inuktitut #language-Japanese #language-Georgian #language-Kazakh #language-Khmer #language-Kannada #language-Konkani (individual language) #language-Korean #language-Kurdish #language-Kirghiz #language-Luxembourgish #language-Lao #language-Lithuanian #language-Latvian #language-Maori #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Norwegian Bokmål #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Odia #language-Panjabi #language-Polish #language-Dari #language-Central Pashto #language-Portuguese #language-Quechua #language-K'iche' #language-Romanian #language-Russian #language-Kinyarwanda #language-Sindhi #language-Sinhala #language-Slovak #language-Slovenian #language-Albanian #language-Serbian #language-Southern Sotho #language-Swedish #language-Swahili (individual language) #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tswana #language-Turkish #language-Tatar #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Wolof #language-Xhosa #language-Yoruba #language-Chinese #language-Zulu #license-ms-pl #region-us

# Dataset Card for [ms_terms] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Microsoft Terminology Collection - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products. It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary for language development in the nearly 100 languages available. Terminology is provided in .tbx format, an industry standard for terminology exchange. ### Supported Tasks and Leaderboards ### Languages Nearly 100 Languages. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @leoxzhao, @lhoestq for adding this dataset.

6748409009d34a5eaafac3a82e5d90b9b670b6e0

# 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:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791) - **Repository:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset) - **Paper:** [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/) - **Point of Contact:** [Kristina Toutanova](mailto:[email protected]) ### Dataset Summary The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure NCI-PID-PubMed Genomics Knowledge Base Completion Dataset This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts. The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text". FILE FORMAT DETAILS The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in. The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in. The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). The format is: | GENE1 | relation | GENE2 | Example: ABL1 Positive_regulation CDK2 The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). ### Data Instances [More Information Needed] ### Data Fields The format is: | GENE1 | relation | GENE2 | ### Data Splits [More Information Needed] ## Dataset Creation [More Information Needed] ### 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 [More Information Needed] ### 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 The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research. ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{toutanova-etal-2016-compositional, title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text", author = "Toutanova, Kristina and Lin, Victoria and Yih, Wen-tau and Poon, Hoifung and Quirk, Chris", booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = aug, year = "2016", address = "Berlin, Germany", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P16-1136", doi = "10.18653/v1/P16-1136", pages = "1434--1444", } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.

msr_genomics_kbcomp

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "pretty_name": "MsrGenomicsKbcomp", "tags": ["genomics-knowledge-base-bompletion"], "dataset_info": {"features": [{"name": "GENE1", "dtype": "string"}, {"name": "relation", "dtype": {"class_label": {"names": {"0": "Positive_regulation", "1": "Negative_regulation", "2": "Family"}}}}, {"name": "GENE2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 256789, "num_examples": 12160}, {"name": "test", "num_bytes": 58116, "num_examples": 2784}, {"name": "validation", "num_bytes": 27457, "num_examples": 1315}], "download_size": 0, "dataset_size": 342362}}

2024-01-18T11:09:26+00:00

TAGS #task_categories-other #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #genomics-knowledge-base-bompletion #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: NCI-PID-PubMed Genomics Knowledge Base Completion Dataset - Repository: NCI-PID-PubMed Genomics Knowledge Base Completion Dataset - Paper: Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text - Point of Contact: Kristina Toutanova ### Dataset Summary The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README. ### Supported Tasks and Leaderboards ### Languages English ## Dataset Structure NCI-PID-PubMed Genomics Knowledge Base Completion Dataset This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts. The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text". FILE FORMAT DETAILS The files URL, URL, and URL contain the training, development, and test set knowledge base (database of regulation relationships) triples used in. The file URL contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in. The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). The format is: | GENE1 | relation | GENE2 | Example: ABL1 Positive_regulation CDK2 The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). ### Data Instances ### Data Fields The format is: | GENE1 | relation | GENE2 | ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research. ### Licensing Information ### Contributions Thanks to @manandey for adding this dataset.

7a94db6fbf82040d3b9f6f4e00782c1b33c892a6

# Dataset Card for Microsoft Research Sequential Question Answering ## 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:** [Microsoft Research Sequential Question Answering (SQA) Dataset](https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2) - **Repository:** - **Paper:** [https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf) - **Leaderboard:** - **Point of Contact:** - Scott Wen-tau Yih [email protected] - Mohit Iyyer [email protected] - Ming-Wei Chang [email protected] ### Dataset Summary Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ)*, which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables. - Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015. [http://www-nlp.stanford.edu/software/sempre/wikitable/](http://www-nlp.stanford.edu/software/sempre/wikitable/) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English (`en`). ## Dataset Structure ### Data Instances ``` {'id': 'nt-639', 'annotator': 0, 'position': 0, 'question': 'where are the players from?', 'table_file': 'table_csv/203_149.csv', 'table_header': ['Pick', 'Player', 'Team', 'Position', 'School'], 'table_data': [['1', 'Ben McDonald', 'Baltimore Orioles', 'RHP', 'Louisiana State University'], ['2', 'Tyler Houston', 'Atlanta Braves', 'C', '"Valley HS (Las Vegas', ' NV)"'], ['3', 'Roger Salkeld', 'Seattle Mariners', 'RHP', 'Saugus (CA) HS'], ['4', 'Jeff Jackson', 'Philadelphia Phillies', 'OF', '"Simeon HS (Chicago', ' IL)"'], ['5', 'Donald Harris', 'Texas Rangers', 'OF', 'Texas Tech University'], ['6', 'Paul Coleman', 'Saint Louis Cardinals', 'OF', 'Frankston (TX) HS'], ['7', 'Frank Thomas', 'Chicago White Sox', '1B', 'Auburn University'], ['8', 'Earl Cunningham', 'Chicago Cubs', 'OF', 'Lancaster (SC) HS'], ['9', 'Kyle Abbott', 'California Angels', 'LHP', 'Long Beach State University'], ['10', 'Charles Johnson', 'Montreal Expos', 'C', '"Westwood HS (Fort Pierce', ' FL)"'], ['11', 'Calvin Murray', 'Cleveland Indians', '3B', '"W.T. White High School (Dallas', ' TX)"'], ['12', 'Jeff Juden', 'Houston Astros', 'RHP', 'Salem (MA) HS'], ['13', 'Brent Mayne', 'Kansas City Royals', 'C', 'Cal State Fullerton'], ['14', 'Steve Hosey', 'San Francisco Giants', 'OF', 'Fresno State University'], ['15', 'Kiki Jones', 'Los Angeles Dodgers', 'RHP', '"Hillsborough HS (Tampa', ' FL)"'], ['16', 'Greg Blosser', 'Boston Red Sox', 'OF', 'Sarasota (FL) HS'], ['17', 'Cal Eldred', 'Milwaukee Brewers', 'RHP', 'University of Iowa'], ['18', 'Willie Greene', 'Pittsburgh Pirates', 'SS', '"Jones County HS (Gray', ' GA)"'], ['19', 'Eddie Zosky', 'Toronto Blue Jays', 'SS', 'Fresno State University'], ['20', 'Scott Bryant', 'Cincinnati Reds', 'OF', 'University of Texas'], ['21', 'Greg Gohr', 'Detroit Tigers', 'RHP', 'Santa Clara University'], ['22', 'Tom Goodwin', 'Los Angeles Dodgers', 'OF', 'Fresno State University'], ['23', 'Mo Vaughn', 'Boston Red Sox', '1B', 'Seton Hall University'], ['24', 'Alan Zinter', 'New York Mets', 'C', 'University of Arizona'], ['25', 'Chuck Knoblauch', 'Minnesota Twins', '2B', 'Texas A&M University'], ['26', 'Scott Burrell', 'Seattle Mariners', 'RHP', 'Hamden (CT) HS']], 'answer_coordinates': {'row_index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25], 'column_index': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]}, 'answer_text': ['Louisiana State University', 'Valley HS (Las Vegas, NV)', 'Saugus (CA) HS', 'Simeon HS (Chicago, IL)', 'Texas Tech University', 'Frankston (TX) HS', 'Auburn University', 'Lancaster (SC) HS', 'Long Beach State University', 'Westwood HS (Fort Pierce, FL)', 'W.T. White High School (Dallas, TX)', 'Salem (MA) HS', 'Cal State Fullerton', 'Fresno State University', 'Hillsborough HS (Tampa, FL)', 'Sarasota (FL) HS', 'University of Iowa', 'Jones County HS (Gray, GA)', 'Fresno State University', 'University of Texas', 'Santa Clara University', 'Fresno State University', 'Seton Hall University', 'University of Arizona', 'Texas A&M University', 'Hamden (CT) HS']} ``` ### Data Fields - `id` (`str`): question sequence id (the id is consistent with those in WTQ) - `annotator` (`int`): `0`, `1`, `2` (the 3 annotators who annotated the question intent) - `position` (`int`): the position of the question in the sequence - `question` (`str`): the question given by the annotator - `table_file` (`str`): the associated table - `table_header` (`List[str]`): a list of headers in the table - `table_data` (`List[List[str]]`): 2d array of data in the table - `answer_coordinates` (`List[Dict]`): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header) - `row_index` - `column_index` - `answer_text` (`List[str]`): the content of the answer cells Note that some text fields may contain Tab or LF characters and thus start with quotes. It is recommended to use a CSV parser like the Python CSV package to process the data. ### Data Splits | | train | test | |-------------|------:|-----:| | N. examples | 14541 | 3012 | ## 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 [Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view). ### Citation Information ``` @inproceedings{iyyer-etal-2017-search, title = "Search-based Neural Structured Learning for Sequential Question Answering", author = "Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei", 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://aclanthology.org/P17-1167", doi = "10.18653/v1/P17-1167", pages = "1821--1831", } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.

msr_sqa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["ms-pl"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "Microsoft Research Sequential Question Answering", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "annotator", "dtype": "int32"}, {"name": "position", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "question_and_history", "sequence": "string"}, {"name": "table_file", "dtype": "string"}, {"name": "table_header", "sequence": "string"}, {"name": "table_data", "sequence": {"sequence": "string"}}, {"name": "answer_coordinates", "sequence": [{"name": "row_index", "dtype": "int32"}, {"name": "column_index", "dtype": "int32"}]}, {"name": "answer_text", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 19732499, "num_examples": 12276}, {"name": "validation", "num_bytes": 3738331, "num_examples": 2265}, {"name": "test", "num_bytes": 5105873, "num_examples": 3012}], "download_size": 4796932, "dataset_size": 28576703}}

2024-01-18T11:09:28+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-ms-pl #region-us

Dataset Card for Microsoft Research Sequential Question Answering ================================================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: Microsoft Research Sequential Question Answering (SQA) Dataset * Repository: * Paper: URL * Leaderboard: * Point of Contact: + Scott Wen-tau Yih scottyih@URL + Mohit Iyyer m.iyyer@URL + Ming-Wei Chang minchang@URL ### Dataset Summary Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ)\*, which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables. * Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015. URL ### Supported Tasks and Leaderboards ### Languages English ('en'). Dataset Structure ----------------- ### Data Instances ### Data Fields * 'id' ('str'): question sequence id (the id is consistent with those in WTQ) * 'annotator' ('int'): '0', '1', '2' (the 3 annotators who annotated the question intent) * 'position' ('int'): the position of the question in the sequence * 'question' ('str'): the question given by the annotator * 'table\_file' ('str'): the associated table * 'table\_header' ('List[str]'): a list of headers in the table * 'table\_data' ('List[List[str]]'): 2d array of data in the table * 'answer\_coordinates' ('List[Dict]'): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header) + 'row\_index' + 'column\_index' * 'answer\_text' ('List[str]'): the content of the answer cells Note that some text fields may contain Tab or LF characters and thus start with quotes. It is recommended to use a CSV parser like the Python CSV package to process the data. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Microsoft Research Data License Agreement. ### Contributions Thanks to @mattbui for adding this dataset.

82726ef3f96ae4628b2aba34a10c909ce3dd96c4

# 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://msropendata.com/datasets/f8ce2ec9-7fbd-48f7-a8bb-2d2279373563 - **Repository:** - **Paper:** https://www.microsoft.com/en-us/research/wp-content/uploads/2016/09/Sentence_Compression_final-1.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains sentences and short paragraphs with corresponding shorter (compressed) versions. There are up to five compressions for each input text, together with quality judgements of their meaning preservation and grammaticality. The dataset is derived using source texts from the Open American National Corpus (ww.anc.org) and crowd-sourcing. ### Supported Tasks and Leaderboards Text Summarization ### Languages English ## Dataset Structure ### Data Instances It contains approximately 6,000 source texts with multiple compressions (about 26,000 pairs of source and compressed texts), representing business letters, newswire, journals, and technical documents sampled from the Open American National Corpus (OANC1). - Each source text is accompanied by up to five crowd-sourced rewrites constrained to a preset compression ratio and annotated with quality judgments. Multiple rewrites permit study of the impact of operations on human compression quality and facilitate automatic evaluation. - This dataset is the first to provide compressions at the multi-sentence (two-sentence paragraph) level, which may present a stepping stone to whole document summarization. - Many of these two-sentence paragraphs are compressed both as paragraphs and separately sentence-bysentence, offering data that may yield insights into the impact of multi-sentence operations on human compression quality. | Description | Source | Target | Average CPS | Meaning Quality | Grammar Quality | | :------------- | :----------: | -----------: | -----------: | -----------: | -----------: | | 1-Sentence | 3764 | 15523 | 4.12 | 2.78 | 2.81 | | 2-Sentence | 2405 | 10900 | 4.53 | 2.78 | 2.83 | **Note**: Average CPS = Average Compressions per Source Text ### Data Fields ``` {'domain': 'Newswire', 'source_id': '106', 'source_text': '" Except for this small vocal minority, we have just not gotten a lot of groundswell against this from members, " says APA president Philip G. Zimbardo of Stanford University.', 'targets': {'compressed_text': ['"Except for this small vocal minority, we have not gotten a lot of groundswell against this," says APA president Zimbardo.', '"Except for a vocal minority, we haven\'t gotten much groundswell from members, " says Philip G. Zimbardo of Stanford University.', 'APA president of Stanford has stated that except for a vocal minority they have not gotten a lot of pushback from members.', 'APA president Philip G. Zimbardo of Stanford says they have not had much opposition against this.'], 'judge_id': ['2', '22', '10', '0'], 'num_ratings': [3, 3, 3, 3], 'ratings': [[6, 6, 6], [11, 6, 6], [6, 11, 6], [6, 11, 11]]}} ``` - source_id: index of article per original dataset - source_text: uncompressed original text - domain: source of the article - targets: - compressed_text: compressed version of `source_text` - judge_id: anonymized ids of crowdworkers who proposed compression - num_ratings: number of ratings available for each proposed compression - ratings: see table below Ratings system (excerpted from authors' README): - 6 = Most important meaning Flawless language (3 on meaning and 3 on grammar as per the paper's terminology) - 7 = Most important meaning Minor errors (3 on meaning and 2 on grammar) - 9 = Most important meaning Disfluent or incomprehensible (3 on meaning and 1 on grammar) - 11 = Much meaning Flawless language (2 on meaning and 3 on grammar) - 12 = Much meaning Minor errors (2 on meaning and 2 on grammar) - 14 = Much meaning Disfluent or incomprehensible (2 on meaning and 1 on grammar) - 21 = Little or none meaning Flawless language (1 on meaning and 3 on grammar) - 22 = Little or none meaning Minor errors (1 on meaning and 2 on grammar) - 24 = Little or none meaning Disfluent or incomprehensible (1 on meaning and 1 on grammar) See **README.txt** from data archive for additional details. ### Data Splits There are 4,936 source texts in the training, 448 in the development, and 785 in the test set. ## Dataset Creation ### Annotations #### Annotation process Compressions were created using UHRS, an inhouse crowd-sourcing system similar to Amazon’s Mechanical Turk, in two annotation rounds, one for shortening and a second to rate compression quality: 1. In the first round, five workers were tasked with abridging each source text by at least 25%, while remaining grammatical and fluent, and retaining the meaning of the original. 2. In the second round, 3-5 judges (raters) were asked to evaluate the grammaticality of each compression on a scale from 1 (major errors, disfluent) through 3 (fluent), and again analogously for meaning preservation on a scale from 1 (orthogonal) through 3 (most important meaning-preserving). ## Additional Information ### Licensing Information Microsoft Research Data License Agreement ### Citation Information @inproceedings{Toutanova2016ADA, title={A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs}, author={Kristina Toutanova and Chris Brockett and Ke M. Tran and Saleema Amershi}, booktitle={EMNLP}, year={2016} } ### Contributions Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset.

msr_text_compression

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|other-Open-American-National-Corpus-(OANC1)"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "MsrTextCompression", "license_details": "Microsoft Research Data License Agreement", "dataset_info": {"features": [{"name": "source_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "source_text", "dtype": "string"}, {"name": "targets", "sequence": [{"name": "compressed_text", "dtype": "string"}, {"name": "judge_id", "dtype": "string"}, {"name": "num_ratings", "dtype": "int64"}, {"name": "ratings", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 5001312, "num_examples": 4936}, {"name": "validation", "num_bytes": 449691, "num_examples": 447}, {"name": "test", "num_bytes": 804536, "num_examples": 785}], "download_size": 0, "dataset_size": 6255539}}

2024-01-18T11:09:30+00:00

TAGS #task_categories-summarization #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|other-Open-American-National-Corpus-(OANC1) #language-English #license-other #region-us

Dataset Card for [Dataset Name] =============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary This dataset contains sentences and short paragraphs with corresponding shorter (compressed) versions. There are up to five compressions for each input text, together with quality judgements of their meaning preservation and grammaticality. The dataset is derived using source texts from the Open American National Corpus (URL) and crowd-sourcing. ### Supported Tasks and Leaderboards Text Summarization ### Languages English Dataset Structure ----------------- ### Data Instances It contains approximately 6,000 source texts with multiple compressions (about 26,000 pairs of source and compressed texts), representing business letters, newswire, journals, and technical documents sampled from the Open American National Corpus (OANC1). * Each source text is accompanied by up to five crowd-sourced rewrites constrained to a preset compression ratio and annotated with quality judgments. Multiple rewrites permit study of the impact of operations on human compression quality and facilitate automatic evaluation. * This dataset is the first to provide compressions at the multi-sentence (two-sentence paragraph) level, which may present a stepping stone to whole document summarization. * Many of these two-sentence paragraphs are compressed both as paragraphs and separately sentence-bysentence, offering data that may yield insights into the impact of multi-sentence operations on human compression quality. Note: Average CPS = Average Compressions per Source Text ### Data Fields * source\_id: index of article per original dataset * source\_text: uncompressed original text * domain: source of the article * targets: + compressed\_text: compressed version of 'source\_text' + judge\_id: anonymized ids of crowdworkers who proposed compression + num\_ratings: number of ratings available for each proposed compression + ratings: see table below Ratings system (excerpted from authors' README): * 6 = Most important meaning Flawless language (3 on meaning and 3 on grammar as per the paper's terminology) * 7 = Most important meaning Minor errors (3 on meaning and 2 on grammar) * 9 = Most important meaning Disfluent or incomprehensible (3 on meaning and 1 on grammar) * 11 = Much meaning Flawless language (2 on meaning and 3 on grammar) * 12 = Much meaning Minor errors (2 on meaning and 2 on grammar) * 14 = Much meaning Disfluent or incomprehensible (2 on meaning and 1 on grammar) * 21 = Little or none meaning Flawless language (1 on meaning and 3 on grammar) * 22 = Little or none meaning Minor errors (1 on meaning and 2 on grammar) * 24 = Little or none meaning Disfluent or incomprehensible (1 on meaning and 1 on grammar) See URL from data archive for additional details. ### Data Splits There are 4,936 source texts in the training, 448 in the development, and 785 in the test set. Dataset Creation ---------------- ### Annotations #### Annotation process Compressions were created using UHRS, an inhouse crowd-sourcing system similar to Amazon’s Mechanical Turk, in two annotation rounds, one for shortening and a second to rate compression quality: 1. In the first round, five workers were tasked with abridging each source text by at least 25%, while remaining grammatical and fluent, and retaining the meaning of the original. 2. In the second round, 3-5 judges (raters) were asked to evaluate the grammaticality of each compression on a scale from 1 (major errors, disfluent) through 3 (fluent), and again analogously for meaning preservation on a scale from 1 (orthogonal) through 3 (most important meaning-preserving). Additional Information ---------------------- ### Licensing Information Microsoft Research Data License Agreement @inproceedings{Toutanova2016ADA, title={A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs}, author={Kristina Toutanova and Chris Brockett and Ke M. Tran and Saleema Amershi}, booktitle={EMNLP}, year={2016} } ### Contributions Thanks to @jeromeku for adding this dataset.

466bc4cb5888dc309c5694492056e2aab739a430

# Dataset Card for msr_zhen_translation_parity ## 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:** [Translator Human Parity Data](https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b) - **Repository:** - **Paper:** [Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary > Human evaluation results and translation output for the Translator Human Parity Data release, > as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/ > The Translator Human Parity Data release contains all human evaluation results and translations > related to our paper "Achieving Human Parity on Automatic Chinese to English News Translation", > published on March 14, 2018. We have released this data to > 1) allow external validation of our claim of having achieved human parity > 2) to foster future research by releasing two additional human references > for the Reference-WMT test set. > The dataset includes: 1) two new references for Chinese-English language pair of WMT17, one based on human translation from scratch (Reference-HT), the other based on human post-editing (Reference-PE); 2) human parity translations generated by our research systems Combo-4, Combo-5, and Combo-6, as well as translation output from online machine translation service Online-A-1710, collected on October 16, 2017; The data package provided with the study also includes (but not parsed and provided as workable features of this dataset) all data points collected in human evaluation campaigns. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages This dataset contains 6 extra English translations to Chinese-English language pair of WMT17. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields As mentioned in the summary, this dataset provides 6 extra English translations of Chinese-English language pair of WMT17. Data fields are named exactly like the associated paper for easier cross-referenceing. - `Reference-HT`: human translation from scrach. - `Reference-PE`: human post-editing. - `Combo-4`, `Combo-5`, `Combo-6`: three translations by research systems. - `Online-A-1710`: a translation from an anonymous online machine translation service. All data fields of a record are translations for the same Chinese source sentence. ### 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 Citation information is available at this link [Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/) ### Contributions Thanks to [@leoxzhao](https://github.com/leoxzhao) for adding this dataset.

msr_zhen_translation_parity

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated", "machine-generated"], "language": ["en"], "license": ["ms-pl"], "multilinguality": ["monolingual", "translation"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|other-newstest2017"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "MsrZhenTranslationParity", "dataset_info": {"features": [{"name": "Reference-HT", "dtype": "string"}, {"name": "Reference-PE", "dtype": "string"}, {"name": "Combo-4", "dtype": "string"}, {"name": "Combo-5", "dtype": "string"}, {"name": "Combo-6", "dtype": "string"}, {"name": "Online-A-1710", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1797033, "num_examples": 2001}], "download_size": 0, "dataset_size": 1797033}}

2024-01-18T11:09:33+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #multilinguality-translation #size_categories-1K<n<10K #source_datasets-extended|other-newstest2017 #language-English #license-ms-pl #region-us

# Dataset Card for msr_zhen_translation_parity ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Translator Human Parity Data - Repository: - Paper: Achieving Human Parity on Automatic Chinese to English News Translation - Leaderboard: - Point of Contact: ### Dataset Summary > Human evaluation results and translation output for the Translator Human Parity Data release, > as described in URL > The Translator Human Parity Data release contains all human evaluation results and translations > related to our paper "Achieving Human Parity on Automatic Chinese to English News Translation", > published on March 14, 2018. We have released this data to > 1) allow external validation of our claim of having achieved human parity > 2) to foster future research by releasing two additional human references > for the Reference-WMT test set. > The dataset includes: 1) two new references for Chinese-English language pair of WMT17, one based on human translation from scratch (Reference-HT), the other based on human post-editing (Reference-PE); 2) human parity translations generated by our research systems Combo-4, Combo-5, and Combo-6, as well as translation output from online machine translation service Online-A-1710, collected on October 16, 2017; The data package provided with the study also includes (but not parsed and provided as workable features of this dataset) all data points collected in human evaluation campaigns. ### Supported Tasks and Leaderboards ### Languages This dataset contains 6 extra English translations to Chinese-English language pair of WMT17. ## Dataset Structure ### Data Instances ### Data Fields As mentioned in the summary, this dataset provides 6 extra English translations of Chinese-English language pair of WMT17. Data fields are named exactly like the associated paper for easier cross-referenceing. - 'Reference-HT': human translation from scrach. - 'Reference-PE': human post-editing. - 'Combo-4', 'Combo-5', 'Combo-6': three translations by research systems. - 'Online-A-1710': a translation from an anonymous online machine translation service. All data fields of a record are translations for the same Chinese source sentence. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Citation information is available at this link Achieving Human Parity on Automatic Chinese to English News Translation ### Contributions Thanks to @leoxzhao for adding this dataset.

250b3cc647ff1cef9d592301a5ba501679e27e91

# Dataset Card for MSRA NER ## 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/OYE93/Chinese-NLP-Corpus/tree/master/NER/MSRA) - **Repository:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus) - **Paper:** - **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.

msra_ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["zh"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "MSRA NER", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "config_name": "msra_ner", "splits": [{"name": "train", "num_bytes": 33323074, "num_examples": 45001}, {"name": "test", "num_bytes": 2642934, "num_examples": 3443}], "download_size": 15156606, "dataset_size": 35966008}, "train-eval-index": [{"config": "msra_ner", "task": "token-classification", "task_id": "entity_extraction", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"tokens": "tokens", "ner_tags": "tags"}, "metrics": [{"type": "seqeval", "name": "seqeval"}]}]}

2024-01-18T11:09:36+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Chinese #license-unknown #region-us

# Dataset Card for MSRA NER ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Github - Repository: Github - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @JetRunner for adding this dataset.

c30130315b68ace89174cfa285fa98240a92a106

# Dataset Card for mt_eng_vietnamese ## 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.stanford.edu/projects/nmt/data/iwslt15.en-vi/ - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Preprocessed Dataset from IWSLT'15 English-Vietnamese machine translation: English-Vietnamese. ### Supported Tasks and Leaderboards Machine Translation ### Languages English, Vietnamese ## Dataset Structure ### Data Instances An example from the dataset: ``` { 'translation': { 'en': 'In 4 minutes , atmospheric chemist Rachel Pike provides a glimpse of the massive scientific effort behind the bold headlines on climate change , with her team -- one of thousands who contributed -- taking a risky flight over the rainforest in pursuit of data on a key molecule .', 'vi': 'Trong 4 phút , chuyên gia hoá học khí quyển Rachel Pike giới thiệu sơ lược về những nỗ lực khoa học miệt mài đằng sau những tiêu đề táo bạo về biến đổi khí hậu , cùng với đoàn nghiên cứu của mình -- hàng ngàn người đã cống hiến cho dự án này -- một chuyến bay mạo hiểm qua rừng già để tìm kiếm thông tin về một phân tử then chốt .' } } ``` ### Data Fields - translation: - en: text in english - vi: text in vietnamese ### Data Splits train: 133318, validation: 1269, test: 1269 ## 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{Luong-Manning:iwslt15, Address = {Da Nang, Vietnam} Author = {Luong, Minh-Thang and Manning, Christopher D.}, Booktitle = {International Workshop on Spoken Language Translation}, Title = {Stanford Neural Machine Translation Systems for Spoken Language Domain}, Year = {2015}} ``` ### Contributions Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput) for adding this dataset.

mt_eng_vietnamese

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en", "vi"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "MtEngVietnamese", "dataset_info": [{"config_name": "iwslt2015-vi-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["vi", "en"]}}}], "splits": [{"name": "train", "num_bytes": 32478282, "num_examples": 133318}, {"name": "validation", "num_bytes": 323743, "num_examples": 1269}, {"name": "test", "num_bytes": 323743, "num_examples": 1269}], "download_size": 32323025, "dataset_size": 33125768}, {"config_name": "iwslt2015-en-vi", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "vi"]}}}], "splits": [{"name": "train", "num_bytes": 32478282, "num_examples": 133318}, {"name": "validation", "num_bytes": 323743, "num_examples": 1269}, {"name": "test", "num_bytes": 323743, "num_examples": 1269}], "download_size": 32323025, "dataset_size": 33125768}]}

2024-01-18T11:09:37+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-English #language-Vietnamese #license-unknown #region-us

# Dataset Card for mt_eng_vietnamese ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Preprocessed Dataset from IWSLT'15 English-Vietnamese machine translation: English-Vietnamese. ### Supported Tasks and Leaderboards Machine Translation ### Languages English, Vietnamese ## Dataset Structure ### Data Instances An example from the dataset: ### Data Fields - translation: - en: text in english - vi: text in vietnamese ### Data Splits train: 133318, validation: 1269, test: 1269 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @Nilanshrajput for adding this dataset.

645fd1ec568d98ff4e48d015608553a70633de59

# Dataset Card for Muchocine ## 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.lsi.us.es/~fermin/index.php/Datasets ### Dataset Summary The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language, each with a shorter summary review, and a rating on a 1-5 scale. ### Supported Tasks and Leaderboards - `text-classification`: This dataset can be used for Text Classification, more precisely Sentiment Classification where the task is to predict the `star_rating` for a `reveiw_body` or a `review summaray`. ### Languages Spanish. ## Dataset Structure ### Data Instances An example from the train split: ``` { 'review_body': 'Zoom nos cuenta la historia de Jack Shepard, anteriormente conocido como el Capitán Zoom, Superhéroe que perdió sus poderes y que actualmente vive en el olvido. La llegada de una amenaza para la Tierra hará que la agencia del gobierno que se ocupa de estos temas acuda a él para que entrene a un grupo de jóvenes con poderes para combatir esta amenaza.Zoom es una comedia familiar, con todo lo que eso implica, es decir, guión flojo y previsible, bromas no salidas de tono, historia amorosa de por medio y un desenlace tópico. La gracia está en que los protagonistas son jóvenes con superpoderes, una producción cargada de efectos especiales y unos cuantos guiños frikis. La película además se pasa volando ya que dura poco mas de ochenta minutos y cabe destacar su prologo en forma de dibujos de comics explicando la historia de la cual partimos en la película.Tim Allen protagoniza la cinta al lado de un envejecido Chevy Chase, que hace de doctor encargado del proyecto, un papel bastante gracioso y ridículo, pero sin duda el mejor papel es el de Courteney Cox, en la piel de una científica amante de los comics y de lo más friki. Del grupito de los cuatro niños sin duda la mas graciosa es la niña pequeña con súper fuerza y la que provocara la mayor parte de los gags debido a su poder.Una comedia entretenida y poca cosa más para ver una tarde de domingo. ', 'review_summary': 'Una comedia entretenida y poca cosa más para ver una tarde de domingo ', 'star_rating': 2 } ``` ### Data Fields - `review_body` - longform review - `review_summary` - shorter-form review - `star_rating` - an integer star rating (1-5) The original source also includes part-of-speech tagging for body and summary fields. ### Data Splits One split (train) with 3,872 reviews. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Data was collected from www.muchocine.net and uploaded by Dr. Fermín L. Cruz Mata of La Universidad de Sevilla. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The text reviews and star ratings came directly from users, so no additional annotation was 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 Dr. Fermín L. Cruz Mata. ### Licensing Information [More Information Needed] ### Citation Information See http://www.lsi.us.es/~fermin/index.php/Datasets ### Contributions Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset.

muchocine

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["es"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "Muchocine", "dataset_info": {"features": [{"name": "review_body", "dtype": "string"}, {"name": "review_summary", "dtype": "string"}, {"name": "star_rating", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5"}}}}], "splits": [{"name": "train", "num_bytes": 11871095, "num_examples": 3872}], "download_size": 55556703, "dataset_size": 11871095}}

2024-01-18T11:09:39+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-unknown #region-us

# Dataset Card for Muchocine ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL ### Dataset Summary The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language, each with a shorter summary review, and a rating on a 1-5 scale. ### Supported Tasks and Leaderboards - 'text-classification': This dataset can be used for Text Classification, more precisely Sentiment Classification where the task is to predict the 'star_rating' for a 'reveiw_body' or a 'review summaray'. ### Languages Spanish. ## Dataset Structure ### Data Instances An example from the train split: ### Data Fields - 'review_body' - longform review - 'review_summary' - shorter-form review - 'star_rating' - an integer star rating (1-5) The original source also includes part-of-speech tagging for body and summary fields. ### Data Splits One split (train) with 3,872 reviews. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization Data was collected from URL and uploaded by Dr. Fermín L. Cruz Mata of La Universidad de Sevilla. #### Who are the source language producers? ### Annotations #### Annotation process The text reviews and star ratings came directly from users, so no additional annotation was needed. #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators Dr. Fermín L. Cruz Mata. ### Licensing Information See URL ### Contributions Thanks to @mapmeld for adding this dataset.

7d17b781ad7fd91e6e99015bb68072bec5339bc0

# Dataset Card for MultiBooked ## 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://hdl.handle.net/10230/33928 - **Repository:** https://github.com/jerbarnes/multibooked - **Paper:** https://arxiv.org/abs/1803.08614 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification. The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the guidelines set out in the OpeNER project. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Each sub-dataset is monolingual in the languages: - ca: Catalan - eu: Basque ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `text`: layer of the original text. - `wid`: list of word IDs for each word within the example. - `sent`: list of sentence IDs for each sentence within the example. - `para`: list of paragraph IDs for each paragraph within the example. - `word`: list of words. - `terms`: layer of the terms resulting from the analysis of the original text (lemmatization, morphological, PoS tagging) - `tid`: list of term IDs for each term within the example. - `lemma`: list of lemmas. - `morphofeat`: list of morphological features. - `pos`: list of PoS tags. - `target`: list of sublists of the corresponding word IDs (normally, the sublists contain only one element, in a one-to-one correspondence between words and terms). - `opinions`: layer of the opinions in the text. - `oid`: list of opinion IDs - `opinion_holder_target`: list of sublists of the corresponding term IDs that span the opinion holder. - `opinion_target_target`: list of sublists of the corresponding term IDs that span the opinion target. - `opinion_expression_polarity`: list of the opinion expression polarities. The polarity can take one of the values: `StrongNegative`, `Negative`, `Positive`, or `StrongPositive`. - `opinion_expression_target`: list of sublists of the corresponding term IDs that span the opinion expression. ### 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 3.0](https://creativecommons.org/licenses/by/3.0/) license. ### Citation Information ``` @inproceedings{Barnes2018multibooked, author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni}, title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)}, year = {2018}, month = {May}, date = {7-12}, address = {Miyazaki, Japan}, publisher = {European Language Resources Association (ELRA)}, language = {english} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

multi_booked

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca", "eu"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "multibooked", "pretty_name": "MultiBooked", "config_names": ["ca", "eu"], "dataset_info": [{"config_name": "ca", "features": [{"name": "text", "sequence": [{"name": "wid", "dtype": "string"}, {"name": "sent", "dtype": "string"}, {"name": "para", "dtype": "string"}, {"name": "word", "dtype": "string"}]}, {"name": "terms", "sequence": [{"name": "tid", "dtype": "string"}, {"name": "lemma", "dtype": "string"}, {"name": "morphofeat", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "target", "sequence": "string"}]}, {"name": "opinions", "sequence": [{"name": "oid", "dtype": "string"}, {"name": "opinion_holder_target", "sequence": "string"}, {"name": "opinion_target_target", "sequence": "string"}, {"name": "opinion_expression_polarity", "dtype": {"class_label": {"names": {"0": "StrongNegative", "1": "Negative", "2": "Positive", "3": "StrongPositive"}}}}, {"name": "opinion_expression_target", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 1952731, "num_examples": 567}], "download_size": 4429415, "dataset_size": 1952731}, {"config_name": "eu", "features": [{"name": "text", "sequence": [{"name": "wid", "dtype": "string"}, {"name": "sent", "dtype": "string"}, {"name": "para", "dtype": "string"}, {"name": "word", "dtype": "string"}]}, {"name": "terms", "sequence": [{"name": "tid", "dtype": "string"}, {"name": "lemma", "dtype": "string"}, {"name": "morphofeat", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "target", "sequence": "string"}]}, {"name": "opinions", "sequence": [{"name": "oid", "dtype": "string"}, {"name": "opinion_holder_target", "sequence": "string"}, {"name": "opinion_target_target", "sequence": "string"}, {"name": "opinion_expression_polarity", "dtype": {"class_label": {"names": {"0": "StrongNegative", "1": "Negative", "2": "Positive", "3": "StrongPositive"}}}}, {"name": "opinion_expression_target", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 1175816, "num_examples": 343}], "download_size": 4429415, "dataset_size": 1175816}]}

2024-01-18T11:09:42+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Catalan #language-Basque #license-cc-by-3.0 #arxiv-1803.08614 #region-us

# Dataset Card for MultiBooked ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification. The corpora are compiled from hotel reviews taken mainly from URL. The corpora are in Kaf/Naf format, which is an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the guidelines set out in the OpeNER project. ### Supported Tasks and Leaderboards ### Languages Each sub-dataset is monolingual in the languages: - ca: Catalan - eu: Basque ## Dataset Structure ### Data Instances ### Data Fields - 'text': layer of the original text. - 'wid': list of word IDs for each word within the example. - 'sent': list of sentence IDs for each sentence within the example. - 'para': list of paragraph IDs for each paragraph within the example. - 'word': list of words. - 'terms': layer of the terms resulting from the analysis of the original text (lemmatization, morphological, PoS tagging) - 'tid': list of term IDs for each term within the example. - 'lemma': list of lemmas. - 'morphofeat': list of morphological features. - 'pos': list of PoS tags. - 'target': list of sublists of the corresponding word IDs (normally, the sublists contain only one element, in a one-to-one correspondence between words and terms). - 'opinions': layer of the opinions in the text. - 'oid': list of opinion IDs - 'opinion_holder_target': list of sublists of the corresponding term IDs that span the opinion holder. - 'opinion_target_target': list of sublists of the corresponding term IDs that span the opinion target. - 'opinion_expression_polarity': list of the opinion expression polarities. The polarity can take one of the values: 'StrongNegative', 'Negative', 'Positive', or 'StrongPositive'. - 'opinion_expression_target': list of sublists of the corresponding term IDs that span the opinion expression. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Dataset is under the CC-BY 3.0 license. ### Contributions Thanks to @albertvillanova for adding this dataset.

fed4e4005fbc67c1abbdb8d3311561f0effd72a0

# Dataset Card for "MultiEURLEX" ## 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:** https://github.com/nlpaueb/MultiEURLEX/ - **Paper:** https://arxiv.org/abs/2109.00904 - **Data:** https://doi.org/10.5281/zenodo.5363165 - **Leaderboard:** N/A - **Point of Contact:** [Ilias Chalkidis](mailto:[email protected]) ### Dataset Summary **Documents** MultiEURLEX comprises 65k EU laws in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a *label descriptor*, e.g., [60, agri-foodstuffs], [6006, plant product], [1115, fruit]. The descriptors are also available in the 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EUR-LEX, comprising 57k EU laws with the originally assigned gold labels. **Multi-granular Labeling** EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8. We created three alternative sets of labels per document, by replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively. Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment. Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3. **Data Split and Concept Drift** MultiEURLEX is *chronologically* split in training (55k, 1958-2010), development (5k, 2010-2012), test (5k, 2012-2016) subsets, using the English documents. The test subset contains the same 5k documents in all 23 languages. The development subset also contains the same 5k documents in 23 languages, except Croatian. Croatia is the most recent EU member (2013); older laws are gradually translated. For the official languages of the seven oldest member countries, the same 55k training documents are available; for the other languages, only a subset of the 55k training documents is available. Compared to EUR-LEX (Chalkidis et al., 2019), MultiEURLEX is not only larger (8k more documents) and multilingual; it is also more challenging, as the chronological split leads to temporal real-world *concept drift* across the training, development, test subsets, i.e., differences in label distribution and phrasing, representing a realistic *temporal generalization* problem (Huang et al., 2019; Lazaridou et al., 2021). Recently, Søgaard et al. (2021) showed this setup is more realistic, as it does not over-estimate real performance, contrary to random splits (Gorman and Bedrick, 2019). ### Supported Tasks and Leaderboards Similarly to EUR-LEX (Chalkidis et al., 2019), MultiEURLEX can be used for legal topic classification, a multi-label classification task where legal documents need to be assigned concepts (in our case, from EUROVOC) reflecting their topics. Unlike EUR-LEX, however, MultiEURLEX supports labels from three different granularities (EUROVOC levels). More importantly, apart from monolingual (*one-to-one*) experiments, it can be used to study cross-lingual transfer scenarios, including *one-to-many* (systems trained in one language and used in other languages with no training data), and *many-to-one* or *many-to-many* (systems jointly trained in multiple languages and used in one or more other languages). The dataset is not yet part of an established benchmark. ### Languages The EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at https://europa.eu/european-union/about-eu/eu-languages_en). This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them. ## Dataset Structure ### Data Instances **Multilingual use of the dataset** When the dataset is used in a multilingual setting selecting the the 'all_languages' flag: ```python from datasets import load_dataset dataset = load_dataset('multi_eurlex', 'all_languages') ``` ```json { "celex_id": "31979D0509", "text": {"en": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,", "es": "DECISIÓN DEL CONSEJO de 24 de mayo de 1979 sobre ayuda financiera de la Comunidad para la erradicación de la peste porcina africana en España (79/509/CEE)\nEL CONSEJO DE LAS COMUNIDADES EUROPEAS\nVeniendo en cuenta el Tratado constitutivo de la Comunidad Económica Europea y, en particular, Su artículo 43,\n Vista la propuesta de la Comisión (1),\n Visto el dictamen del Parlamento Europeo (2),\nConsiderando que la Comunidad debe tomar todas las medidas adecuadas para protegerse contra la aparición de la peste porcina africana en su territorio;\nConsiderando a tal fin que la Comunidad ha emprendido y sigue llevando a cabo acciones destinadas a contener los brotes de este tipo de enfermedades lejos de sus fronteras, ayudando a los países afectados a reforzar sus medidas preventivas; que a tal efecto ya se han concedido a España subvenciones comunitarias;\nQue estas medidas han contribuido sin duda alguna a la protección de la ganadería comunitaria, especialmente mediante la creación y mantenimiento de una zona tampón al norte del río Ebro;\nConsiderando, no obstante, , a juicio de las propias autoridades españolas, las medidas implementadas hasta ahora deben reforzarse si se quiere alcanzar el objetivo fundamental de erradicar la enfermedad en todo el país;\nConsiderando que las autoridades españolas han pedido a la Comunidad que contribuya a los gastos necesarios para la ejecución eficaz de un programa de erradicación total;\nConsiderando que conviene dar una respuesta favorable a esta solicitud concediendo una ayuda a España, habida cuenta del compromiso asumido por dicho país de proteger a la Comunidad contra la peste porcina africana y de eliminar completamente esta enfermedad al final de un plan de erradicación de cinco años;\nMientras que este plan de erradicación debe incluir e determinadas medidas que garanticen la eficacia de las acciones emprendidas, debiendo ser posible adaptar estas medidas a la evolución de la situación mediante un procedimiento que establezca una estrecha cooperación entre los Estados miembros y la Comisión;\nConsiderando que es necesario mantener el Los Estados miembros informados periódicamente sobre el progreso de las acciones emprendidas.", "de": "...", "bg": "..." }, "labels": [ 1, 13, 47 ] } ``` **Monolingual use of the dataset** When the dataset is used in a monolingual setting selecting the ISO language code for one of the 23 supported languages. For example: ```python from datasets import load_dataset dataset = load_dataset('multi_eurlex', 'en') ``` ```json { "celex_id": "31979D0509", "text": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,", "labels": [ 1, 13, 47 ] } ``` ### Data Fields **Multilingual use of the dataset** The following data fields are provided for documents (`train`, `dev`, `test`): `celex_id`: (**str**) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\ `text`: (dict[**str**]) A dictionary with the 23 languages as keys and the full content of each document as values.\ `labels`: (**List[int]**) The relevant EUROVOC concepts (labels). **Monolingual use of the dataset** The following data fields are provided for documents (`train`, `dev`, `test`): `celex_id`: (**str**) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\ `text`: (**str**) The full content of each document across languages.\ `labels`: (**List[int]**) The relevant EUROVOC concepts (labels). If you want to use the descriptors of the EUROVOC concepts, similar to [Chalkidis et al. (2020)](https://aclanthology.org/2020.emnlp-main.607/), please download the relevant JSON file [here](https://raw.githubusercontent.com/nlpaueb/multi-eurlex/master/data/eurovoc_descriptors.json). Then you may load it and use it: ```python import json from datasets import load_dataset # Load the English part of the dataset dataset = load_dataset('multi_eurlex', 'en', split='train') # Load (label_id, descriptor) mapping with open('./eurovoc_descriptors.json') as jsonl_file: eurovoc_concepts = json.load(jsonl_file) # Get feature map info classlabel = dataset.features["labels"].feature # Retrieve IDs and descriptors from dataset for sample in dataset: print(f'DOCUMENT: {sample["celex_id"]}') # DOCUMENT: 32006D0213 for label_id in sample['labels']: print(f'LABEL: id:{label_id}, eurovoc_id: {classlabel.int2str(label_id)}, \ eurovoc_desc:{eurovoc_concepts[classlabel.int2str(label_id)]}') # LABEL: id: 1, eurovoc_id: '100160', eurovoc_desc: 'industry' ``` ### Data Splits <table> <tr><td> Language </td> <td> ISO code </td> <td> Member Countries where official </td> <td> EU Speakers [1] </td> <td> Number of Documents [2] </td> </tr> <tr><td> English </td> <td> <b>en</b> </td> <td> United Kingdom (1973-2020), Ireland (1973), Malta (2004) </td> <td> 13/ 51% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> German </td> <td> <b>de</b> </td> <td> Germany (1958), Belgium (1958), Luxembourg (1958) </td> <td> 16/32% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> French </td> <td> <b>fr</b> </td> <td> France (1958), Belgium(1958), Luxembourg (1958) </td> <td> 12/26% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Italian </td> <td> <b>it</b> </td> <td> Italy (1958) </td> <td> 13/16% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Spanish </td> <td> <b>es</b> </td> <td> Spain (1986) </td> <td> 8/15% </td> <td> 52,785 / 5,000 / 5,000 </td> </tr> <tr><td> Polish </td> <td> <b>pl</b> </td> <td> Poland (2004) </td> <td> 8/9% </td> <td> 23,197 / 5,000 / 5,000 </td> </tr> <tr><td> Romanian </td> <td> <b>ro</b> </td> <td> Romania (2007) </td> <td> 5/5% </td> <td> 15,921 / 5,000 / 5,000 </td> </tr> <tr><td> Dutch </td> <td> <b>nl</b> </td> <td> Netherlands (1958), Belgium (1958) </td> <td> 4/5% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Greek </td> <td> <b>el</b> </td> <td> Greece (1981), Cyprus (2008) </td> <td> 3/4% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Hungarian </td> <td> <b>hu</b> </td> <td> Hungary (2004) </td> <td> 3/3% </td> <td> 22,664 / 5,000 / 5,000 </td> </tr> <tr><td> Portuguese </td> <td> <b>pt</b> </td> <td> Portugal (1986) </td> <td> 2/3% </td> <td> 23,188 / 5,000 / 5,000 </td> </tr> <tr><td> Czech </td> <td> <b>cs</b> </td> <td> Czech Republic (2004) </td> <td> 2/3% </td> <td> 23,187 / 5,000 / 5,000 </td> </tr> <tr><td> Swedish </td> <td> <b>sv</b> </td> <td> Sweden (1995) </td> <td> 2/3% </td> <td> 42,490 / 5,000 / 5,000 </td> </tr> <tr><td> Bulgarian </td> <td> <b>bg</b> </td> <td> Bulgaria (2007) </td> <td> 2/2% </td> <td> 15,986 / 5,000 / 5,000 </td> </tr> <tr><td> Danish </td> <td> <b>da</b> </td> <td> Denmark (1973) </td> <td> 1/1% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Finnish </td> <td> <b>fi</b> </td> <td> Finland (1995) </td> <td> 1/1% </td> <td> 42,497 / 5,000 / 5,000 </td> </tr> <tr><td> Slovak </td> <td> <b>sk</b> </td> <td> Slovakia (2004) </td> <td> 1/1% </td> <td> 15,986 / 5,000 / 5,000 </td> </tr> <tr><td> Lithuanian </td> <td> <b>lt</b> </td> <td> Lithuania (2004) </td> <td> 1/1% </td> <td> 23,188 / 5,000 / 5,000 </td> </tr> <tr><td> Croatian </td> <td> <b>hr</b> </td> <td> Croatia (2013) </td> <td> 1/1% </td> <td> 7,944 / 2,500 / 5,000 </td> </tr> <tr><td> Slovene </td> <td> <b>sl</b> </td> <td> Slovenia (2004) </td> <td> <1/<1% </td> <td> 23,184 / 5,000 / 5,000 </td> </tr> <tr><td> Estonian </td> <td> <b>et</b> </td> <td> Estonia (2004) </td> <td> <1/<1% </td> <td> 23,126 / 5,000 / 5,000 </td> </tr> <tr><td> Latvian </td> <td> <b>lv</b> </td> <td> Latvia (2004) </td> <td> <1/<1% </td> <td> 23,188 / 5,000 / 5,000 </td> </tr> <tr><td> Maltese </td> <td> <b>mt</b> </td> <td> Malta (2004) </td> <td> <1/<1% </td> <td> 17,521 / 5,000 / 5,000 </td> </tr> </table> [1] Native and Total EU speakers percentage (%) \ [2] Training / Development / Test Splits ## Dataset Creation ### Curation Rationale The dataset was curated by Chalkidis et al. (2021).\ The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en). ### Source Data #### Initial Data Collection and Normalization The original data are available at the EUR-LEX portal (https://eur-lex.europa.eu) in unprocessed formats (HTML, XML, RDF). The documents were downloaded from the EUR-LEX portal in HTML. The relevant EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql). We stripped HTML mark-up to provide the documents in plain text format. We inferred the labels for EUROVOC levels 1--3, by backtracking the EUROVOC hierarchy branches, from the originally assigned labels to their ancestors in levels 1--3, respectively. #### Who are the source language producers? The EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at https://europa.eu/european-union/about-eu/eu-languages_en). This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them. ### Annotations #### Annotation process All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/). EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8. We augmented the annotation with three alternative sets of labels per document, replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively. Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment.Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3. #### Who are the annotators? Publications Office of EU (https://publications.europa.eu/en) ### Personal and Sensitive Information The dataset contains publicly available EU laws that do not include personal or sensitive information with the exception of trivial information presented by consent, e.g., the names of the current presidents of the European Parliament and European Council, and other administration bodies. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). This does not imply that no other languages are spoken in EU countries, although EU laws are not translated to other languages (https://europa.eu/european-union/about-eu/eu-languages_en). ## Additional Information ### Dataset Curators Chalkidis et al. (2021) ### Licensing Information We provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0): © European Union, 1998-2021 The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes. The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \ Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html ### Citation Information *Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos.* *MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer.* *Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic. 2021* ``` @InProceedings{chalkidis-etal-2021-multieurlex, author = {Chalkidis, Ilias and Fergadiotis, Manos and Androutsopoulos, Ion}, title = {MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer}, booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, year = {2021}, publisher = {Association for Computational Linguistics}, location = {Punta Cana, Dominican Republic}, url = {https://arxiv.org/abs/2109.00904} } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.

multi_eurlex

2022-03-02T23:29:22+00:00

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2023-06-14T12:34:30+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-sa-4.0 #arxiv-2109.00904 #region-us

Dataset Card for "MultiEURLEX" ============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: URL * Paper: URL * Data: URL * Leaderboard: N/A * Point of Contact: Ilias Chalkidis ### Dataset Summary Documents MultiEURLEX comprises 65k EU laws in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a *label descriptor*, e.g., [60, agri-foodstuffs], [6006, plant product], [1115, fruit]. The descriptors are also available in the 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EUR-LEX, comprising 57k EU laws with the originally assigned gold labels. Multi-granular Labeling EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8. We created three alternative sets of labels per document, by replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively. Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment. Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3. Data Split and Concept Drift MultiEURLEX is *chronologically* split in training (55k, 1958-2010), development (5k, 2010-2012), test (5k, 2012-2016) subsets, using the English documents. The test subset contains the same 5k documents in all 23 languages. The development subset also contains the same 5k documents in 23 languages, except Croatian. Croatia is the most recent EU member (2013); older laws are gradually translated. For the official languages of the seven oldest member countries, the same 55k training documents are available; for the other languages, only a subset of the 55k training documents is available. Compared to EUR-LEX (Chalkidis et al., 2019), MultiEURLEX is not only larger (8k more documents) and multilingual; it is also more challenging, as the chronological split leads to temporal real-world *concept drift* across the training, development, test subsets, i.e., differences in label distribution and phrasing, representing a realistic *temporal generalization* problem (Huang et al., 2019; Lazaridou et al., 2021). Recently, Søgaard et al. (2021) showed this setup is more realistic, as it does not over-estimate real performance, contrary to random splits (Gorman and Bedrick, 2019). ### Supported Tasks and Leaderboards Similarly to EUR-LEX (Chalkidis et al., 2019), MultiEURLEX can be used for legal topic classification, a multi-label classification task where legal documents need to be assigned concepts (in our case, from EUROVOC) reflecting their topics. Unlike EUR-LEX, however, MultiEURLEX supports labels from three different granularities (EUROVOC levels). More importantly, apart from monolingual (*one-to-one*) experiments, it can be used to study cross-lingual transfer scenarios, including *one-to-many* (systems trained in one language and used in other languages with no training data), and *many-to-one* or *many-to-many* (systems jointly trained in multiple languages and used in one or more other languages). The dataset is not yet part of an established benchmark. ### Languages The EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at URL This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them. Dataset Structure ----------------- ### Data Instances Multilingual use of the dataset When the dataset is used in a multilingual setting selecting the the 'all\_languages' flag: Monolingual use of the dataset When the dataset is used in a monolingual setting selecting the ISO language code for one of the 23 supported languages. For example: ### Data Fields Multilingual use of the dataset The following data fields are provided for documents ('train', 'dev', 'test'): 'celex\_id': (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR. 'text': (dict[str]) A dictionary with the 23 languages as keys and the full content of each document as values. 'labels': (List[int]) The relevant EUROVOC concepts (labels). Monolingual use of the dataset The following data fields are provided for documents ('train', 'dev', 'test'): 'celex\_id': (str) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR. 'text': (str) The full content of each document across languages. 'labels': (List[int]) The relevant EUROVOC concepts (labels). If you want to use the descriptors of the EUROVOC concepts, similar to Chalkidis et al. (2020), please download the relevant JSON file here. Then you may load it and use it: ### Data Splits [1] Native and Total EU speakers percentage (%) [2] Training / Development / Test Splits Dataset Creation ---------------- ### Curation Rationale The dataset was curated by Chalkidis et al. (2021). The documents have been annotated by the Publications Office of EU (URL ### Source Data #### Initial Data Collection and Normalization The original data are available at the EUR-LEX portal (URL) in unprocessed formats (HTML, XML, RDF). The documents were downloaded from the EUR-LEX portal in HTML. The relevant EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (URL We stripped HTML mark-up to provide the documents in plain text format. We inferred the labels for EUROVOC levels 1--3, by backtracking the EUROVOC hierarchy branches, from the originally assigned labels to their ancestors in levels 1--3, respectively. #### Who are the source language producers? The EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at URL This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them. ### Annotations #### Annotation process All the documents of the dataset have been annotated by the Publications Office of EU (URL with multiple concepts from EUROVOC (URL EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8. We augmented the annotation with three alternative sets of labels per document, replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively. Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment.Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3. #### Who are the annotators? Publications Office of EU (URL ### Personal and Sensitive Information The dataset contains publicly available EU laws that do not include personal or sensitive information with the exception of trivial information presented by consent, e.g., the names of the current presidents of the European Parliament and European Council, and other administration bodies. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). This does not imply that no other languages are spoken in EU countries, although EU laws are not translated to other languages (URL Additional Information ---------------------- ### Dataset Curators Chalkidis et al. (2021) ### Licensing Information We provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0): © European Union, 1998-2021 The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes. The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: URL Read more: URL *Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos.* *MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer.* *Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic. 2021* ### Contributions Thanks to @iliaschalkidis for adding this dataset.

38cb206959a2cca3aa49858914fba76258a3dcaf

# Dataset Card for Multi-News ## 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://github.com/Alex-Fabbri/Multi-News](https://github.com/Alex-Fabbri/Multi-News) - **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:** 256.96 MB - **Size of the generated dataset:** 700.18 MB - **Total amount of disk used:** 957.14 MB ### Dataset Summary Multi-News, consists of news articles and human-written summaries of these articles from the site newser.com. Each summary is professionally written by editors and includes links to the original articles cited. There are two features: - document: text of news articles seperated by special token "|||||". - summary: news 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:** 256.96 MB - **Size of the generated dataset:** 700.18 MB - **Total amount of disk used:** 957.14 MB An example of 'validation' looks as follows. ``` { "document": "some line val \n another line", "summary": "target val line" } ``` ### Data Fields The data fields are the same among all splits. #### default - `document`: a `string` feature. - `summary`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|44972| 5622|5622| ## 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 ``` This Dataset Usage Agreement ("Agreement") is a legal agreement with LILY LAB for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions. The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset. By sharing content with m, such as by submitting content to this site or by corresponding with LILY LAB contributors, the Researcher grants LILY LAB the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate LILY LAB to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by LILY LAB without obligation or restriction of any kind. The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless m, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations. THE DATASET IS PROVIDED "AS IS." LILY LAB DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, LILY LAB DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL LILY LAB BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY. This Agreement is effective until terminated. LILY LAB reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession. This Agreement is governed by the laws of the SOME_PLACE, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected. This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter. ``` ### Citation Information ``` @misc{alex2019multinews, title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model}, author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev}, year={2019}, eprint={1906.01749}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

multi_news

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "paperswithcode_id": "multi-news", "pretty_name": "Multi-News", "dataset_info": {"features": [{"name": "document", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 558392265, "num_examples": 44972}, {"name": "validation", "num_bytes": 68272432, "num_examples": 5622}, {"name": "test", "num_bytes": 70032124, "num_examples": 5622}], "download_size": 756785627, "dataset_size": 696696821}, "train-eval-index": [{"config": "default", "task": "summarization", "task_id": "summarization", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"document": "text", "summary": "target"}, "metrics": [{"type": "rouge", "name": "Rouge"}]}]}

2024-01-18T11:09:43+00:00

TAGS #task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #arxiv-1906.01749 #region-us

Dataset Card for Multi-News =========================== Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 256.96 MB * Size of the generated dataset: 700.18 MB * Total amount of disk used: 957.14 MB ### Dataset Summary Multi-News, consists of news articles and human-written summaries of these articles from the site URL. Each summary is professionally written by editors and includes links to the original articles cited. There are two features: * document: text of news articles seperated by special token "|||||". * summary: news summary. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 256.96 MB * Size of the generated dataset: 700.18 MB * Total amount of disk used: 957.14 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'document': a 'string' feature. * 'summary': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patrickvonplaten, @lewtun, @thomwolf for adding this dataset.

da70db2af9d09693783c3320c4249840212ee221

# Dataset Card for Multi-Genre Natural Language Inference (MultiNLI) ## 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.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/) - **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:** 226.85 MB - **Size of the generated dataset:** 76.95 MB - **Total amount of disk used:** 303.81 MB ### Dataset Summary The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. The corpus served as the basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen. ### 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 contains samples in English only. ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 226.85 MB - **Size of the generated dataset:** 76.95 MB - **Total amount of disk used:** 303.81 MB Example of a data instance: ``` { "promptID": 31193, "pairID": "31193n", "premise": "Conceptually cream skimming has two basic dimensions - product and geography.", "premise_binary_parse": "( ( Conceptually ( cream skimming ) ) ( ( has ( ( ( two ( basic dimensions ) ) - ) ( ( product and ) geography ) ) ) . ) )", "premise_parse": "(ROOT (S (NP (JJ Conceptually) (NN cream) (NN skimming)) (VP (VBZ has) (NP (NP (CD two) (JJ basic) (NNS dimensions)) (: -) (NP (NN product) (CC and) (NN geography)))) (. .)))", "hypothesis": "Product and geography are what make cream skimming work. ", "hypothesis_binary_parse": "( ( ( Product and ) geography ) ( ( are ( what ( make ( cream ( skimming work ) ) ) ) ) . ) )", "hypothesis_parse": "(ROOT (S (NP (NN Product) (CC and) (NN geography)) (VP (VBP are) (SBAR (WHNP (WP what)) (S (VP (VBP make) (NP (NP (NN cream)) (VP (VBG skimming) (NP (NN work)))))))) (. .)))", "genre": "government", "label": 1 } ``` ### Data Fields The data fields are the same among all splits. - `promptID`: Unique identifier for prompt - `pairID`: Unique identifier for pair - `{premise,hypothesis}`: combination of `premise` and `hypothesis` - `{premise,hypothesis} parse`: Each sentence as parsed by the Stanford PCFG Parser 3.5.2 - `{premise,hypothesis} binary parse`: parses in unlabeled binary-branching format - `genre`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`. ### Data Splits |train |validation_matched|validation_mismatched| |-----:|-----------------:|--------------------:| |392702| 9815| 9832| ## Dataset Creation ### Curation Rationale They constructed MultiNLI so as to make it possible to explicitly evaluate models both on the quality of their sentence representations within the training domain and on their ability to derive reasonable representations in unfamiliar domains. ### Source Data #### Initial Data Collection and Normalization They created each sentence pair by selecting a premise sentence from a preexisting text source and asked a human annotator to compose a novel sentence to pair with it as a hypothesis. #### 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The majority of the corpus is released under the OANC’s license, which allows all content to be freely used, modified, and shared under permissive terms. The data in the FICTION section falls under several permissive licenses; Seven Swords is available under a Creative Commons Share-Alike 3.0 Unported License, and with the explicit permission of the author, Living History and Password Incorrect are available under Creative Commons Attribution 3.0 Unported Licenses; the remaining works of fiction are in the public domain in the United States (but may be licensed differently elsewhere). ### Citation Information ``` @InProceedings{N18-1101, author = "Williams, Adina and Nangia, Nikita and Bowman, Samuel", title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference", booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", year = "2018", publisher = "Association for Computational Linguistics", pages = "1112--1122", location = "New Orleans, Louisiana", url = "http://aclweb.org/anthology/N18-1101" } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

multi_nli

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["en"], "license": ["cc-by-3.0", "cc-by-sa-3.0", "mit", "other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"], "paperswithcode_id": "multinli", "pretty_name": "Multi-Genre Natural Language Inference", "license_details": "Open Portion of the American National Corpus", "dataset_info": {"features": [{"name": "promptID", "dtype": "int32"}, {"name": "pairID", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "premise_binary_parse", "dtype": "string"}, {"name": "premise_parse", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "hypothesis_binary_parse", "dtype": "string"}, {"name": "hypothesis_parse", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 410210306, "num_examples": 392702}, {"name": "validation_matched", "num_bytes": 10063907, "num_examples": 9815}, {"name": "validation_mismatched", "num_bytes": 10610189, "num_examples": 9832}], "download_size": 224005223, "dataset_size": 430884402}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation_matched", "path": "data/validation_matched-*"}, {"split": "validation_mismatched", "path": "data/validation_mismatched-*"}]}]}

2024-01-04T16:06:27+00:00

TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-3.0 #license-cc-by-sa-3.0 #license-mit #license-other #region-us

Dataset Card for Multi-Genre Natural Language Inference (MultiNLI) ================================================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 226.85 MB * Size of the generated dataset: 76.95 MB * Total amount of disk used: 303.81 MB ### Dataset Summary The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. The corpus served as the basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen. ### Supported Tasks and Leaderboards ### Languages The dataset contains samples in English only. Dataset Structure ----------------- ### Data Instances * Size of downloaded dataset files: 226.85 MB * Size of the generated dataset: 76.95 MB * Total amount of disk used: 303.81 MB Example of a data instance: ### Data Fields The data fields are the same among all splits. * 'promptID': Unique identifier for prompt * 'pairID': Unique identifier for pair * '{premise,hypothesis}': combination of 'premise' and 'hypothesis' * '{premise,hypothesis} parse': Each sentence as parsed by the Stanford PCFG Parser 3.5.2 * '{premise,hypothesis} binary parse': parses in unlabeled binary-branching format * 'genre': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using 'URL'. ### Data Splits Dataset Creation ---------------- ### Curation Rationale They constructed MultiNLI so as to make it possible to explicitly evaluate models both on the quality of their sentence representations within the training domain and on their ability to derive reasonable representations in unfamiliar domains. ### Source Data #### Initial Data Collection and Normalization They created each sentence pair by selecting a premise sentence from a preexisting text source and asked a human annotator to compose a novel sentence to pair with it as a hypothesis. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The majority of the corpus is released under the OANC’s license, which allows all content to be freely used, modified, and shared under permissive terms. The data in the FICTION section falls under several permissive licenses; Seven Swords is available under a Creative Commons Share-Alike 3.0 Unported License, and with the explicit permission of the author, Living History and Password Incorrect are available under Creative Commons Attribution 3.0 Unported Licenses; the remaining works of fiction are in the public domain in the United States (but may be licensed differently elsewhere). ### Contributions Thanks to @bhavitvyamalik, @patrickvonplaten, @thomwolf, @mariamabarham for adding this dataset.

d9138f7ae27ea422c6a723432c4846d974e932a5

# Dataset Card for Multi-Genre Natural Language Inference (Mismatched only) ## 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.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/) - **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:** 226.85 MB - **Size of the generated dataset:** 77.62 MB - **Total amount of disk used:** 304.46 MB ### Dataset Summary The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. The corpus served as the basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen. ### 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:** 226.85 MB - **Size of the generated dataset:** 77.62 MB - **Total amount of disk used:** 304.46 MB An example of 'train' looks as follows. ``` { "hypothesis": "independence", "label": "contradiction", "premise": "correlation" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a `string` feature. ### Data Splits | name |train |validation| |----------|-----:|---------:| |plain_text|392702| 10000| ## 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{N18-1101, author = "Williams, Adina and Nangia, Nikita and Bowman, Samuel", title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference", booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", year = "2018", publisher = "Association for Computational Linguistics", pages = "1112--1122", location = "New Orleans, Louisiana", url = "http://aclweb.org/anthology/N18-1101" } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

multi_nli_mismatch

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["en"], "license": ["cc-by-3.0", "cc-by-sa-3.0", "mit", "other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"], "paperswithcode_id": "multinli", "pretty_name": "Multi-Genre Natural Language Inference", "license_details": "Open Portion of the American National Corpus", "dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 75601459, "num_examples": 392702}, {"name": "validation", "num_bytes": 2009444, "num_examples": 10000}], "download_size": 226850426, "dataset_size": 77610903}}

2024-01-18T11:09:45+00:00

TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-3.0 #license-cc-by-sa-3.0 #license-mit #license-other #region-us

Dataset Card for Multi-Genre Natural Language Inference (Mismatched only) ========================================================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 226.85 MB * Size of the generated dataset: 77.62 MB * Total amount of disk used: 304.46 MB ### Dataset Summary The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. The corpus served as the basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### plain\_text * Size of downloaded dataset files: 226.85 MB * Size of the generated dataset: 77.62 MB * Total amount of disk used: 304.46 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### plain\_text * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset.

d5c4579a946efc94daa84eba37a1bd27ad3875b0

# Dataset Card for MultiParaCrawl ## 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/MultiParaCrawl.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **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/MultiParaCrawl.php E.g. `dataset = load_dataset("multi_para_crawl", lang1="en", lang2="nl")` ### 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.

multi_para_crawl

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["bg", "ca", "cs", "da", "de", "el", "es", "et", "eu", "fi", "fr", "ga", "gl", "ha", "hr", "hu", "ig", "is", "it", "km", "lt", "lv", "mt", "my", "nb", "ne", "nl", "nn", "pl", "ps", "pt", "ro", "ru", "si", "sk", "sl", "so", "sv", "sw", "tl"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "MultiParaCrawl", "dataset_info": [{"config_name": "cs-is", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["cs", "is"]}}}], "splits": [{"name": "train", "num_bytes": 148967967, "num_examples": 691006}], "download_size": 61609317, "dataset_size": 148967967}, {"config_name": "ga-sk", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["ga", "sk"]}}}], "splits": [{"name": "train", "num_bytes": 92802332, "num_examples": 390327}], "download_size": 39574554, "dataset_size": 92802332}, {"config_name": "lv-mt", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["lv", "mt"]}}}], "splits": [{"name": "train", "num_bytes": 116533998, "num_examples": 464160}], "download_size": 49770574, "dataset_size": 116533998}, {"config_name": "nb-ru", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["nb", "ru"]}}}], "splits": [{"name": "train", "num_bytes": 116899303, "num_examples": 399050}], "download_size": 40932849, "dataset_size": 116899303}, {"config_name": "de-tl", "features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["de", "tl"]}}}], "splits": [{"name": "train", "num_bytes": 30880849, "num_examples": 98156}], "download_size": 12116471, "dataset_size": 30880849}]}

2024-01-18T11:09:47+00:00

TAGS #task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Bulgarian #language-Catalan #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-Spanish #language-Estonian #language-Basque #language-Finnish #language-French #language-Irish #language-Galician #language-Hausa #language-Croatian #language-Hungarian #language-Igbo #language-Icelandic #language-Italian #language-Khmer #language-Lithuanian #language-Latvian #language-Maltese #language-Burmese #language-Norwegian Bokmål #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Sinhala #language-Slovak #language-Slovenian #language-Somali #language-Swedish #language-Swahili (macrolanguage) #language-Tagalog #license-cc0-1.0 #region-us

# Dataset Card for MultiParaCrawl ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: None - Paper: URL - Leaderboard: - Point of Contact: ### 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: URL E.g. 'dataset = load_dataset("multi_para_crawl", lang1="en", lang2="nl")' ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @abhishekkrthakur for adding this dataset.

a0eb66b078fd4b269a19503dc0dc8b073350eb9c

# Dataset Card for MultiReQA ## 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/MultiReQA - **Repository:** https://github.com/google-research-datasets/MultiReQA - **Paper:** https://arxiv.org/pdf/2005.02507.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation) ### Supported Tasks and Leaderboards - Question answering (QA) - Retrieval question answering (ReQA) ### Languages Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC ## Dataset Structure ### Data Instances The general format is: ` { "candidate_id": <candidate_id>, "response_start": <response_start>, "response_end": <response_end> } ... ` An example from SearchQA: `{'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1', 'response_end': 306, 'response_start': 243} ` ### Data Fields ` { "candidate_id": <STRING>, "response_start": <INT>, "response_end": <INT> } ... ` - **candidate_id:** The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task. - **response_start:** The start index of the sentence with respect to its original context. - **response_end:** The end index of the sentence with respect to its original context ### Data Splits Train and Dev splits are available only for the following datasets, - SearchQA - TriviaQA - HotpotQA - SQuAD - NaturalQuestions Test splits are available only for the following datasets, - BioASQ - RelationExtraction - TextbookQA The number of candidate sentences for each dataset in the table below. | | MultiReQA | | |--------------------|-----------|---------| | | train | test | | SearchQA | 629,160 | 454,836 | | TriviaQA | 335,659 | 238,339 | | HotpotQA | 104,973 | 52,191 | | SQuAD | 87,133 | 10,642 | | NaturalQuestions | 106,521 | 22,118 | | BioASQ | - | 14,158 | | RelationExtraction | - | 3,301 | | TextbookQA | - | 3,701 | ## Dataset Creation ### Curation Rationale MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the [MRQA shared task](https://mrqa.github.io/). The dataset was curated by converting existing QA datasets from [MRQA shared task](https://mrqa.github.io/) to the format of MultiReQA benchmark. ### Source Data #### Initial Data Collection and Normalization The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### 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 annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{m2020multireqa, title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models}, author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant}, year={2020}, eprint={2005.02507}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset.

multi_re_qa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "found"], "language_creators": ["expert-generated", "found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1K<n<10K", "1M<n<10M"], "source_datasets": ["extended|other-BioASQ", "extended|other-DuoRC", "extended|other-HotpotQA", "extended|other-Natural-Questions", "extended|other-Relation-Extraction", "extended|other-SQuAD", "extended|other-SearchQA", "extended|other-TextbookQA", "extended|other-TriviaQA"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "open-domain-qa"], "paperswithcode_id": "multireqa", "pretty_name": "MultiReQA", "config_names": ["BioASQ", "DuoRC", "HotpotQA", "NaturalQuestions", "RelationExtraction", "SQuAD", "SearchQA", "TextbookQA", "TriviaQA"], "dataset_info": [{"config_name": "SearchQA", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 183902877, "num_examples": 3163801}, {"name": "validation", "num_bytes": 26439174, "num_examples": 454836}], "download_size": 36991959, "dataset_size": 210342051}, {"config_name": "TriviaQA", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 107326326, "num_examples": 1893674}, {"name": "validation", "num_bytes": 13508062, "num_examples": 238339}], "download_size": 21750402, "dataset_size": 120834388}, {"config_name": "HotpotQA", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 29516866, "num_examples": 508879}, {"name": "validation", "num_bytes": 3027229, "num_examples": 52191}], "download_size": 6343389, "dataset_size": 32544095}, {"config_name": "SQuAD", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 16828974, "num_examples": 95659}, {"name": "validation", "num_bytes": 2012997, "num_examples": 10642}], "download_size": 3003646, "dataset_size": 18841971}, {"config_name": "NaturalQuestions", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 28732767, "num_examples": 448355}, {"name": "validation", "num_bytes": 1418124, "num_examples": 22118}], "download_size": 6124487, "dataset_size": 30150891}, {"config_name": "BioASQ", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "test", "num_bytes": 766190, "num_examples": 14158}], "download_size": 156649, "dataset_size": 766190}, {"config_name": "RelationExtraction", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "test", "num_bytes": 217870, "num_examples": 3301}], "download_size": 73019, "dataset_size": 217870}, {"config_name": "TextbookQA", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "test", "num_bytes": 4182675, "num_examples": 71147}], "download_size": 704602, "dataset_size": 4182675}, {"config_name": "DuoRC", "features": [{"name": "candidate_id", "dtype": "string"}, {"name": "response_start", "dtype": "int32"}, {"name": "response_end", "dtype": "int32"}], "splits": [{"name": "test", "num_bytes": 1483518, "num_examples": 5525}], "download_size": 97625, "dataset_size": 1483518}]}

2024-01-18T11:09:48+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #annotations_creators-found #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #source_datasets-extended|other-BioASQ #source_datasets-extended|other-DuoRC #source_datasets-extended|other-HotpotQA #source_datasets-extended|other-Natural-Questions #source_datasets-extended|other-Relation-Extraction #source_datasets-extended|other-SQuAD #source_datasets-extended|other-SearchQA #source_datasets-extended|other-TextbookQA #source_datasets-extended|other-TriviaQA #language-English #license-unknown #arxiv-2005.02507 #region-us

Dataset Card for MultiReQA ========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation) ### Supported Tasks and Leaderboards * Question answering (QA) * Retrieval question answering (ReQA) ### Languages Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC Dataset Structure ----------------- ### Data Instances The general format is: ' { "candidate\_id": <candidate\_id>, "response\_start": <response\_start>, "response\_end": <response\_end> } ... ' An example from SearchQA: '{'candidate\_id': 'SearchQA\_000077f3912049dfb4511db271697bad/\_0\_1', 'response\_end': 306, 'response\_start': 243} ' ### Data Fields ' { "candidate\_id": , "response\_start": , "response\_end": } ... ' * candidate\_id: The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task. * response\_start: The start index of the sentence with respect to its original context. * response\_end: The end index of the sentence with respect to its original context ### Data Splits Train and Dev splits are available only for the following datasets, * SearchQA * TriviaQA * HotpotQA * SQuAD * NaturalQuestions Test splits are available only for the following datasets, * BioASQ * RelationExtraction * TextbookQA The number of candidate sentences for each dataset in the table below. MultiReQA: MultiReQA: SearchQA MultiReQA: TriviaQA MultiReQA: HotpotQA MultiReQA: SQuAD MultiReQA: NaturalQuestions MultiReQA: BioASQ MultiReQA: RelationExtraction MultiReQA: TextbookQA Dataset Creation ---------------- ### Curation Rationale MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the MRQA shared task. The dataset was curated by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark. ### Source Data #### Initial Data Collection and Normalization The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? The annotators/curators of the dataset are mandyguo-xyguo and mwurts4google, the contributors of the official MultiReQA github repository ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The annotators/curators of the dataset are mandyguo-xyguo and mwurts4google, the contributors of the official MultiReQA github repository ### Licensing Information ### Contributions Thanks to @Karthik-Bhaskar for adding this dataset.

fd8c4a9484b1f47152fe23ddcc87289777d355fd

# Dataset Card for MultiWOZ ## 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:** [MultiWOZ 2.2 github repository](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2) - **Paper:** [MultiWOZ v2](https://arxiv.org/abs/1810.00278), and [MultiWOZ v2.2](https://www.aclweb.org/anthology/2020.nlp4convai-1.13.pdf) - **Point of Contact:** [Paweł Budzianowski]([email protected]) ### Dataset Summary Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fixes dialogue state annotation errors across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values (e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots. ### Supported Tasks and Leaderboards This dataset supports a range of task. - **Generative dialogue modeling** or `dialogue-modeling`: the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-[BLEU](https://huggingface.co/metrics/bleu), inform rate and request success. - **Intent state tracking**, a `multi-class-classification` task: predict the belief state of the user side of the conversation, performance is measured by [F1](https://huggingface.co/metrics/f1). - **Dialog act prediction**, a `parsing` task: parse an utterance into the corresponding dialog acts for the system to use. [F1](https://huggingface.co/metrics/f1) is typically reported. ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances A data instance is a full multi-turn dialogue between a `USER` and a `SYSTEM`. Each turn has a single utterance, e.g.: ``` ['What fun places can I visit in the East?', 'We have five spots which include boating, museums and entertainment. Any preferences that you have?'] ``` The utterances of the `USER` are also annotated with frames denoting their intent and believe state: ``` [{'service': ['attraction'], 'slots': [{'copy_from': [], 'copy_from_value': [], 'exclusive_end': [], 'slot': [], 'start': [], 'value': []}], 'state': [{'active_intent': 'find_attraction', 'requested_slots': [], 'slots_values': {'slots_values_list': [['east']], 'slots_values_name': ['attraction-area']}}]}, {'service': [], 'slots': [], 'state': []}] ``` Finally, each of the utterances is annotated with dialog acts which provide a structured representation of what the `USER` or `SYSTEM` is inquiring or giving information about. ``` [{'dialog_act': {'act_slots': [{'slot_name': ['east'], 'slot_value': ['area']}], 'act_type': ['Attraction-Inform']}, 'span_info': {'act_slot_name': ['area'], 'act_slot_value': ['east'], 'act_type': ['Attraction-Inform'], 'span_end': [39], 'span_start': [35]}}, {'dialog_act': {'act_slots': [{'slot_name': ['none'], 'slot_value': ['none']}, {'slot_name': ['boating', 'museums', 'entertainment', 'five'], 'slot_value': ['type', 'type', 'type', 'choice']}], 'act_type': ['Attraction-Select', 'Attraction-Inform']}, 'span_info': {'act_slot_name': ['type', 'type', 'type', 'choice'], 'act_slot_value': ['boating', 'museums', 'entertainment', 'five'], 'act_type': ['Attraction-Inform', 'Attraction-Inform', 'Attraction-Inform', 'Attraction-Inform'], 'span_end': [40, 49, 67, 12], 'span_start': [33, 42, 54, 8]}}] ``` ### Data Fields Each dialogue instance has the following fields: - `dialogue_id`: a unique ID identifying the dialog. The MUL and PMUL names refer to strictly multi domain dialogues (at least 2 main domains are involved) while the SNG, SSNG and WOZ names refer to single domain dialogues with potentially sub-domains like booking. - `services`: a list of services mentioned in the dialog, such as `train` or `hospitals`. - `turns`: the sequence of utterances with their annotations, including: - `turn_id`: a turn identifier, unique per dialog. - `speaker`: either the `USER` or `SYSTEM`. - `utterance`: the text of the utterance. - `dialogue_acts`: The structured parse of the utterance into dialog acts in the system's grammar - `act_type`: Such as e.g. `Attraction-Inform` to seek or provide information about an `attraction` - `act_slots`: provide more details about the action - `span_info`: maps these `act_slots` to the `utterance` text. - `frames`: only for `USER` utterances, track the user's belief state, i.e. a structured representation of what they are trying to achieve in the fialog. This decomposes into: - `service`: the service they are interested in - `state`: their belief state including their `active_intent` and further information expressed in `requested_slots` - `slots`: a mapping of the `requested_slots` to where they are mentioned in the text. It takes one of two forms, detailed next: The first type are span annotations that identify the location where slot values have been mentioned in the utterances for non-categorical slots. These span annotations are represented as follows: ``` { "slots": [ { "slot": String of slot name. "start": Int denoting the index of the starting character in the utterance corresponding to the slot value. "exclusive_end": Int denoting the index of the character just after the last character corresponding to the slot value in the utterance. In python, utterance[start:exclusive_end] gives the slot value. "value": String of value. It equals to utterance[start:exclusive_end], where utterance is the current utterance in string. } ] } ``` There are also some non-categorical slots whose values are carried over from another slot in the dialogue state. Their values don"t explicitly appear in the utterances. For example, a user utterance can be "I also need a taxi from the restaurant to the hotel.", in which the state values of "taxi-departure" and "taxi-destination" are respectively carried over from that of "restaurant-name" and "hotel-name". For these slots, instead of annotating them as spans, a "copy from" annotation identifies the slot it copies the value from. This annotation is formatted as follows, ``` { "slots": [ { "slot": Slot name string. "copy_from": The slot to copy from. "value": A list of slot values being . It corresponds to the state values of the "copy_from" slot. } ] } ``` ### Data Splits The dataset is split into a `train`, `validation`, and `test` split with the following sizes: | | train | validation | test | |---------------------|------:|-----------:|-----:| | Number of dialogues | 8438 | 1000 | 1000 | | Number of turns | 42190 | 5000 | 5000 | ## 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 The initial dataset (Versions 1.0 and 2.0) was created by a team of researchers from the [Cambridge Dialogue Systems Group](https://mi.eng.cam.ac.uk/research/dialogue/corpora/). Version 2.1 was developed on top of v2.0 by a team from Amazon, and v2.2 was developed by a team of Google researchers. ### Licensing Information The dataset is released under the Apache License 2.0. ### Citation Information You can cite the following for the various versions of MultiWOZ: Version 1.0 ``` @inproceedings{ramadan2018large, title={Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing}, author={Ramadan, Osman and Budzianowski, Pawe{\l} and Gasic, Milica}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics}, volume={2}, pages={432--437}, year={2018} } ``` Version 2.0 ``` @inproceedings{budzianowski2018large, Author = {Budzianowski, Pawe{\l} and Wen, Tsung-Hsien and Tseng, Bo-Hsiang and Casanueva, I{\~n}igo and Ultes Stefan and Ramadan Osman and Ga{\v{s}}i\'c, Milica}, title={MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling}, booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year={2018} } ``` Version 2.1 ``` @article{eric2019multiwoz, title={MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines}, author={Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek}, journal={arXiv preprint arXiv:1907.01669}, year={2019} } ``` Version 2.2 ``` @inproceedings{zang2020multiwoz, title={MultiWOZ 2.2: A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines}, author={Zang, Xiaoxue and Rastogi, Abhinav and Sunkara, Srinivas and Gupta, Raghav and Zhang, Jianguo and Chen, Jindong}, booktitle={Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020}, pages={109--117}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.

multi_woz_v22

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced", "machine-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask", "token-classification", "text-classification"], "task_ids": ["dialogue-modeling", "multi-class-classification", "parsing"], "paperswithcode_id": "multiwoz", "pretty_name": "Multi-domain Wizard-of-Oz", "dataset_info": [{"config_name": "v2.2", "features": [{"name": "dialogue_id", "dtype": "string"}, {"name": "services", "sequence": "string"}, {"name": "turns", "sequence": [{"name": "turn_id", "dtype": "string"}, {"name": "speaker", "dtype": {"class_label": {"names": {"0": "USER", "1": "SYSTEM"}}}}, {"name": "utterance", "dtype": "string"}, {"name": "frames", "sequence": [{"name": "service", "dtype": "string"}, {"name": "state", "struct": [{"name": "active_intent", "dtype": "string"}, {"name": "requested_slots", "sequence": "string"}, {"name": "slots_values", "sequence": [{"name": "slots_values_name", "dtype": "string"}, {"name": "slots_values_list", "sequence": "string"}]}]}, {"name": "slots", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "exclusive_end", "dtype": "int32"}, {"name": "copy_from", "dtype": "string"}, {"name": "copy_from_value", "sequence": "string"}]}]}, {"name": "dialogue_acts", "struct": [{"name": "dialog_act", "sequence": [{"name": "act_type", "dtype": "string"}, {"name": "act_slots", "sequence": [{"name": "slot_name", "dtype": "string"}, {"name": "slot_value", "dtype": "string"}]}]}, {"name": "span_info", "sequence": [{"name": "act_type", "dtype": "string"}, {"name": "act_slot_name", "dtype": "string"}, {"name": "act_slot_value", "dtype": "string"}, {"name": "span_start", "dtype": "int32"}, {"name": "span_end", "dtype": "int32"}]}]}]}], "splits": [{"name": "train", "num_bytes": 68222649, "num_examples": 8437}, {"name": "validation", "num_bytes": 8990945, "num_examples": 1000}, {"name": "test", "num_bytes": 9027095, "num_examples": 1000}], "download_size": 276592909, "dataset_size": 86240689}, {"config_name": "v2.2_active_only", "features": [{"name": "dialogue_id", "dtype": "string"}, {"name": "services", "sequence": "string"}, {"name": "turns", "sequence": [{"name": "turn_id", "dtype": "string"}, {"name": "speaker", "dtype": {"class_label": {"names": {"0": "USER", "1": "SYSTEM"}}}}, {"name": "utterance", "dtype": "string"}, {"name": "frames", "sequence": [{"name": "service", "dtype": "string"}, {"name": "state", "struct": [{"name": "active_intent", "dtype": "string"}, {"name": "requested_slots", "sequence": "string"}, {"name": "slots_values", "sequence": [{"name": "slots_values_name", "dtype": "string"}, {"name": "slots_values_list", "sequence": "string"}]}]}, {"name": "slots", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "exclusive_end", "dtype": "int32"}, {"name": "copy_from", "dtype": "string"}, {"name": "copy_from_value", "sequence": "string"}]}]}, {"name": "dialogue_acts", "struct": [{"name": "dialog_act", "sequence": [{"name": "act_type", "dtype": "string"}, {"name": "act_slots", "sequence": [{"name": "slot_name", "dtype": "string"}, {"name": "slot_value", "dtype": "string"}]}]}, {"name": "span_info", "sequence": [{"name": "act_type", "dtype": "string"}, {"name": "act_slot_name", "dtype": "string"}, {"name": "act_slot_value", "dtype": "string"}, {"name": "span_start", "dtype": "int32"}, {"name": "span_end", "dtype": "int32"}]}]}]}], "splits": [{"name": "train", "num_bytes": 40937577, "num_examples": 8437}, {"name": "validation", "num_bytes": 5377939, "num_examples": 1000}, {"name": "test", "num_bytes": 5410819, "num_examples": 1000}], "download_size": 276592909, "dataset_size": 51726335}]}

2024-01-18T11:09:50+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_categories-token-classification #task_categories-text-classification #task_ids-dialogue-modeling #task_ids-multi-class-classification #task_ids-parsing #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #arxiv-1810.00278 #region-us

Dataset Card for MultiWOZ ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: MultiWOZ 2.2 github repository * Paper: MultiWOZ v2, and MultiWOZ v2.2 * Point of Contact: Paweł Budzianowski ### Dataset Summary Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fixes dialogue state annotation errors across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values (e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots. ### Supported Tasks and Leaderboards This dataset supports a range of task. * Generative dialogue modeling or 'dialogue-modeling': the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-BLEU, inform rate and request success. * Intent state tracking, a 'multi-class-classification' task: predict the belief state of the user side of the conversation, performance is measured by F1. * Dialog act prediction, a 'parsing' task: parse an utterance into the corresponding dialog acts for the system to use. F1 is typically reported. ### Languages The text in the dataset is in English ('en'). Dataset Structure ----------------- ### Data Instances A data instance is a full multi-turn dialogue between a 'USER' and a 'SYSTEM'. Each turn has a single utterance, e.g.: The utterances of the 'USER' are also annotated with frames denoting their intent and believe state: Finally, each of the utterances is annotated with dialog acts which provide a structured representation of what the 'USER' or 'SYSTEM' is inquiring or giving information about. ### Data Fields Each dialogue instance has the following fields: * 'dialogue\_id': a unique ID identifying the dialog. The MUL and PMUL names refer to strictly multi domain dialogues (at least 2 main domains are involved) while the SNG, SSNG and WOZ names refer to single domain dialogues with potentially sub-domains like booking. * 'services': a list of services mentioned in the dialog, such as 'train' or 'hospitals'. * 'turns': the sequence of utterances with their annotations, including: + 'turn\_id': a turn identifier, unique per dialog. + 'speaker': either the 'USER' or 'SYSTEM'. + 'utterance': the text of the utterance. + 'dialogue\_acts': The structured parse of the utterance into dialog acts in the system's grammar - 'act\_type': Such as e.g. 'Attraction-Inform' to seek or provide information about an 'attraction' - 'act\_slots': provide more details about the action - 'span\_info': maps these 'act\_slots' to the 'utterance' text. + 'frames': only for 'USER' utterances, track the user's belief state, i.e. a structured representation of what they are trying to achieve in the fialog. This decomposes into: - 'service': the service they are interested in - 'state': their belief state including their 'active\_intent' and further information expressed in 'requested\_slots' - 'slots': a mapping of the 'requested\_slots' to where they are mentioned in the text. It takes one of two forms, detailed next: The first type are span annotations that identify the location where slot values have been mentioned in the utterances for non-categorical slots. These span annotations are represented as follows: There are also some non-categorical slots whose values are carried over from another slot in the dialogue state. Their values don"t explicitly appear in the utterances. For example, a user utterance can be "I also need a taxi from the restaurant to the hotel.", in which the state values of "taxi-departure" and "taxi-destination" are respectively carried over from that of "restaurant-name" and "hotel-name". For these slots, instead of annotating them as spans, a "copy from" annotation identifies the slot it copies the value from. This annotation is formatted as follows, ### Data Splits The dataset is split into a 'train', 'validation', and 'test' split with the following sizes: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The initial dataset (Versions 1.0 and 2.0) was created by a team of researchers from the Cambridge Dialogue Systems Group. Version 2.1 was developed on top of v2.0 by a team from Amazon, and v2.2 was developed by a team of Google researchers. ### Licensing Information The dataset is released under the Apache License 2.0. You can cite the following for the various versions of MultiWOZ: Version 1.0 Version 2.0 Version 2.1 Version 2.2 ### Contributions Thanks to @yjernite for adding this dataset.

4dc551810809f0dd77b099b511c580dc3dea293f

# Dataset Card for Multi-XScience ## 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:** [Multi-XScience repository](https://github.com/yaolu/Multi-XScience) - **Paper:** [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235) ### Dataset Summary Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English ## Dataset Structure ### Data Instances {'abstract': 'Author(s): Kuperberg, Greg; Thurston, Dylan P. | Abstract: We give a purely topological definition of the perturbative quantum invariants of links and 3-manifolds associated with Chern-Simons field theory. Our definition is as close as possible to one given by Kontsevich. We will also establish some basic properties of these invariants, in particular that they are universally finite type with respect to algebraically split surgery and with respect to Torelli surgery. Torelli surgery is a mutual generalization of blink surgery of Garoufalidis and Levine and clasper surgery of Habiro.', 'aid': 'math9912167', 'mid': '1631980677', 'ref_abstract': {'abstract': ['This note is a sequel to our earlier paper of the same title [4] and describes invariants of rational homology 3-spheres associated to acyclic orthogonal local systems. Our work is in the spirit of the Axelrod–Singer papers [1], generalizes some of their results, and furnishes a new setting for the purely topological implications of their work.', 'Recently, Mullins calculated the Casson-Walker invariant of the 2-fold cyclic branched cover of an oriented link in S^3 in terms of its Jones polynomial and its signature, under the assumption that the 2-fold branched cover is a rational homology 3-sphere. Using elementary principles, we provide a similar calculation for the general case. In addition, we calculate the LMO invariant of the p-fold branched cover of twisted knots in S^3 in terms of the Kontsevich integral of the knot.'], 'cite_N': ['@cite_16', '@cite_26'], 'mid': ['1481005306', '1641082372']}, 'related_work': 'Two other generalizations that can be considered are invariants of graphs in 3-manifolds, and invariants associated to other flat connections @cite_16 . We will analyze these in future work. Among other things, there should be a general relation between flat bundles and links in 3-manifolds on the one hand and finite covers and branched covers on the other hand @cite_26 .'} ### Data Fields {`abstract`: text of paper abstract \ `aid`: arxiv id \ `mid`: microsoft academic graph id \ `ref_abstract`: \ { \ `abstract`: text of reference paper (cite_N) abstract \ `cite_N`: special cite symbol, \ `mid`: reference paper's (cite_N) microsoft academic graph id \ }, \ `related_work`: text of paper related work \ } ### Data Splits The data is split into a training, validation and test. | train | validation | test | |------:|-----------:|-----:| | 30369 | 5066 | 5093 | ## 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{lu2020multi, title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles}, author={Lu, Yao and Dong, Yue and Charlin, Laurent}, journal={arXiv preprint arXiv:2010.14235}, year={2020} } ``` ### Contributions Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset.

multi_x_science_sum

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "paperswithcode_id": "multi-xscience", "pretty_name": "Multi-XScience", "tags": ["paper-abstract-generation"], "dataset_info": {"features": [{"name": "aid", "dtype": "string"}, {"name": "mid", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "related_work", "dtype": "string"}, {"name": "ref_abstract", "sequence": [{"name": "cite_N", "dtype": "string"}, {"name": "mid", "dtype": "string"}, {"name": "abstract", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 169364465, "num_examples": 30369}, {"name": "test", "num_bytes": 27965523, "num_examples": 5093}, {"name": "validation", "num_bytes": 28168498, "num_examples": 5066}], "download_size": 61329304, "dataset_size": 225498486}}

2024-01-18T11:09:52+00:00

TAGS #task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #paper-abstract-generation #arxiv-2010.14235 #region-us

Dataset Card for Multi-XScience =============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: Multi-XScience repository * Paper: Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles ### Dataset Summary Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. ### Supported Tasks and Leaderboards ### Languages The text in the dataset is in English Dataset Structure ----------------- ### Data Instances {'abstract': 'Author(s): Kuperberg, Greg; Thurston, Dylan P. | Abstract: We give a purely topological definition of the perturbative quantum invariants of links and 3-manifolds associated with Chern-Simons field theory. Our definition is as close as possible to one given by Kontsevich. We will also establish some basic properties of these invariants, in particular that they are universally finite type with respect to algebraically split surgery and with respect to Torelli surgery. Torelli surgery is a mutual generalization of blink surgery of Garoufalidis and Levine and clasper surgery of Habiro.', 'aid': 'math9912167', 'mid': '1631980677', 'ref\_abstract': {'abstract': ['This note is a sequel to our earlier paper of the same title [4] and describes invariants of rational homology 3-spheres associated to acyclic orthogonal local systems. Our work is in the spirit of the Axelrod–Singer papers [1], generalizes some of their results, and furnishes a new setting for the purely topological implications of their work.', 'Recently, Mullins calculated the Casson-Walker invariant of the 2-fold cyclic branched cover of an oriented link in S^3 in terms of its Jones polynomial and its signature, under the assumption that the 2-fold branched cover is a rational homology 3-sphere. Using elementary principles, we provide a similar calculation for the general case. In addition, we calculate the LMO invariant of the p-fold branched cover of twisted knots in S^3 in terms of the Kontsevich integral of the knot.'], 'cite\_N': ['@cite\_16', '@cite\_26'], 'mid': ['1481005306', '1641082372']}, 'related\_work': 'Two other generalizations that can be considered are invariants of graphs in 3-manifolds, and invariants associated to other flat connections @cite\_16 . We will analyze these in future work. Among other things, there should be a general relation between flat bundles and links in 3-manifolds on the one hand and finite covers and branched covers on the other hand @cite\_26 .'} ### Data Fields {'abstract': text of paper abstract 'aid': arxiv id 'mid': microsoft academic graph id 'ref\_abstract': { 'abstract': text of reference paper (cite\_N) abstract 'cite\_N': special cite symbol, 'mid': reference paper's (cite\_N) microsoft academic graph id }, 'related\_work': text of paper related work } ### Data Splits The data is split into a training, validation and test. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @moussaKam for adding this dataset.

1108a969d076f04c7367f0c2427d1c5d6d6bdaa0

# Dataset Card for MultiDoc2Dial ## 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://doc2dial.github.io/multidoc2dial/ - **Repository:** https://github.com/IBM/multidoc2dial - **Paper:** https://arxiv.org/pdf/2109.12595.pdf - **Leaderboard:** - **Point of Contact:** [email protected] ### Dataset Summary MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. ### Supported Tasks and Leaderboards > Supported Task: Open domain question answering, document-grounded dialogue, passage retrieval > Leaderboard: ### Languages English ## Dataset Structure ### Data Instances Sample data instance for `multidoc2dial` : ``` { "id": "8df07b7a98990db27c395cb1f68a962e_1", "title": "Top 5 DMV Mistakes and How to Avoid Them#3_0", "context": "Many DMV customers make easily avoidable mistakes that cause them significant problems, including encounters with law enforcement and impounded vehicles. Because we see customers make these mistakes over and over again , we are issuing this list of the top five DMV mistakes and how to avoid them. \n\n1. Forgetting to Update Address \nBy statute , you must report a change of address to DMV within ten days of moving. That is the case for the address associated with your license, as well as all the addresses associated with each registered vehicle, which may differ. It is not sufficient to only: write your new address on the back of your old license; tell the United States Postal Service; or inform the police officer writing you a ticket. If you fail to keep your address current , you will miss a suspension order and may be charged with operating an unregistered vehicle and/or aggravated unlicensed operation, both misdemeanors. This really happens , but the good news is this is a problem that is easily avoidable. Learn more about how to change the address on your license and registrations [1 ] \n\n2. Leaving the State Without Notifying DMV \nStates communicate with each other , so when you move to another state, be sure to tie up any loose ends regarding your New York State license or registration. That means resolving any unanswered tickets, suspensions or revocations, and surrendering your license plates to NYS when you get to your new home state. A license suspension or revocation here could mean that your new home state will not issue you a license there. Remember , it is important to notify DMV of your new address so that any possible mail correspondence can reach you. Also , turning in your plates is important to avoid an insurance lapse. \n\n3. Letting Insurance Lapse \nBecause we all pay indirectly for crashes involving uninsured motorists , New York State requires every motorist to maintain auto insurance every single day a vehicle is registered. DMV works with insurance companies to electronically monitor your insurance coverage , and we know when coverage is dropped for any reason. When that happens , we mail you an insurance inquiry letter to allow you to clear up the problem. We send 500,000 inquiry letters a year. If the inquiry letter does not resolve the problem , we must suspend the vehicle registration and , if it persists, your driver license!We suspend 300,000 registrations a year for failure to maintain insurance. If you fail to maintain an updated address with us , you won t learn that you have an insurance problem , and we will suspend your registration and license. Make sure you turn in your vehicle s license plates at DMV before you cancel your insurance policy. Insurance policies must be from a company licensed in New York State. Learn more about Insurances Lapes [2] and How to Surrender your Plates [3 ] \n\n4. Understanding how Much Traffic Points Cost \nDMV maintains a point system to track dangerous drivers. Often , motorists convicted of a traffic ticket feel they have resolved all their motoring issues with the local court, but later learn that the Driver Responsibility Assessment DRA is a separate DMV charge based on the total points they accumulate. The $300 DRA fee can be paid in $100 annual installments over three years. Motorists who fail to maintain an updated address with DMV may resolve their tickets with the court, but never receive their DRA assessment because we do not have their new address on record. Failure to pay the DRA will result in a suspended license. Learn more about About the NYS Driver Point System [4] and how to Pay Driver Responsibility Assessment [5 ] \n\n5. Not Bringing Proper Documentation to DMV Office \nAbout ten percent of customers visiting a DMV office do not bring what they need to complete their transaction, and have to come back a second time to finish their business. This can be as simple as not bringing sufficient funds to pay for a license renewal or not having the proof of auto insurance required to register a car. Better yet , don t visit a DMV office at all, and see if your transaction can be performed online, like an address change, registration renewal, license renewal, replacing a lost title, paying a DRA or scheduling a road test. Our award - winning website is recognized as one of the best in the nation. It has all the answers you need to efficiently perform any DMV transaction. Consider signing up for our MyDMV service, which offers even more benefits. Sign up or log into MyDMV [6 ] ", "question": "Hello, I forgot o update my address, can you help me with that?[SEP]", "da": "query_condition", "answers": { "text": ['you must report a change of address to DMV within ten days of moving. That is the case for the address associated with your license, as well as all the addresses associated with each registered vehicle, which may differ. "], "answer_start": [346] }, "utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles.", "domain": "dmv" } ``` Sample data instance for `document_domain` : ``` { "domain": "ssa", "doc_id": "Benefits Planner: Survivors | Planning For Your Survivors | Social Security Administration#1_0", "title": "Benefits Planner: Survivors | Planning For Your Survivors | Social Security Administration#1", "doc_text": "\n\nBenefits Planner: Survivors | Planning For Your Survivors \nAs you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. You can earn up to four credits each year. In 2019 , for example , you earn one credit for each $1,360 of wages or self - employment income. When you have earned $5,440 , you have earned your four credits for the year. The number of credits needed to provide benefits for your survivors depends on your age when you die. No one needs more than 40 credits 10 years of work to be eligible for any Social Security benefit. But , the younger a person is , the fewer credits they must have for family members to receive survivors benefits. Benefits can be paid to your children and your spouse who is caring for the children even if you don't have the required number of credits. They can get benefits if you have credit for one and one - half years of work 6 credits in the three years just before your death. \n\nFor Your Widow Or Widower \nThere are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse's earnings record. And , for many of those survivors, particularly aged women, those benefits are keeping them out of poverty. Widows and widowers can receive : reduced benefits as early as age 60 or full benefits at full retirement age or older. benefits as early as age 50 if they're disabled AND their disability started before or within seven years of your death. benefits at any age , if they have not remarried , and if they take care of your child who is under age 16 or disabled and receives benefits on your record. If applying for disability benefits on a deceased worker s record , they can speed up the application process if they complete an Adult Disability Report and have it available at the time of their appointment. We use the same definition of disability for widows and widowers as we do for workers. \n\nFor Your Surviving Divorced Spouse \nIf you have a surviving divorced spouse , they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more. Benefits paid to a surviving divorced spouse won't affect the benefit amounts your other survivors will receive based on your earnings record. If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record , they will not have to meet the length - of - marriage rule. The child must be your natural or legally adopted child. \n\nFor Your Children \nYour unmarried children who are under 18 up to age 19 if attending elementary or secondary school full time can be eligible to receive Social Security benefits when you die. And your child can get benefits at any age if they were disabled before age 22 and remain disabled. Besides your natural children , your stepchildren, grandchildren, step grandchildren or adopted children may receive benefits under certain circumstances. For further information , view our publication. \n\nFor Your Parents \nYou must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record. Generally, your parent also must not have married after your death ; however, there are some exceptions. In addition to your natural parent , your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16. \n\nHow Much Would Your Survivors Receive \nHow much your family could receive in benefits depends on your average lifetime earnings. The higher your earnings were , the higher their benefits would be. We calculate a basic amount as if you had reached full retirement age at the time you die. These are examples of monthly benefit payments : Widow or widower, full retirement age or older 100 percent of your benefit amount ; Widow or widower , age 60 to full retirement age 71 to 99 percent of your basic amount ; Disabled widow or widower , age 50 through 59 71 percent ; Widow or widower , any age, caring for a child under age 16 75 percent ; A child under age 18 19 if still in elementary or secondary school or disabled 75 percent ; and Your dependent parent , age 62 or older : One surviving parent 82 percent. Two surviving parents 75 percent to each parent. Percentages for a surviving divorced spouse would be the same as above. There may also be a special lump - sum death payment. \n\nMaximum Family Amount \nThere's a limit to the amount that family members can receive each month. The limit varies , but it is generally equal to between 150 and 180 percent of the basic benefit rate. If the sum of the benefits payable to family members is greater than this limit , the benefits will be reduced proportionately. Any benefits paid to a surviving divorced spouse based on disability or age won't count toward this maximum amount. Get your online or check our Benefit Calculators for an estimate of the benefits your family could receive if you died right now. \n\nOther Things You Need To Know \nThere are limits on how much survivors may earn while they receive benefits. Benefits for a widow, widower, or surviving divorced spouse may be affected by several additional factors : If your widow, widower, or surviving divorced spouse remarries before they reach age 60 age 50 if disabled , they cannot receive benefits as a surviving spouse while they're married. If your widow, widower, or surviving divorced spouse remarries after they reach age 60 age 50 if disabled , they will continue to qualify for benefits on your Social Security record. However , if their current spouse is a Social Security beneficiary , they may want to apply for spouse's benefits on their record. If that amount is more than the widow's or widower's benefit on your record , they will receive a combination of benefits that equals the higher amount. If your widow, widower, or surviving divorced spouse receives benefits on your record , they can switch to their own retirement benefit as early as age 62. This assumes they're eligible for retirement benefits and their retirement rate is higher than their rate as a widow, widower, or surviving divorced spouse. In many cases , a widow or widower can begin receiving one benefit at a reduced rate and then, at full retirement age, switch to the other benefit at an unreduced rate. If your widow, widower, or surviving divorced spouse will also receive a pension based on work not covered by Social Security, such as government or foreign work , their Social Security benefits as a survivor may be affected. ", "spans": [ { "id_sp": "1", "tag": "h2", "start_sp": 0, "end_sp": 61, "text_sp": "\n\nBenefits Planner: Survivors | Planning For Your Survivors \n", "title": "Benefits Planner: Survivors | Planning For Your Survivors", "parent_titles": { "id_sp": [], "text": [], "level": [] }, "id_sec": "t_0", "start_sec": 0, "text_sec": "\n\nBenefits Planner: Survivors | Planning For Your Survivors \n", "end_sec": 61 }, { "id_sp": "2", "tag": "u", "start_sp": 61, "end_sp": 90, "text_sp": "As you plan for the future , ", "title": "Benefits Planner: Survivors | Planning For Your Survivors", "parent_titles": { "id_sp": [], "text": [], "level": [] }, "id_sec": "1", "start_sec": 61, "text_sec": "As you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. ", "end_sec": 274 }, { "id_sp": "3", "tag": "u", "start_sp": 90, "end_sp": 168, "text_sp": "you'll want to think about what your family would need if you should die now. ", "title": "Benefits Planner: Survivors | Planning For Your Survivors", "parent_titles": { "id_sp": [], "text": [], "level": [] }, "id_sec": "1", "start_sec": 61, "text_sec": "As you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. ", "end_sec": 274 } ], "doc_html_ts": "<main><section><div><h2 sent_id=\"1\" text_id=\"1\">Benefits Planner: Survivors | Planning For Your Survivors</h2></div></section><section><div><article><section><div tag_id=\"1\"><u sent_id=\"2\" tag_id=\"1\"><u sent_id=\"2\" tag_id=\"1\" text_id=\"2\">As you plan for the future ,</u><u sent_id=\"2\" tag_id=\"1\" text_id=\"3\">you 'll want to think about what your family would need if you should die now .</u></u><u sent_id=\"3\" tag_id=\"1\"><u sent_id=\"3\" tag_id=\"1\" text_id=\"4\">Social Security can help your family if you have earned enough Social Security credits through your work .</u></u></div><div tag_id=\"2\"><u sent_id=\"4\" tag_id=\"2\"><u sent_id=\"4\" tag_id=\"2\" text_id=\"5\">You can earn up to four credits each year .</u></u><u sent_id=\"5\" tag_id=\"2\"><u sent_id=\"5\" tag_id=\"2\" text_id=\"6\">In 2019 ,</u><u sent_id=\"5\" tag_id=\"2\" text_id=\"7\">for example ,</u><u sent_id=\"5\" tag_id=\"2\" text_id=\"8\">you earn one credit for each $ 1,360 of wages or self - employment income .</u></u><u sent_id=\"6\" tag_id=\"2\"><u sent_id=\"6\" tag_id=\"2\" text_id=\"9\">When you have earned $ 5,440 ,</u><u sent_id=\"6\" tag_id=\"2\" text_id=\"10\">you have earned your four credits for the year .</u></u></div><div tag_id=\"3\"><u sent_id=\"7\" tag_id=\"3\"><u sent_id=\"7\" tag_id=\"3\" text_id=\"11\">The number of credits needed to provide benefits for your survivors depends on your age when you die .</u></u><u sent_id=\"8\" tag_id=\"3\"><u sent_id=\"8\" tag_id=\"3\" text_id=\"12\">No one needs more than 40 credits 10 years of work to be eligible for any Social Security benefit .</u></u><u sent_id=\"9\" tag_id=\"3\"><u sent_id=\"9\" tag_id=\"3\" text_id=\"13\">But ,</u><u sent_id=\"9\" tag_id=\"3\" text_id=\"14\">the younger a person is ,</u><u sent_id=\"9\" tag_id=\"3\" text_id=\"15\">the fewer credits they must have for family members to receive survivors benefits .</u></u></div><div tag_id=\"4\"><u sent_id=\"10\" tag_id=\"4\"><u sent_id=\"10\" tag_id=\"4\" text_id=\"16\">Benefits can be paid to your children and your spouse who is caring for the children even if you do n't have the required number of credits .</u></u><u sent_id=\"11\" tag_id=\"4\"><u sent_id=\"11\" tag_id=\"4\" text_id=\"17\">They can get benefits if you have credit for one and one - half years of work 6 credits in the three years just before your death .</u></u></div></section><section><h3 sent_id=\"12\" text_id=\"18\">For Your Widow Or Widower</h3><div tag_id=\"5\"><u sent_id=\"13\" tag_id=\"5\"><u sent_id=\"13\" tag_id=\"5\" text_id=\"19\">There are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse 's earnings record .</u></u><u sent_id=\"14\" tag_id=\"5\"><u sent_id=\"14\" tag_id=\"5\" text_id=\"20\">And ,</u><u sent_id=\"14\" tag_id=\"5\" text_id=\"21\">for many of those survivors , particularly aged women , those benefits are keeping them out of poverty .</u></u></div><div tag_id=\"6\"><u sent_id=\"15\" tag_id=\"6\"><u sent_id=\"15\" tag_id=\"6\" text_id=\"22\">Widows and widowers can receive :</u></u></div><ul class=\"browser-default\" tag_id=\"6\"><li tag_id=\"6\"><u sent_id=\"16\" tag_id=\"6\"><u sent_id=\"16\" tag_id=\"6\" text_id=\"23\">reduced benefits as early as age 60 or full benefits at full retirement age or older .</u></u></li><div>If widows or widowers qualify for retirement benefits on their own record, they can switch to their own retirement benefit as early as age 62.</div><li tag_id=\"6\"><u sent_id=\"17\" tag_id=\"6\"><u sent_id=\"17\" tag_id=\"6\" text_id=\"24\">benefits as early as age 50 if they 're disabled AND their disability started before or within seven years of your death .</u></u></li><div>If a widow or widower who is caring for your children receives Social Security benefits, they're still eligible if their disability starts before those payments end or within seven years after they end.</div><li tag_id=\"6\"><u sent_id=\"18\" tag_id=\"6\"><u sent_id=\"18\" tag_id=\"6\" text_id=\"25\">benefits at any age ,</u><u sent_id=\"18\" tag_id=\"6\" text_id=\"26\">if they have not remarried ,</u><u sent_id=\"18\" tag_id=\"6\" text_id=\"27\">and if they take care of your child who is under age 16 or disabled and receives benefits on your record .</u></u></li><div>If a widow or widower remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.</div></ul><div>Widows, widowers, and surviving divorced spouses cannot apply online for survivors benefits. They should <a>contact Social Security</a> at <nobr><strong>1-800-772-1213</strong></nobr> (TTY <nobr><strong>1-800-325-0778</strong>) to request an appointment.</nobr></div><div tag_id=\"7\"><u sent_id=\"19\" tag_id=\"7\"><u sent_id=\"19\" tag_id=\"7\" text_id=\"28\">If applying for disability benefits on a deceased worker s record ,</u><u sent_id=\"19\" tag_id=\"7\" text_id=\"29\">they can speed up the application process if they complete an Adult Disability Report and have it available at the time of their appointment .</u></u></div><div tag_id=\"8\"><u sent_id=\"20\" tag_id=\"8\"><u sent_id=\"20\" tag_id=\"8\" text_id=\"30\">We use the same definition of disability for widows and widowers as we do for workers .</u></u></div></section><section><h3 sent_id=\"21\" text_id=\"31\">For Your Surviving Divorced Spouse</h3><div tag_id=\"9\"><u sent_id=\"22\" tag_id=\"9\"><u sent_id=\"22\" tag_id=\"9\" text_id=\"32\">If you have a surviving divorced spouse ,</u><u sent_id=\"22\" tag_id=\"9\" text_id=\"33\">they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more .</u></u></div><div>If your surviving divorced spouse qualifies for retirement benefits on their own record they can switch to their own retirement benefit as early as age 62.</div><div tag_id=\"10\"><u sent_id=\"23\" tag_id=\"10\"><u sent_id=\"23\" tag_id=\"10\" text_id=\"34\">Benefits paid to a surviving divorced spouse wo n't affect the benefit amounts your other survivors will receive based on your earnings record .</u></u></div><div>If your surviving divorced spouse remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.</div><div tag_id=\"11\"><u sent_id=\"24\" tag_id=\"11\"><u sent_id=\"24\" tag_id=\"11\" text_id=\"35\">If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record ,</u><u sent_id=\"24\" tag_id=\"11\" text_id=\"36\">they will not have to meet the length - of - marriage rule .</u></u><u sent_id=\"25\" tag_id=\"11\"><u sent_id=\"25\" tag_id=\"11\" text_id=\"37\">The child must be your natural or legally adopted child .</u></u></div><div>However, if they qualify for benefits as a surviving divorced mother or father who is caring for your child, their benefits may affect the amount of benefits your other survivors will receive based on your earnings record.</div></section><section><h3 sent_id=\"26\" text_id=\"38\">For Your Children</h3><div tag_id=\"12\"><u sent_id=\"27\" tag_id=\"12\"><u sent_id=\"27\" tag_id=\"12\" text_id=\"39\">Your unmarried children who are under 18 up to age 19 if attending elementary or secondary school full time can be eligible to receive Social Security benefits when you die .</u></u></div><div tag_id=\"13\"><u sent_id=\"28\" tag_id=\"13\"><u sent_id=\"28\" tag_id=\"13\" text_id=\"40\">And your child can get benefits at any age if they were disabled before age 22 and remain disabled .</u></u></div><div tag_id=\"14\"><u sent_id=\"29\" tag_id=\"14\"><u sent_id=\"29\" tag_id=\"14\" text_id=\"41\">Besides your natural children ,</u><u sent_id=\"29\" tag_id=\"14\" text_id=\"42\">your stepchildren , grandchildren , step grandchildren or adopted children may receive benefits under certain circumstances .</u></u><u sent_id=\"30\" tag_id=\"14\"><u sent_id=\"30\" tag_id=\"14\" text_id=\"43\">For further information ,</u><u sent_id=\"30\" tag_id=\"14\" text_id=\"44\">view our publication .</u></u></div></section><section><h3 sent_id=\"31\" text_id=\"45\">For Your Parents</h3><div tag_id=\"15\"><u sent_id=\"32\" tag_id=\"15\"><u sent_id=\"32\" tag_id=\"15\" text_id=\"46\">You must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record .</u></u><u sent_id=\"33\" tag_id=\"15\"><u sent_id=\"33\" tag_id=\"15\" text_id=\"47\">Generally , your parent also must not have married after your death ;</u><u sent_id=\"33\" tag_id=\"15\" text_id=\"48\">however , there are some exceptions .</u></u></div><div tag_id=\"16\"><u sent_id=\"34\" tag_id=\"16\"><u sent_id=\"34\" tag_id=\"16\" text_id=\"49\">In addition to your natural parent ,</u><u sent_id=\"34\" tag_id=\"16\" text_id=\"50\">your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16 .</u></u></div></section><section><h3 sent_id=\"35\" text_id=\"51\">How Much Would Your Survivors Receive</h3><div tag_id=\"17\"><u sent_id=\"36\" tag_id=\"17\"><u sent_id=\"36\" tag_id=\"17\" text_id=\"52\">How much your family could receive in benefits</u><u sent_id=\"36\" tag_id=\"17\" text_id=\"53\">depends on your average lifetime earnings .</u></u><u sent_id=\"37\" tag_id=\"17\"><u sent_id=\"37\" tag_id=\"17\" text_id=\"54\">The higher your earnings were ,</u><u sent_id=\"37\" tag_id=\"17\" text_id=\"55\">the higher their benefits would be .</u></u><u sent_id=\"38\" tag_id=\"17\"><u sent_id=\"38\" tag_id=\"17\" text_id=\"56\">We calculate a basic amount as if you had reached full retirement age at the time you die .</u></u></div><div>If you are already receiving reduced benefits when you die, survivors benefits are based on that amount.</div><div tag_id=\"18\"><u sent_id=\"39\" tag_id=\"18\"><u sent_id=\"39\" tag_id=\"18\" text_id=\"57\">These are examples of monthly benefit payments :</u></u></div><ul class=\"browser-default\" tag_id=\"18\"><li tag_id=\"18\"><u sent_id=\"40\" tag_id=\"18\"><u sent_id=\"40\" tag_id=\"18\" text_id=\"58\">Widow or widower , full retirement age or older 100 percent of your benefit amount ;</u></u></li><li tag_id=\"18\"><u sent_id=\"41\" tag_id=\"18\"><u sent_id=\"41\" tag_id=\"18\" text_id=\"59\">Widow or widower ,</u><u sent_id=\"41\" tag_id=\"18\" text_id=\"60\">age 60 to full retirement age 71 to 99 percent of your basic amount ;</u></u></li><li tag_id=\"18\"><u sent_id=\"42\" tag_id=\"18\"><u sent_id=\"42\" tag_id=\"18\" text_id=\"61\">Disabled widow or widower ,</u><u sent_id=\"42\" tag_id=\"18\" text_id=\"62\">age 50 through 59 71 percent ;</u></u></li><li tag_id=\"18\"><u sent_id=\"43\" tag_id=\"18\"><u sent_id=\"43\" tag_id=\"18\" text_id=\"63\">Widow or widower ,</u><u sent_id=\"43\" tag_id=\"18\" text_id=\"64\">any age , caring for a child under age 16 75 percent ;</u></u></li><li tag_id=\"18\"><u sent_id=\"44\" tag_id=\"18\"><u sent_id=\"44\" tag_id=\"18\" text_id=\"65\">A child under age 18 19 if still in elementary or secondary school or disabled 75 percent ;</u><u sent_id=\"44\" tag_id=\"18\" text_id=\"66\">and</u></u></li><li tag_id=\"18\"><div tag_id=\"18\"><u sent_id=\"48\" tag_id=\"18\"><u sent_id=\"48\" tag_id=\"18\" text_id=\"67\">Your dependent parent ,</u><u sent_id=\"48\" tag_id=\"18\" text_id=\"68\">age 62 or older :</u></u></div><ul class=\"browser-default\" tag_id=\"18\"><li tag_id=\"18\"><u sent_id=\"49\" tag_id=\"18\"><u sent_id=\"49\" tag_id=\"18\" text_id=\"69\">One surviving parent 82 percent .</u></u></li><li tag_id=\"18\"><u sent_id=\"50\" tag_id=\"18\"><u sent_id=\"50\" tag_id=\"18\" text_id=\"70\">Two surviving parents 75 percent to each parent .</u></u></li></ul></li></ul><div tag_id=\"19\"><u sent_id=\"51\" tag_id=\"19\"><u sent_id=\"51\" tag_id=\"19\" text_id=\"71\">Percentages for a surviving divorced spouse would be the same as above .</u></u></div><div tag_id=\"20\"><u sent_id=\"52\" tag_id=\"20\"><u sent_id=\"52\" tag_id=\"20\" text_id=\"72\">There may also be a special lump - sum death payment .</u></u></div><h3 sent_id=\"53\" text_id=\"73\">Maximum Family Amount</h3><div tag_id=\"21\"><u sent_id=\"54\" tag_id=\"21\"><u sent_id=\"54\" tag_id=\"21\" text_id=\"74\">There 's a limit to the amount that family members can receive each month .</u></u><u sent_id=\"55\" tag_id=\"21\"><u sent_id=\"55\" tag_id=\"21\" text_id=\"75\">The limit varies ,</u><u sent_id=\"55\" tag_id=\"21\" text_id=\"76\">but it is generally equal to between 150 and 180 percent of the basic benefit rate .</u></u></div><div tag_id=\"22\"><u sent_id=\"56\" tag_id=\"22\"><u sent_id=\"56\" tag_id=\"22\" text_id=\"77\">If the sum of the benefits payable to family members is greater than this limit ,</u><u sent_id=\"56\" tag_id=\"22\" text_id=\"78\">the benefits will be reduced proportionately .</u></u><u sent_id=\"57\" tag_id=\"22\"><u sent_id=\"57\" tag_id=\"22\" text_id=\"79\">Any benefits paid to a surviving divorced spouse based on disability or age wo n't count toward this maximum amount .</u></u></div><div tag_id=\"23\"><u sent_id=\"58\" tag_id=\"23\"><u sent_id=\"58\" tag_id=\"23\" text_id=\"80\">Get your online or check our Benefit Calculators for an estimate of the benefits your family could receive if you died right now .</u></u></div><h3 sent_id=\"59\" text_id=\"81\">Other Things You Need To Know</h3><div tag_id=\"24\"><u sent_id=\"60\" tag_id=\"24\"><u sent_id=\"60\" tag_id=\"24\" text_id=\"82\">There are limits on how much survivors may earn while they receive benefits .</u></u></div><div tag_id=\"25\"><u sent_id=\"61\" tag_id=\"25\"><u sent_id=\"61\" tag_id=\"25\" text_id=\"83\">Benefits for a widow , widower , or surviving divorced spouse may be affected by several additional factors :</u></u></div><div><a>If they remarry</a><section><div tag_id=\"26\"><u sent_id=\"62\" tag_id=\"26\"><u sent_id=\"62\" tag_id=\"26\" text_id=\"84\">If your widow , widower , or surviving divorced spouse remarries before they reach age 60 age 50 if disabled ,</u><u sent_id=\"62\" tag_id=\"26\" text_id=\"85\">they can not receive benefits as a surviving spouse while they 're married .</u></u></div><div tag_id=\"27\"><u sent_id=\"63\" tag_id=\"27\"><u sent_id=\"63\" tag_id=\"27\" text_id=\"86\">If your widow , widower , or surviving divorced spouse remarries after they reach age 60 age 50 if disabled ,</u><u sent_id=\"63\" tag_id=\"27\" text_id=\"87\">they will continue to qualify for benefits on your Social Security record .</u></u></div><div tag_id=\"28\"><u sent_id=\"64\" tag_id=\"28\"><u sent_id=\"64\" tag_id=\"28\" text_id=\"88\">However ,</u><u sent_id=\"64\" tag_id=\"28\" text_id=\"89\">if their current spouse is a Social Security beneficiary ,</u><u sent_id=\"64\" tag_id=\"28\" text_id=\"90\">they may want to apply for spouse 's benefits on their record .</u></u><u sent_id=\"65\" tag_id=\"28\"><u sent_id=\"65\" tag_id=\"28\" text_id=\"91\">If that amount is more than the widow 's or widower 's benefit on your record ,</u><u sent_id=\"65\" tag_id=\"28\" text_id=\"92\">they will receive a combination of benefits that equals the higher amount .</u></u></div></section></div><div><a>If they're eligible for retirement benefits on their own record</a><section><div tag_id=\"29\"><u sent_id=\"66\" tag_id=\"29\"><u sent_id=\"66\" tag_id=\"29\" text_id=\"93\">If your widow , widower , or surviving divorced spouse receives benefits on your record ,</u><u sent_id=\"66\" tag_id=\"29\" text_id=\"94\">they can switch to their own retirement benefit as early as age 62 .</u></u><u sent_id=\"67\" tag_id=\"29\"><u sent_id=\"67\" tag_id=\"29\" text_id=\"95\">This assumes they 're eligible for retirement benefits and their retirement rate is higher than their rate as a widow , widower , or surviving divorced spouse .</u></u></div><div tag_id=\"30\"><u sent_id=\"68\" tag_id=\"30\"><u sent_id=\"68\" tag_id=\"30\" text_id=\"96\">In many cases ,</u><u sent_id=\"68\" tag_id=\"30\" text_id=\"97\">a widow or widower can begin receiving one benefit at a reduced rate and then , at full retirement age , switch to the other benefit at an unreduced rate .</u></u></div><div><a>Full retirement age for retirement benefits</a> may not match full retirement age for survivors benefits.</div></section></div><div><a>If they will also receive a pension based on work not covered by Social Security</a><section><div tag_id=\"31\"><u sent_id=\"69\" tag_id=\"31\"><u sent_id=\"69\" tag_id=\"31\" text_id=\"98\">If your widow , widower , or surviving divorced spouse will also receive a pension based on work not covered by Social Security , such as government or foreign work ,</u><u sent_id=\"69\" tag_id=\"31\" text_id=\"99\">their Social Security benefits as a survivor may be affected .</u></u></div></section></div></section></article></div></section></main>", "doc_html_raw": "<main class=\"content\" id=\"content\" role=\"main\">\n\n<section>\n\n<div>\n<h2>Benefits Planner: Survivors | Planning For Your Survivors</h2>\n</div>\n</section>\n\n<section>\n\n<div>\n\n<div>\n\n\n</div>\n\n\n\n<article>\n<section>\n<p>As you plan for the future, you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work.</p>\n<p><a>You can earn up to four credits each year</a>. In 2019, for example, you earn one credit for each $1,360 of wages or <a>self-employment</a> income. When you have earned $5,440, you have earned your four credits for the year.</p>\n<p>The number of credits needed to provide benefits for your survivors depends on your age when you die. No one needs more than 40 credits (10 years of work) to be eligible for any Social Security benefit. But, the younger a person is, the fewer credits they must have for family members to receive survivors benefits.</p>\n<p>Benefits can be paid to your children and your spouse who is caring for the children even if you don't have the required number of credits. They can get benefits if you have credit for one and one-half years of work (6 credits) in the three years just before your death.</p>\n</section>\n<section>\n<h3>For Your Widow Or Widower</h3>\n<p>There are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse's earnings record. And, for many of those survivors, particularly aged women, those benefits are keeping them out of poverty. </p>\n<p>Widows and widowers can receive:</p>\n<ul class=\"browser-default\">\n<li>reduced benefits as early as age 60 or full benefits at <a>full retirement age</a> or older.</li>\n<div>\n If widows or widowers qualify for retirement benefits on their own record, they can switch to their own retirement benefit as early as age 62.\n </div>\n<li>benefits as early as age 50 if they're disabled AND their disability started before or within seven years of your death.</li>\n<div>\n If a widow or widower who is caring for your children receives Social Security benefits, they're still eligible if their disability starts before those payments end or within seven years after they end.\n </div>\n<li>benefits at any age, if they have not remarried, and if they take care of your child who is under age 16 or disabled and receives benefits on your record.</li>\n<div>\n If a widow or widower remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.\n </div>\n</ul>\n<div>\n Widows, widowers, and surviving divorced spouses cannot apply online for survivors benefits. They should <a>contact Social Security</a> at <nobr><strong>1-800-772-1213</strong></nobr> (TTY <nobr><strong>1-800-325-0778</strong>) to request an appointment.</nobr>\n</div>\n<p>If applying for disability benefits on a deceased worker s record, they can speed up the application process if they complete an <a>Adult Disability Report</a> and have it available at the time of their appointment.</p>\n<p>We use the same <a>definition of disability</a> for widows and widowers as we do for workers.</p>\n</section>\n<section>\n<h3>For Your Surviving Divorced Spouse</h3>\n<p>If you have a surviving divorced spouse, they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more.</p>\n<div>\n If your surviving divorced spouse qualifies for retirement benefits on their own record they can switch to their own retirement benefit as early as age 62.\n </div>\n<p>Benefits paid to a surviving divorced spouse won't affect the benefit amounts your other survivors will receive based on your earnings record.</p>\n<div>\n If your surviving divorced spouse remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.\n </div>\n<p>If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record, they will not have to meet the length-of-marriage rule. The child must be your natural or legally adopted child.</p>\n<div>\n However, if they qualify for benefits as a surviving divorced mother or father who is caring for your child, their benefits may affect the amount of benefits your other survivors will receive based on your earnings record.\n </div>\n</section>\n<section>\n<h3>For Your Children</h3>\n<p>Your unmarried children who are under 18 (up to age 19 if attending elementary or secondary school full time) can be eligible to receive Social Security benefits when you die.</p>\n<p>And your child can get benefits at any age if they were disabled before age 22 and remain disabled.</p>\n<p>Besides your natural children, your stepchildren, grandchildren, step grandchildren or adopted children may receive benefits under certain circumstances. For further information, view our <a>publication</a>.</p>\n</section>\n<section>\n<h3>For Your Parents</h3>\n<p>You must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record. Generally, your parent also must not have married after your death; however, there are some exceptions.</p>\n<p>In addition to your natural parent, your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16.</p>\n</section>\n<section>\n<h3>How Much Would Your Survivors Receive</h3>\n<p>How much your family could receive in benefits depends on your average lifetime earnings. The higher your earnings were, the higher their benefits would be. We calculate a basic amount as if you had reached full retirement age at the time you die.</p>\n<div>\n If you are already receiving reduced benefits when you die, survivors benefits are based on that amount.\n </div>\n<p>These are examples of monthly benefit payments:</p>\n<ul class=\"browser-default\">\n<li>Widow or widower, <a>full retirement age</a> or older 100 percent of your benefit amount;</li>\n<li>Widow or widower, age 60 to <a>full retirement age</a> 71 to 99 percent of your basic amount;</li>\n<li>Disabled widow or widower, age 50 through 59 71 percent;</li>\n<li>Widow or widower, any age, caring for a child under age 16 75 percent;</li>\n<li>A child under age 18 (19 if still in elementary or secondary school) or disabled 75 percent; and</li>\n<li>Your dependent parent(s), age 62 or older:\n <ul class=\"browser-default\">\n<li>One surviving parent 82 percent.</li>\n<li>Two surviving parents 75 percent to each parent.</li>\n</ul>\n</li>\n</ul>\n<p>Percentages for a surviving divorced spouse would be the same as above.</p>\n<p>There may also be a <a>special lump-sum death payment</a>.</p>\n<h3>Maximum Family Amount</h3>\n<p>There's a limit to the amount that family members can receive each month. <a>The limit varies</a>, but it is generally equal to between 150 and 180 percent of the basic benefit rate.</p>\n<p>If the sum of the benefits payable to family members is greater than this limit, the benefits will be reduced proportionately. (Any benefits paid to a surviving divorced spouse based on disability or age won't count toward this maximum amount.)</p>\n<p>Get your <a></a> online or check our <a>Benefit Calculators</a> for an estimate of the benefits your family could receive if you died right now.</p>\n<h3>Other Things You Need To Know</h3>\n<p>There are <a>limits on how much survivors may earn</a> while they receive benefits.</p>\n<p>Benefits for a widow, widower, or surviving divorced spouse may be affected by several additional factors:</p>\n<div>\n<a>If they remarry</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse remarries before they reach age 60 (age 50 if disabled), they cannot receive benefits as a surviving spouse while they're married.</p>\n<p>If your widow, widower, or surviving divorced spouse remarries after they reach age 60 (age 50 if disabled), they will continue to qualify for benefits on your Social Security record.</p>\n<p>However, if their current spouse is a Social Security beneficiary, they may want to apply for spouse's benefits on their record. If that amount is more than the widow's or widower's benefit on your record, they will receive a combination of benefits that equals the higher amount.</p>\n</section>\n</div>\n<div>\n<a>If they're eligible for retirement benefits on their own record</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse receives benefits on your record, they can switch to their own retirement benefit as early as age 62. This assumes they're eligible for retirement benefits and their retirement rate is higher than their rate as a widow, widower, or surviving divorced spouse.</p>\n<p>In many cases, a widow or widower can begin receiving one benefit at a reduced rate and then, at full retirement age, switch to the other benefit at an unreduced rate.</p>\n<div>\n<a>Full retirement age for retirement benefits</a> may not match full retirement age for survivors benefits.\n </div>\n</section>\n</div>\n<div>\n<a>If they will also receive a pension based on work not covered by Social Security</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse will also receive a pension based on work not covered by Social Security, such as government or foreign work, <a>their Social Security benefits as a survivor may be affected</a>.</p>\n</section>\n</div>\n</section>\n</article>\n</div>\n</section>\n</main>" } ``` Sample data instance for `dialogue_domain` : ``` { "dial_id": "8df07b7a98990db27c395cb1f68a962e", "domain": "dmv", "turns": [ { "turn_id": 1, "role": "user", "da": "query_condition", "references": [ { "id_sp": "4", "label": "precondition", "doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0" } ], "utterance": "Hello, I forgot o update my address, can you help me with that?" }, { "turn_id": 2, "role": "agent", "da": "respond_solution", "references": [ { "id_sp": "6", "label": "solution", "doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0" }, { "id_sp": "7", "label": "solution", "doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0" } ], "utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles." }, { "turn_id": 3, "role": "user", "da": "query_solution", "references": [ { "id_sp": "56", "label": "solution", "doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0" } ], "utterance": "Can I do my DMV transactions online?" } ] } ``` ### Data Fields - `document_domain` contains the documents that are indexed by key `domain` and `doc_id` . Each document instance includes the following, - `domain`: the domain of the document; - `doc_id`: the ID of a document; - `title`: the title of the document; - `doc_text`: the text content of the document (without HTML markups); - `spans`: key-value pairs of all spans in the document, with `id_sp` as key. Each span includes the following, - `id_sp`: the id of a span as noted by `text_id` in `doc_html_ts`; - `start_sp`/ `end_sp`: the start/end position of the text span in `doc_text`; - `text_sp`: the text content of the span. - `id_sec`: the id of the (sub)section (e.g. `<p>`) or title (`<h2>`) that contains the span. - `start_sec` / `end_sec`: the start/end position of the (sub)section in `doc_text`. - `text_sec`: the text of the (sub)section. - `title`: the title of the (sub)section. - `parent_titles`: the parent titles of the `title`. - `doc_html_ts`: the document content with HTML markups and the annotated spans that are indicated by `text_id` attribute, which corresponds to `id_sp`. - `doc_html_raw`: the document content with HTML markups and without span annotations. - `dialogue_domain` Each dialogue instance includes the following, - `dial_id`: the ID of a dialogue; - `domain`: the domain of the document; - `turns`: a list of dialogue turns. Each turn includes, - `turn_id`: the time order of the turn; - `role`: either "agent" or "user"; - `da`: dialogue act; - `references`: a list of spans with `id_sp` , `label` and `doc_id`. `references` is empty if a turn is for indicating previous user query not answerable or irrelevant to the document. **Note** that labels "*precondition*"/"*solution*" are fuzzy annotations that indicate whether a span is for describing a conditional context or a solution. - `utterance`: the human-generated utterance based on the dialogue scene. - `multidoc2dial` Each dialogue instance includes the following, - `id`: the ID of a QA instance - `title`: the title of the relevant document; - `context`: the text content of the relevant document (without HTML markups). - `question`: user query; - `da`: dialogue act; - `answers`: the answers that are grounded in the associated document; - `text`: the text content of the grounding span; - `answer_start`: the start position of the grounding span in the associated document (context); - `utterance`: the human-generated utterance based on the dialogue scene. - `domain`: domain of the relevant document; ### Data Splits Training, dev and test split for default configuration `multidoc2dial`, with respectively 21451, 4201 and 5 examples, - Training & dev split for dialogue domain, with 3474 and 661 examples, - Training split only for document domain, with 488 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 Song Feng, Siva Sankalp Patel, Hui Wan, Sachindra Joshi ### Licensing Information Creative Commons Attribution 3.0 Unported ### Citation Information ```bibtex @inproceedings{feng2021multidoc2dial, title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents}, author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra}, booktitle={EMNLP}, year={2021} } ``` ### Contributions Thanks to [@songfeng](https://github.com/songfeng) and [@sivasankalpp](https://github.com/sivasankalpp) for adding this dataset.

multidoc2dial

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K", "1K<n<10K", "n<1K"], "source_datasets": ["extended|doc2dial"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "multidoc2dial", "pretty_name": "MultiDoc2Dial", "config_names": ["dialogue_domain", "document_domain", "multidoc2dial"], "dataset_info": [{"config_name": "dialogue_domain", "features": [{"name": "dial_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "turns", "list": [{"name": "turn_id", "dtype": "int32"}, {"name": "role", "dtype": "string"}, {"name": "da", "dtype": "string"}, {"name": "references", "list": [{"name": "id_sp", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}]}, {"name": "utterance", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 11700558, "num_examples": 3474}, {"name": "validation", "num_bytes": 2210338, "num_examples": 661}], "download_size": 6868509, "dataset_size": 13910896}, {"config_name": "document_domain", "features": [{"name": "domain", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "doc_text", "dtype": "string"}, {"name": "spans", "list": [{"name": "id_sp", "dtype": "string"}, {"name": "tag", "dtype": "string"}, {"name": "start_sp", "dtype": "int32"}, {"name": "end_sp", "dtype": "int32"}, {"name": "text_sp", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "parent_titles", "sequence": [{"name": "id_sp", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "level", "dtype": "string"}]}, {"name": "id_sec", "dtype": "string"}, {"name": "start_sec", "dtype": "int32"}, {"name": "text_sec", "dtype": "string"}, {"name": "end_sec", "dtype": "int32"}]}, {"name": "doc_html_ts", "dtype": "string"}, {"name": "doc_html_raw", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29378879, "num_examples": 488}], "download_size": 6868509, "dataset_size": 29378879}, {"config_name": "multidoc2dial", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "da", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "utterance", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 24331936, "num_examples": 4201}, {"name": "train", "num_bytes": 126589862, "num_examples": 21451}, {"name": "test", "num_bytes": 23026892, "num_examples": 4094}], "download_size": 6868509, "dataset_size": 173948690}]}

2023-08-29T08:45:02+00:00

TAGS #task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-extended|doc2dial #language-English #license-apache-2.0 #arxiv-2109.12595 #region-us

# Dataset Card for MultiDoc2Dial ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: sngfng@URL ### Dataset Summary MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. ### Supported Tasks and Leaderboards > Supported Task: Open domain question answering, document-grounded dialogue, passage retrieval > Leaderboard: ### Languages English ## Dataset Structure ### Data Instances Sample data instance for 'multidoc2dial' : Sample data instance for 'document_domain' : Sample data instance for 'dialogue_domain' : ### Data Fields - 'document_domain' contains the documents that are indexed by key 'domain' and 'doc_id' . Each document instance includes the following, - 'domain': the domain of the document; - 'doc_id': the ID of a document; - 'title': the title of the document; - 'doc_text': the text content of the document (without HTML markups); - 'spans': key-value pairs of all spans in the document, with 'id_sp' as key. Each span includes the following, - 'id_sp': the id of a span as noted by 'text_id' in 'doc_html_ts'; - 'start_sp'/ 'end_sp': the start/end position of the text span in 'doc_text'; - 'text_sp': the text content of the span. - 'id_sec': the id of the (sub)section (e.g. '<p>') or title ('<h2>') that contains the span. - 'start_sec' / 'end_sec': the start/end position of the (sub)section in 'doc_text'. - 'text_sec': the text of the (sub)section. - 'title': the title of the (sub)section. - 'parent_titles': the parent titles of the 'title'. - 'doc_html_ts': the document content with HTML markups and the annotated spans that are indicated by 'text_id' attribute, which corresponds to 'id_sp'. - 'doc_html_raw': the document content with HTML markups and without span annotations. - 'dialogue_domain' Each dialogue instance includes the following, - 'dial_id': the ID of a dialogue; - 'domain': the domain of the document; - 'turns': a list of dialogue turns. Each turn includes, - 'turn_id': the time order of the turn; - 'role': either "agent" or "user"; - 'da': dialogue act; - 'references': a list of spans with 'id_sp' , 'label' and 'doc_id'. 'references' is empty if a turn is for indicating previous user query not answerable or irrelevant to the document. Note that labels "*precondition*"/"*solution*" are fuzzy annotations that indicate whether a span is for describing a conditional context or a solution. - 'utterance': the human-generated utterance based on the dialogue scene. - 'multidoc2dial' Each dialogue instance includes the following, - 'id': the ID of a QA instance - 'title': the title of the relevant document; - 'context': the text content of the relevant document (without HTML markups). - 'question': user query; - 'da': dialogue act; - 'answers': the answers that are grounded in the associated document; - 'text': the text content of the grounding span; - 'answer_start': the start position of the grounding span in the associated document (context); - 'utterance': the human-generated utterance based on the dialogue scene. - 'domain': domain of the relevant document; ### Data Splits Training, dev and test split for default configuration 'multidoc2dial', with respectively 21451, 4201 and 5 examples, - Training & dev split for dialogue domain, with 3474 and 661 examples, - Training split only for document domain, with 488 examples. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators Song Feng, Siva Sankalp Patel, Hui Wan, Sachindra Joshi ### Licensing Information Creative Commons Attribution 3.0 Unported ### Contributions Thanks to @songfeng and @sivasankalpp for adding this dataset.

f5b49fe44ad60c13a480399d58cbb3a8936eb675

# Dataset Card for MultiLingual LibriSpeech ## 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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94) - **Repository:** [Needs More Information] - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411) - **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech) ### 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> This legacy dataset doesn't support streaming and is not updated. Use "facebook/multilingual_librispeech" instead.</p> </div> Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: 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 has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER. ### Languages The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac 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. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits | | Train | Train.9h | Train.1h | Dev | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | german | 469942 | 2194 | 241 | 3469 | 3394 | | dutch | 374287 | 2153 | 234 | 3095 | 3075 | | french | 258213 | 2167 | 241 | 2416 | 2426 | | spanish | 220701 | 2110 | 233 | 2408 | 2385 | | italian | 59623 | 2173 | 240 | 1248 | 1262 | | portuguese | 37533 | 2116 | 236 | 826 | 871 | | polish | 25043 | 2173 | 238 | 512 | 520 | ## 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 [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

multilingual_librispeech

2022-03-02T23:29:22+00:00

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2024-01-18T11:09:54+00:00

TAGS #task_categories-automatic-speech-recognition #task_categories-audio-classification #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #license-cc-by-4.0 #arxiv-2012.03411 #region-us

Dataset Card for MultiLingual LibriSpeech ========================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: MultiLingual LibriSpeech ASR corpus * Repository: * Paper: MLS: A Large-Scale Multilingual Dataset for Speech Research * Leaderboard: Paperswithcode Leaderboard ### Dataset Summary **Deprecated:** This legacy dataset doesn't support streaming and is not updated. Use "facebook/multilingual\_librispeech" instead. Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. ### Supported Tasks and Leaderboards * 'automatic-speech-recognition', 'audio-speaker-identification': 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 has an active leaderboard which can be found at URL and ranks models based on their WER. ### Languages The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish Dataset Structure ----------------- ### Data Instances A typical data point comprises the path to the audio file, usually called 'file' and its transcription, called 'text'. Some additional information about the speaker and the passage which contains the transcription is provided. ### Data Fields * file: A path to the downloaded audio file in .flac 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. * id: unique id of the data sample. * speaker\_id: unique id of the speaker. The same speaker id can be found for multiple data samples. * chapter\_id: id of the audiobook chapter which includes the transcription. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License (CC-BY-4.0) ### Contributions Thanks to @patrickvonplaten for adding this dataset.

b1585eaa4e2c64302ccc56c78975f3ed07cfdd42

# Dataset Card for MutualFriends ## 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:** [COCOA](https://stanfordnlp.github.io/cocoa/) - **Repository:** [Github repository](https://github.com/stanfordnlp/cocoa) - **Paper:** [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (ACL 2017)](https://arxiv.org/abs/1704.07130) - **Codalab**: [Codalab](https://worksheets.codalab.org/worksheets/0xc757f29f5c794e5eb7bfa8ca9c945573/) ### Dataset Summary Our goal is to build systems that collaborate with people by exchanging information through natural language and reasoning over structured knowledge base. In the MutualFriend task, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend. ### Supported Tasks and Leaderboards We consider two agents, each with a private knowledge base of items, who must communicate their knowledge to achieve a common goal. Specifically, we designed the MutualFriends task (see the figure below). Each agent has a list of friends with attributes like school, major etc. They must chat with each other to find the unique mutual friend. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this. ``` { 'uuid': 'C_423324a5fff045d78bef75a6f295a3f4' 'scenario_uuid': 'S_hvmRM4YNJd55ecT5', 'scenario_alphas': [0.30000001192092896, 1.0, 1.0], 'scenario_attributes': { 'name': ['School', 'Company', 'Location Preference'], 'unique': [False, False, False], 'value_type': ['school', 'company', 'loc_pref'] }, 'scenario_kbs': [ [ [['School', 'Company', 'Location Preference'], ['Longwood College', 'Alton Steel', 'indoor']], [['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Leonard Green & Partners', 'indoor']], [['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'Crazy Eddie', 'indoor']], [['School', 'Company', 'Location Preference'], ['Rhodes College', "Tully's Coffee", 'indoor']], [['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'AMR Corporation', 'indoor']], [['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']], [['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'The Hartford Financial Services Group', 'indoor']], [['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'Molycorp', 'indoor']], [['School', 'Company', 'Location Preference'], ['Babson College', 'The Hartford Financial Services Group', 'indoor']] ], [ [['School', 'Company', 'Location Preference'], ['National Technological University', 'Molycorp', 'indoor']], [['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Leonard Green & Partners', 'outdoor']], [['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Data Resources Inc.', 'outdoor']], [['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']], [['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Molycorp', 'outdoor']], [['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'Molycorp', 'indoor']], [['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'STX', 'outdoor']], [['School', 'Company', 'Location Preference'], ['National Technological University', 'STX', 'outdoor']], [['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Rockstar Games', 'indoor']] ] ], 'agents': { '0': 'human', '1': 'human' }, 'outcome_reward': 1, 'events': { 'actions': ['message', 'message', 'message', 'message', 'select', 'select'], 'agents': [1, 1, 0, 0, 1, 0], 'data_messages': ['Hello', 'Do you know anyone who works at Molycorp?', 'Hi. All of my friends like the indoors.', 'Ihave two friends that work at Molycorp. They went to Salisbury and Sacred Heart.', '', ''], 'data_selects': { 'attributes': [ [], [], [], [], ['School', 'Company', 'Location Preference'], ['School', 'Company', 'Location Preference'] ], 'values': [ [], [], [], [], ['Salisbury State University', 'Molycorp', 'indoor'], ['Salisbury State University', 'Molycorp', 'indoor'] ] }, 'start_times': [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0], 'times': [1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0] }, } ``` ### Data Fields - `uuid`: example id. - `scenario_uuid`: scenario id. - `scenario_alphas`: scenario alphas. - `scenario_attributes`: all the attributes considered in the scenario. The dictionaries are liniearized: to reconstruct the dictionary of attribute i-th, one should extract the i-th elements of `unique`, `value_type` and `name`. - `unique`: bool. - `value_type`: code/type of the attribute. - `name`: name of the attribute. - `scenario_kbs`: descriptions of the persons present in the two users' databases. List of two (one for each user in the dialogue). `scenario_kbs[i]` is a list of persons. Each person is represented as two lists (one for attribute names and the other for attribute values). The j-th element of attribute names corresponds to the j-th element of attribute values (linearized dictionary). - `agents`: the two users engaged in the dialogue. - `outcome_reward`: reward of the present dialogue. - `events`: dictionary describing the dialogue. The j-th element of each sub-element of the dictionary describes the turn along the axis of the sub-element. - `actions`: type of turn (either `message` or `select`). - `agents`: who is talking? Agent 1 or 0? - `data_messages`: the string exchanged if `action==message`. Otherwise, empty string. - `data_selects`: selection of the user if `action==select`. Otherwise, empty selection/dictionary. - `start_times`: always -1 in these data. - `times`: sending time. ### Data Splits There are 8967 dialogues for training, 1083 for validation and 1107 for testing. ## 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 ``` @inproceedings{he-etal-2017-learning, title = "Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings", author = "He, He and Balakrishnan, Anusha and Eric, Mihail and Liang, Percy", 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/P17-1162", doi = "10.18653/v1/P17-1162", pages = "1766--1776", abstract = "We study a \textit{symmetric collaborative dialogue} setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.", } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

mutual_friends

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["dialogue-modeling"], "paperswithcode_id": "mutualfriends", "pretty_name": "MutualFriends", "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "scenario_uuid", "dtype": "string"}, {"name": "scenario_alphas", "sequence": "float32"}, {"name": "scenario_attributes", "sequence": [{"name": "unique", "dtype": "bool_"}, {"name": "value_type", "dtype": "string"}, {"name": "name", "dtype": "string"}]}, {"name": "scenario_kbs", "sequence": {"sequence": {"sequence": {"sequence": "string"}}}}, {"name": "agents", "struct": [{"name": "1", "dtype": "string"}, {"name": "0", "dtype": "string"}]}, {"name": "outcome_reward", "dtype": "int32"}, {"name": "events", "struct": [{"name": "actions", "sequence": "string"}, {"name": "start_times", "sequence": "float32"}, {"name": "data_messages", "sequence": "string"}, {"name": "data_selects", "sequence": [{"name": "attributes", "sequence": "string"}, {"name": "values", "sequence": "string"}]}, {"name": "agents", "sequence": "int32"}, {"name": "times", "sequence": "float32"}]}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 26979472, "num_examples": 8967}, {"name": "test", "num_bytes": 3327158, "num_examples": 1107}, {"name": "validation", "num_bytes": 3267881, "num_examples": 1083}], "download_size": 41274578, "dataset_size": 33574511}}

2024-01-18T11:09:58+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #arxiv-1704.07130 #region-us

# Dataset Card for MutualFriends ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: COCOA - Repository: Github repository - Paper: Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (ACL 2017) - Codalab: Codalab ### Dataset Summary Our goal is to build systems that collaborate with people by exchanging information through natural language and reasoning over structured knowledge base. In the MutualFriend task, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend. ### Supported Tasks and Leaderboards We consider two agents, each with a private knowledge base of items, who must communicate their knowledge to achieve a common goal. Specifically, we designed the MutualFriends task (see the figure below). Each agent has a list of friends with attributes like school, major etc. They must chat with each other to find the unique mutual friend. ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. ## Dataset Structure ### Data Instances An example looks like this. ### Data Fields - 'uuid': example id. - 'scenario_uuid': scenario id. - 'scenario_alphas': scenario alphas. - 'scenario_attributes': all the attributes considered in the scenario. The dictionaries are liniearized: to reconstruct the dictionary of attribute i-th, one should extract the i-th elements of 'unique', 'value_type' and 'name'. - 'unique': bool. - 'value_type': code/type of the attribute. - 'name': name of the attribute. - 'scenario_kbs': descriptions of the persons present in the two users' databases. List of two (one for each user in the dialogue). 'scenario_kbs[i]' is a list of persons. Each person is represented as two lists (one for attribute names and the other for attribute values). The j-th element of attribute names corresponds to the j-th element of attribute values (linearized dictionary). - 'agents': the two users engaged in the dialogue. - 'outcome_reward': reward of the present dialogue. - 'events': dictionary describing the dialogue. The j-th element of each sub-element of the dictionary describes the turn along the axis of the sub-element. - 'actions': type of turn (either 'message' or 'select'). - 'agents': who is talking? Agent 1 or 0? - 'data_messages': the string exchanged if 'action==message'. Otherwise, empty string. - 'data_selects': selection of the user if 'action==select'. Otherwise, empty selection/dictionary. - 'start_times': always -1 in these data. - 'times': sending time. ### Data Splits There are 8967 dialogues for training, 1083 for validation and 1107 for testing. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @VictorSanh for adding this dataset.

7df164e234e36c492d910d4b8ca89f4c340b0162

# Dataset Card for The modified Winograd Schema Challenge (MWSC) ## 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://decanlp.com](http://decanlp.com) - **Repository:** https://github.com/salesforce/decaNLP - **Paper:** [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730) - **Point of Contact:** [Bryan McCann](mailto:[email protected]), [Nitish Shirish Keskar](mailto:[email protected]) - **Size of downloaded dataset files:** 19.20 kB - **Size of the generated dataset:** 39.35 kB - **Total amount of disk used:** 58.55 kB ### Dataset Summary Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context. This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing. ### 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:** 0.02 MB - **Size of the generated dataset:** 0.04 MB - **Total amount of disk used:** 0.06 MB An example looks as follows: ``` { "sentence": "The city councilmen refused the demonstrators a permit because they feared violence.", "question": "Who feared violence?", "options": [ "councilmen", "demonstrators" ], "answer": "councilmen" } ``` ### Data Fields The data fields are the same among all splits. #### default - `sentence`: a `string` feature. - `question`: a `string` feature. - `options`: a `list` of `string` features. - `answer`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 80| 82| 100| ## 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 Our code for running decaNLP has been open sourced under BSD-3-Clause. We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case. From the [Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html): > Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/). ### Citation Information If you use this in your work, please cite: ``` @article{McCann2018decaNLP, title={The Natural Language Decathlon: Multitask Learning as Question Answering}, author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher}, journal={arXiv preprint arXiv:1806.08730}, year={2018} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

mwsc

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["extended|winograd_wsc"], "task_categories": ["multiple-choice"], "task_ids": ["multiple-choice-coreference-resolution"], "pretty_name": "Modified Winograd Schema Challenge (MWSC)", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11022, "num_examples": 80}, {"name": "test", "num_bytes": 15220, "num_examples": 100}, {"name": "validation", "num_bytes": 13109, "num_examples": 82}], "download_size": 19197, "dataset_size": 39351}}

2024-01-18T11:09:59+00:00

TAGS #task_categories-multiple-choice #task_ids-multiple-choice-coreference-resolution #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-extended|winograd_wsc #language-English #license-cc-by-4.0 #arxiv-1806.08730 #region-us

Dataset Card for The modified Winograd Schema Challenge (MWSC) ============================================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: The Natural Language Decathlon: Multitask Learning as Question Answering * Point of Contact: Bryan McCann, Nitish Shirish Keskar * Size of downloaded dataset files: 19.20 kB * Size of the generated dataset: 39.35 kB * Total amount of disk used: 58.55 kB ### Dataset Summary Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context. This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 0.02 MB * Size of the generated dataset: 0.04 MB * Total amount of disk used: 0.06 MB An example looks as follows: ### Data Fields The data fields are the same among all splits. #### default * 'sentence': a 'string' feature. * 'question': a 'string' feature. * 'options': a 'list' of 'string' features. * 'answer': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Our code for running decaNLP has been open sourced under BSD-3-Clause. We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case. From the Winograd Schema Challenge: > > Both versions of the collections are licenced under a Creative Commons Attribution 4.0 International License. > > > If you use this in your work, please cite: ### Contributions Thanks to @thomwolf, @lewtun, @ghomasHudson, @lhoestq for adding this dataset.

b899ec06227db3679b0fe3c4188a6b48cc0b65eb

# Dataset Card for Myanmar_News ## Dataset Description - **Repository:** https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem ### Dataset Summary The Myanmar news dataset contains article snippets in four categories: Business, Entertainment, Politics, and Sport. These were collected in October 2017 by Aye Hninn Khine ### Languages Myanmar/Burmese language ## Dataset Structure ### Data Fields - text - text from article - category - a topic: Business, Entertainment, **Politic**, or **Sport** (note spellings) ### Data Splits One training set (8,116 total rows) ### Source Data #### Initial Data Collection and Normalization Data was collected by Aye Hninn Khine and shared on GitHub with a GPL-3.0 license. Multiple text files were consolidated into one labeled CSV file by Nick Doiron. ## Additional Information ### Dataset Curators Contributors to original GitHub repo: - https://github.com/ayehninnkhine ### Licensing Information GPL-3.0 ### Citation Information See https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem ### Contributions Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset.

myanmar_news

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["my"], "license": ["gpl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "pretty_name": "MyanmarNews", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "Sport", "1": "Politic", "2": "Business", "3": "Entertainment"}}}}], "splits": [{"name": "train", "num_bytes": 3797368, "num_examples": 8116}], "download_size": 610592, "dataset_size": 3797368}}

2024-01-18T11:10:03+00:00

TAGS #task_categories-text-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Burmese #license-gpl-3.0 #region-us

# Dataset Card for Myanmar_News ## Dataset Description - Repository: URL ### Dataset Summary The Myanmar news dataset contains article snippets in four categories: Business, Entertainment, Politics, and Sport. These were collected in October 2017 by Aye Hninn Khine ### Languages Myanmar/Burmese language ## Dataset Structure ### Data Fields - text - text from article - category - a topic: Business, Entertainment, Politic, or Sport (note spellings) ### Data Splits One training set (8,116 total rows) ### Source Data #### Initial Data Collection and Normalization Data was collected by Aye Hninn Khine and shared on GitHub with a GPL-3.0 license. Multiple text files were consolidated into one labeled CSV file by Nick Doiron. ## Additional Information ### Dataset Curators Contributors to original GitHub repo: - URL ### Licensing Information GPL-3.0 See URL ### Contributions Thanks to @mapmeld for adding this dataset.

ffef5c92c6074016bcd1bb9adb7d4ee0e849096c

# Dataset Card for Narrative 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 - **Repository:** https://github.com/deepmind/narrativeqa - **Paper:** https://arxiv.org/abs/1712.07040 - **Paper:** https://aclanthology.org/Q18-1023/ - **Point of Contact:** [Tomáš Kočiský](mailto:[email protected]) [Jonathan Schwarz](mailto:[email protected]) [Phil Blunsom]([email protected]) [Chris Dyer]([email protected]) [Karl Moritz Hermann](mailto:[email protected]) [Gábor Melis](mailto:[email protected]) [Edward Grefenstette](mailto:[email protected]) ### Dataset Summary NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. ### Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question. ### Languages English ## Dataset Structure ### Data Instances A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided. A typical example looks like this: ``` { "document": { "id": "23jncj2n3534563110", "kind": "movie", "url": "https://www.imsdb.com/Movie%20Scripts/Name%20of%20Movie.html", "file_size": 80473, "word_count": 41000, "start": "MOVIE screenplay by", "end": ". THE END", "summary": { "text": "Joe Bloggs begins his journey exploring...", "tokens": ["Joe", "Bloggs", "begins", "his", "journey", "exploring",...], "url": "http://en.wikipedia.org/wiki/Name_of_Movie", "title": "Name of Movie (film)" }, "text": "MOVIE screenplay by John Doe\nSCENE 1..." }, "question": { "text": "Where does Joe Bloggs live?", "tokens": ["Where", "does", "Joe", "Bloggs", "live", "?"], }, "answers": [ {"text": "At home", "tokens": ["At", "home"]}, {"text": "His house", "tokens": ["His", "house"]} ] } ``` ### Data Fields - `document.id` - Unique ID for the story. - `document.kind` - "movie" or "gutenberg" depending on the source of the story. - `document.url` - The URL where the story was downloaded from. - `document.file_size` - File size (in bytes) of the story. - `document.word_count` - Number of tokens in the story. - `document.start` - First 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.end` - Last 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.summary.text` - Text of the wikipedia summary of the story. - `document.summary.tokens` - Tokenized version of `document.summary.text`. - `document.summary.url` - Wikipedia URL of the summary. - `document.summary.title` - Wikipedia Title of the summary. - `question` - `{"text":"...", "tokens":[...]}` for the question about the story. - `answers` - List of `{"text":"...", "tokens":[...]}` for valid answers for the question. ### Data Splits The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split): | Train | Valid | Test | | ------ | ----- | ----- | | 32747 | 3461 | 10557 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Stories and movies scripts were downloaded from [Project Gutenburg](https://www.gutenberg.org) and a range of movie script repositories (mainly [imsdb](http://www.imsdb.com)). #### Who are the source language producers? The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions. ### Annotations #### Annotation process Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information None ## 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 The dataset is released under a [Apache-2.0 License](https://github.com/deepmind/narrativeqa/blob/master/LICENSE). ### Citation Information ``` @article{kocisky-etal-2018-narrativeqa, title = "The {N}arrative{QA} Reading Comprehension Challenge", author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and Schwarz, Jonathan and Blunsom, Phil and Dyer, Chris and Hermann, Karl Moritz and Melis, G{\'a}bor and Grefenstette, Edward", editor = "Lee, Lillian and Johnson, Mark and Toutanova, Kristina and Roark, Brian", journal = "Transactions of the Association for Computational Linguistics", volume = "6", year = "2018", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q18-1023", doi = "10.1162/tacl_a_00023", pages = "317--328", abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.", } ``` ### Contributions Thanks to [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.

narrativeqa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": ["abstractive-qa"], "paperswithcode_id": "narrativeqa", "pretty_name": "NarrativeQA", "dataset_info": {"features": [{"name": "document", "struct": [{"name": "id", "dtype": "string"}, {"name": "kind", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "file_size", "dtype": "int32"}, {"name": "word_count", "dtype": "int32"}, {"name": "start", "dtype": "string"}, {"name": "end", "dtype": "string"}, {"name": "summary", "struct": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "text", "dtype": "string"}]}, {"name": "question", "struct": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}]}, {"name": "answers", "list": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 11556607782, "num_examples": 32747}, {"name": "test", "num_bytes": 3547135501, "num_examples": 10557}, {"name": "validation", "num_bytes": 1211859418, "num_examples": 3461}], "download_size": 192528922, "dataset_size": 16315602701}}

2024-01-19T15:35:27+00:00

TAGS #task_categories-text2text-generation #task_ids-abstractive-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #arxiv-1712.07040 #region-us

Dataset Card for Narrative QA ============================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: URL * Paper: URL * Paper: URL * Point of Contact: Tomáš Kočiský Jonathan Schwarz Phil Blunsom Chris Dyer Karl Moritz Hermann Gábor Melis Edward Grefenstette ### Dataset Summary NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. ### Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question. ### Languages English Dataset Structure ----------------- ### Data Instances A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided. A typical example looks like this: ### Data Fields * 'URL' - Unique ID for the story. * 'URL' - "movie" or "gutenberg" depending on the source of the story. * 'URL' - The URL where the story was downloaded from. * 'document.file\_size' - File size (in bytes) of the story. * 'document.word\_count' - Number of tokens in the story. * 'URL' - First 3 tokens of the story. Used for verifying the story hasn't been modified. * 'URL' - Last 3 tokens of the story. Used for verifying the story hasn't been modified. * 'URL' - Text of the wikipedia summary of the story. * 'URL' - Tokenized version of 'URL'. * 'URL' - Wikipedia URL of the summary. * 'URL' - Wikipedia Title of the summary. * 'question' - '{"text":"...", "tokens":[...]}' for the question about the story. * 'answers' - List of '{"text":"...", "tokens":[...]}' for valid answers for the question. ### Data Splits The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split): Train: 32747, Valid: 3461, Test: 10557 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization Stories and movies scripts were downloaded from Project Gutenburg and a range of movie script repositories (mainly imsdb). #### Who are the source language producers? The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions. ### Annotations #### Annotation process Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information None Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is released under a Apache-2.0 License. ### Contributions Thanks to @ghomasHudson for adding this dataset.

f3120dcc1dc0e173c9c1285216ef5cb422631cd0

# Dataset Card for Narrative QA Manual ## 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:** [NarrativeQA Homepage](https://deepmind.com/research/open-source/narrativeqa) - **Repository:** [NarrativeQA Repo](https://github.com/deepmind/narrativeqa) - **Paper:** [The NarrativeQA Reading Comprehension Challenge](https://arxiv.org/pdf/1712.07040.pdf) - **Leaderboard:** - **Point of Contact:** [Tomáš Kočiský](mailto:[email protected]) [Jonathan Schwarz](mailto:[email protected]) [Phil Blunsom]([email protected]) [Chris Dyer]([email protected]) [Karl Moritz Hermann](mailto:[email protected]) [Gábor Melis](mailto:[email protected]) [Edward Grefenstette](mailto:[email protected]) ### Dataset Summary NarrativeQA Manual is an English-language dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. THIS DATASET REQUIRES A MANUALLY DOWNLOADED FILE! Because of a script in the original repository which downloads the stories from original URLs everytime, the links are sometimes broken or invalid. Therefore, you need to manually download the stories for this dataset using the script provided by the authors (https://github.com/deepmind/narrativeqa/blob/master/download_stories.sh). Running the shell script creates a folder named "tmp" in the root directory and downloads the stories there. This folder containing the stories can be used to load the dataset via `datasets.load_dataset("narrativeqa_manual", data_dir="<path/to/folder>")`. ### Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question. ### Languages English ## Dataset Structure ### Data Instances A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided. A typical example looks like this: ``` { "document": { "id": "23jncj2n3534563110", "kind": "movie", "url": "https://www.imsdb.com/Movie%20Scripts/Name%20of%20Movie.html", "file_size": 80473, "word_count": 41000, "start": "MOVIE screenplay by", "end": ". THE END", "summary": { "text": "Joe Bloggs begins his journey exploring...", "tokens": ["Joe", "Bloggs", "begins", "his", "journey", "exploring",...], "url": "http://en.wikipedia.org/wiki/Name_of_Movie", "title": "Name of Movie (film)" }, "text": "MOVIE screenplay by John Doe\nSCENE 1..." }, "question": { "text": "Where does Joe Bloggs live?", "tokens": ["Where", "does", "Joe", "Bloggs", "live", "?"], }, "answers": [ {"text": "At home", "tokens": ["At", "home"]}, {"text": "His house", "tokens": ["His", "house"]} ] } ``` ### Data Fields - `document.id` - Unique ID for the story. - `document.kind` - "movie" or "gutenberg" depending on the source of the story. - `document.url` - The URL where the story was downloaded from. - `document.file_size` - File size (in bytes) of the story. - `document.word_count` - Number of tokens in the story. - `document.start` - First 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.end` - Last 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.summary.text` - Text of the wikipedia summary of the story. - `document.summary.tokens` - Tokenized version of `document.summary.text`. - `document.summary.url` - Wikipedia URL of the summary. - `document.summary.title` - Wikipedia Title of the summary. - `question` - `{"text":"...", "tokens":[...]}` for the question about the story. - `answers` - List of `{"text":"...", "tokens":[...]}` for valid answers for the question. ### Data Splits The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split): | Train | Valid | Test | | ------ | ----- | ----- | | 32747 | 3461 | 10557 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Stories and movies scripts were downloaded from [Project Gutenburg](https://www.gutenberg.org) and a range of movie script repositories (mainly [imsdb](http://www.imsdb.com)). #### Who are the source language producers? The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions. ### Annotations #### Annotation process Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information None ## 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 The dataset is released under a [Apache-2.0 License](https://github.com/deepmind/narrativeqa/blob/master/LICENSE). ### Citation Information ``` @article{narrativeqa, author = {Tom\'a\v s Ko\v cisk\'y and Jonathan Schwarz and Phil Blunsom and Chris Dyer and Karl Moritz Hermann and G\'abor Melis and Edward Grefenstette}, title = {The {NarrativeQA} Reading Comprehension Challenge}, journal = {Transactions of the Association for Computational Linguistics}, url = {https://TBD}, volume = {TBD}, year = {2018}, pages = {TBD}, } ``` ### Contributions Thanks to [@rsanjaykamath](https://github.com/rsanjaykamath) for adding this dataset.

narrativeqa_manual

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": ["abstractive-qa"], "paperswithcode_id": "narrativeqa", "pretty_name": "NarrativeQA", "dataset_info": {"features": [{"name": "document", "struct": [{"name": "id", "dtype": "string"}, {"name": "kind", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "file_size", "dtype": "int32"}, {"name": "word_count", "dtype": "int32"}, {"name": "start", "dtype": "string"}, {"name": "end", "dtype": "string"}, {"name": "summary", "struct": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "text", "dtype": "string"}]}, {"name": "question", "struct": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}]}, {"name": "answers", "list": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 9115940054, "num_examples": 32747}, {"name": "test", "num_bytes": 2911702563, "num_examples": 10557}, {"name": "validation", "num_bytes": 968994186, "num_examples": 3461}], "download_size": 22638273, "dataset_size": 12996636803}}

2024-01-18T11:10:06+00:00

TAGS #task_categories-text2text-generation #task_ids-abstractive-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #arxiv-1712.07040 #region-us

Dataset Card for Narrative QA Manual ==================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: NarrativeQA Homepage * Repository: NarrativeQA Repo * Paper: The NarrativeQA Reading Comprehension Challenge * Leaderboard: * Point of Contact: Tomáš Kočiský Jonathan Schwarz Phil Blunsom Chris Dyer Karl Moritz Hermann Gábor Melis Edward Grefenstette ### Dataset Summary NarrativeQA Manual is an English-language dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. THIS DATASET REQUIRES A MANUALLY DOWNLOADED FILE! Because of a script in the original repository which downloads the stories from original URLs everytime, the links are sometimes broken or invalid. Therefore, you need to manually download the stories for this dataset using the script provided by the authors (URL Running the shell script creates a folder named "tmp" in the root directory and downloads the stories there. This folder containing the stories can be used to load the dataset via 'datasets.load\_dataset("narrativeqa\_manual", data\_dir="<path/to/folder>")'. ### Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question. ### Languages English Dataset Structure ----------------- ### Data Instances A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided. A typical example looks like this: ### Data Fields * 'URL' - Unique ID for the story. * 'URL' - "movie" or "gutenberg" depending on the source of the story. * 'URL' - The URL where the story was downloaded from. * 'document.file\_size' - File size (in bytes) of the story. * 'document.word\_count' - Number of tokens in the story. * 'URL' - First 3 tokens of the story. Used for verifying the story hasn't been modified. * 'URL' - Last 3 tokens of the story. Used for verifying the story hasn't been modified. * 'URL' - Text of the wikipedia summary of the story. * 'URL' - Tokenized version of 'URL'. * 'URL' - Wikipedia URL of the summary. * 'URL' - Wikipedia Title of the summary. * 'question' - '{"text":"...", "tokens":[...]}' for the question about the story. * 'answers' - List of '{"text":"...", "tokens":[...]}' for valid answers for the question. ### Data Splits The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split): Train: 32747, Valid: 3461, Test: 10557 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization Stories and movies scripts were downloaded from Project Gutenburg and a range of movie script repositories (mainly imsdb). #### Who are the source language producers? The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions. ### Annotations #### Annotation process Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information None Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is released under a Apache-2.0 License. ### Contributions Thanks to @rsanjaykamath for adding this dataset.

19ba7767b174ad046a84f46af056517a3910ee57

# Dataset Card for Natural 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://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset) - **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions) - **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/) - **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:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB ### Dataset Summary The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. ### Supported Tasks and Leaderboards [https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions) ### Languages en ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB An example of 'train' looks as follows. This is a toy example. ``` { "id": "797803103760793766", "document": { "title": "Google", "url": "http://www.wikipedia.org/Google", "html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>", "tokens":[ {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True}, {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False}, {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False}, {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False}, {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True}, {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True}, {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False}, {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False}, {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False}, {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False}, {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False}, {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False}, {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False}, {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True}, {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True}, {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False}, {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True}, {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True}, {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False}, {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True}, {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True}, {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True} ], }, "question" :{ "text": "who founded google", "tokens": ["who", "founded", "google"] }, "long_answer_candidates": [ {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True}, {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False}, {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False}, {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False} ], "annotations": [{ "id": "6782080525527814293", "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0}, "short_answers": [ {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"}, {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"} ], "yes_no_answer": -1 }] } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `document` a dictionary feature containing: - `title`: a `string` feature. - `url`: a `string` feature. - `html`: a `string` feature. - `tokens`: a dictionary feature containing: - `token`: a `string` feature. - `is_html`: a `bool` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `question`: a dictionary feature containing: - `text`: a `string` feature. - `tokens`: a `list` of `string` features. - `long_answer_candidates`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `top_level`: a `bool` feature. - `annotations`: a dictionary feature containing: - `id`: a `string` feature. - `long_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `candidate_index`: a `int64` feature. - `short_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `text`: a `string` feature. - `yes_no_answer`: a classification label, with possible values including `NO` (0), `YES` (1). ### Data Splits | name | train | validation | |---------|-------:|-----------:| | default | 307373 | 7830 | | dev | N/A | 7830 | ## 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 [Creative Commons Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{47761, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {Transactions of the Association of Computational Linguistics} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

natural_questions

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "natural-questions", "pretty_name": "Natural Questions", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "document", "struct": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html", "dtype": "string"}, {"name": "tokens", "sequence": [{"name": "token", "dtype": "string"}, {"name": "is_html", "dtype": "bool"}]}]}, {"name": "question", "struct": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}]}, {"name": "annotations", "sequence": [{"name": "id", "dtype": "string"}, {"name": "long_answer", "struct": [{"name": "start_token", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}]}, {"name": "short_answers", "sequence": [{"name": "start_token", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}, {"name": "yes_no_answer", "dtype": {"class_label": {"names": {"0": "NO", "1": "YES"}}}}, {"name": "long_answer_candidates", "sequence": [{"name": "start_token", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}, {"name": "top_label", "dtype": "bool"}]}]}], "splits": [{"name": "train", "num_bytes": 97445142568, "num_examples": 307373}, {"name": "validation", "num_bytes": 2353975312, "num_examples": 7830}], "download_size": 45069199013, "dataset_size": 99799117880}}

2024-01-18T11:10:09+00:00

TAGS #task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-sa-3.0 #region-us

Dataset Card for Natural Questions ================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Point of Contact: * Size of downloaded dataset files: 45.07 GB * Size of the generated dataset: 99.80 GB * Total amount of disk used: 144.87 GB ### Dataset Summary The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. ### Supported Tasks and Leaderboards URL ### Languages en Dataset Structure ----------------- ### Data Instances * Size of downloaded dataset files: 45.07 GB * Size of the generated dataset: 99.80 GB * Total amount of disk used: 144.87 GB An example of 'train' looks as follows. This is a toy example. ### Data Fields The data fields are the same among all splits. #### default * 'id': a 'string' feature. * 'document' a dictionary feature containing: + 'title': a 'string' feature. + 'url': a 'string' feature. + 'html': a 'string' feature. + 'tokens': a dictionary feature containing: - 'token': a 'string' feature. - 'is\_html': a 'bool' feature. - 'start\_byte': a 'int64' feature. - 'end\_byte': a 'int64' feature. * 'question': a dictionary feature containing: + 'text': a 'string' feature. + 'tokens': a 'list' of 'string' features. * 'long\_answer\_candidates': a dictionary feature containing: + 'start\_token': a 'int64' feature. + 'end\_token': a 'int64' feature. + 'start\_byte': a 'int64' feature. + 'end\_byte': a 'int64' feature. + 'top\_level': a 'bool' feature. * 'annotations': a dictionary feature containing: + 'id': a 'string' feature. + 'long\_answers': a dictionary feature containing: - 'start\_token': a 'int64' feature. - 'end\_token': a 'int64' feature. - 'start\_byte': a 'int64' feature. - 'end\_byte': a 'int64' feature. - 'candidate\_index': a 'int64' feature. + 'short\_answers': a dictionary feature containing: - 'start\_token': a 'int64' feature. - 'end\_token': a 'int64' feature. - 'start\_byte': a 'int64' feature. - 'end\_byte': a 'int64' feature. - 'text': a 'string' feature. + 'yes\_no\_answer': a classification label, with possible values including 'NO' (0), 'YES' (1). ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Creative Commons Attribution-ShareAlike 3.0 Unported. ### Contributions Thanks to @thomwolf, @lhoestq for adding this dataset.

de3b3cbb82b5d4f7903888ec1e8d099a3cbdb713

# Dataset Card for NCBI Disease ## Table of Contents - [Dataset Card for NCBI Disease](#dataset-card-for-ncbi-disease) - [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:** [NCBI](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease) - **Repository:** [Github](https://github.com/spyysalo/ncbi-disease) - **Paper:** [NCBI disease corpus: A resource for disease name recognition and concept normalization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655) - **Leaderboard:** [Named Entity Recognition on NCBI-disease](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease) - **Point of Contact:** [email]([email protected]) ### Dataset Summary This dataset contains the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. ### Supported Tasks and Leaderboards Named Entity Recognition: [Leaderboard](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease) ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset: ``` { 'tokens': ['Identification', 'of', 'APC2', ',', 'a', 'homologue', 'of', 'the', 'adenomatous', 'polyposis', 'coli', 'tumour', 'suppressor', '.'], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0], 'id': '0' } ``` ### 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 The data is split into a train (5433 instances), validation (924 instances) and test set (941 instances). ## Dataset Creation ### Curation Rationale The goal of the dataset consists on improving the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. ### Source Data #### Initial Data Collection and Normalization The dataset consists on abstracts extracted from PubMed. #### Who are the source language producers? The source language producers are the authors of publication abstracts hosted in PubMed. ### Annotations #### Annotation process Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. #### Who are the annotators? The annotator group consisted of 14 people with backgrounds in biomedical informatics research and experience in biomedical text corpus annotation. ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information. This dataset provides an annotated corpora that can be used to develop highly effective tools to automatically detect central biomedical concepts such as diseases. ### Discussion of Biases To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets. ### Other Known Limitations A handful of disease concepts were discovered that were not included in MEDIC. For those, we decided to include the appropriate OMIM identifiers. In addition, certain disease mentions were found to not be easily represented using the standard categorizations. Also, each PMID document was pre-annotated using the Inference Method developed for disease name normalization, which properly handles abbreviation recognition, robust string matching, etc. As such, human annotators were given the pre-annotated documents as a starting point and allowed to see each pre-annotation with a computed confidence. ## Additional Information ### Dataset Curators Rezarta Islamaj Doğan, Robert Leaman, Zhiyong Lu ### Licensing Information ``` PUBLIC DOMAIN NOTICE This work is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the authors' official duties as a United States Government employee and thus cannot be copyrighted within the United States. The data is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction. Although all reasonable efforts have been taken to ensure the accuracy and reliability of the data and its source code, the NLM and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using it. The NLM and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose. Please cite the authors in any work or product based on this material: An improved corpus of disease mentions in PubMed citations http://aclweb.org/anthology-new/W/W12/W12-2411.pdf NCBI Disease Corpus: A Resource for Disease Name Recognition and Normalization http://www.ncbi.nlm.nih.gov/pubmed/24393765 Disease Name Normalization with Pairwise Learning to Rank http://www.ncbi.nlm.nih.gov/pubmed/23969135 ``` ### Citation Information ``` @article{dougan2014ncbi, title={NCBI disease corpus: a resource for disease name recognition and concept normalization}, author={Do{\u{g}}an, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong}, journal={Journal of biomedical informatics}, volume={47}, pages={1--10}, year={2014}, publisher={Elsevier} } ``` ### Contributions Thanks to [@edugp](https://github.com/edugp) for adding this dataset.

ncbi_disease

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "ncbi-disease-1", "pretty_name": "NCBI Disease", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-Disease", "2": "I-Disease"}}}}], "config_name": "ncbi_disease", "splits": [{"name": "train", "num_bytes": 2355516, "num_examples": 5433}, {"name": "validation", "num_bytes": 413900, "num_examples": 924}, {"name": "test", "num_bytes": 422842, "num_examples": 941}], "download_size": 1546492, "dataset_size": 3192258}, "train-eval-index": [{"config": "ncbi_disease", "task": "token-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"tokens": "text", "ner_tags": "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"}}]}]}

2024-01-18T11:10:11+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us

# Dataset Card for NCBI Disease ## Table of Contents - Dataset Card for NCBI Disease - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Initial Data Collection and Normalization - Who are the source language producers? - Annotations - Annotation process - Who are the annotators? - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: NCBI - Repository: Github - Paper: NCBI disease corpus: A resource for disease name recognition and concept normalization - Leaderboard: Named Entity Recognition on NCBI-disease - Point of Contact: email ### Dataset Summary This dataset contains the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. ### Supported Tasks and Leaderboards Named Entity Recognition: Leaderboard ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. ## Dataset Structure ### Data Instances Instances of the dataset contain an array of 'tokens', 'ner_tags' and an 'id'. An example of an instance of the dataset: ### 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 The data is split into a train (5433 instances), validation (924 instances) and test set (941 instances). ## Dataset Creation ### Curation Rationale The goal of the dataset consists on improving the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. ### Source Data #### Initial Data Collection and Normalization The dataset consists on abstracts extracted from PubMed. #### Who are the source language producers? The source language producers are the authors of publication abstracts hosted in PubMed. ### Annotations #### Annotation process Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. #### Who are the annotators? The annotator group consisted of 14 people with backgrounds in biomedical informatics research and experience in biomedical text corpus annotation. ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information. This dataset provides an annotated corpora that can be used to develop highly effective tools to automatically detect central biomedical concepts such as diseases. ### Discussion of Biases To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets. ### Other Known Limitations A handful of disease concepts were discovered that were not included in MEDIC. For those, we decided to include the appropriate OMIM identifiers. In addition, certain disease mentions were found to not be easily represented using the standard categorizations. Also, each PMID document was pre-annotated using the Inference Method developed for disease name normalization, which properly handles abbreviation recognition, robust string matching, etc. As such, human annotators were given the pre-annotated documents as a starting point and allowed to see each pre-annotation with a computed confidence. ## Additional Information ### Dataset Curators Rezarta Islamaj Doğan, Robert Leaman, Zhiyong Lu ### Licensing Information ### Contributions Thanks to @edugp for adding this dataset.

0df99f2a81059e8a5785f9e80e70c8474faa564e

# Dataset Card for NCHLT ## 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:** [link](https://repo.sadilar.org/handle/20.500.12185/7/discover?filtertype_0=database&filtertype_1=title&filter_relational_operator_1=contains&filter_relational_operator_0=equals&filter_1=&filter_0=Monolingual+Text+Corpora%3A+Annotated&filtertype=project&filter_relational_operator=equals&filter=NCHLT+Text+II) - **Repository:** []() - **Paper:** []() - **Leaderboard:** []() - **Point of Contact:** []() ### Dataset Summary The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context. ### 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 [More Information Needed] ## Additional Information ### Dataset Curators [email protected] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{eiselen2014developing, title={Developing Text Resources for Ten South African Languages.}, author={Eiselen, Roald and Puttkammer, Martin J}, booktitle={LREC}, pages={3698--3703}, year={2014} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.

nchlt

2022-03-02T23:29:22+00:00

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2024-01-18T11:10:13+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-Afrikaans #language-South Ndebele #language-Pedi #language-Swati #language-Tswana #language-Tsonga #language-Venda #language-Xhosa #language-Zulu #license-cc-by-2.5 #region-us

# Dataset Card for NCHLT ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: link - Repository: []() - Paper: []() - Leaderboard: []() - Point of Contact: []() ### Dataset Summary The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators Martin.Puttkammer@URL ### Licensing Information ### Contributions Thanks to @Narsil for adding this dataset.

8240676886b41a51dccab02d1eeb6bc9fe5d7ee5

# Dataset Card for NCSLGR ## 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.bu.edu/asllrp/ncslgr.html - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A small corpus of American Sign Language (ASL) video data from native signers, annotated with non-manual features. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages - American Sign Language - English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - eaf: path to an ELAN annotation file - videos: sequence of strings to video paths - sentences: sequence of parallel sentences - gloss: American Sign Language gloss annotations - text: English text ### Data Splits None ## 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 @misc{dataset:databases2007volumes, title={Volumes 2--7}, author={Databases, NCSLGR}, year={2007}, publisher={American Sign Language Linguistic Research Project (Distributed on CD-ROM~…} } ``` ### Contributions Thanks to [@AmitMY](https://github.com/AmitMY) for adding this dataset.

ncslgr

2022-03-02T23:29:22+00:00

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2024-01-18T11:10:15+00:00

TAGS #task_categories-translation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-translation #size_categories-n<1K #source_datasets-original #language-American Sign Language #language-English #license-mit #region-us

# Dataset Card for NCSLGR ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary A small corpus of American Sign Language (ASL) video data from native signers, annotated with non-manual features. ### Supported Tasks and Leaderboards ### Languages - American Sign Language - English ## Dataset Structure ### Data Instances ### Data Fields - eaf: path to an ELAN annotation file - videos: sequence of strings to video paths - sentences: sequence of parallel sentences - gloss: American Sign Language gloss annotations - text: English text ### Data Splits None ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @AmitMY for adding this dataset.