Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- conditional-text-generation-other-meaning-representtion-to-text
+---
+
+# Dataset Card for the Cleaned Version of the E2E Dataset
+
+## 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:** [homepage](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
+- **Repository:** [repository](https://github.com/tuetschek/e2e-dataset/)
+- **Paper:** [paper](https://arxiv.org/abs/1706.09254)
+- **Leaderboard:** [leaderboard](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
+
+
+### Dataset Summary
+
+An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:
+
+The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
+The E2E dataset poses new challenges:
+(1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
+(2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
+
+E2E is released in the following paper where you can find more details and baseline results:
+https://arxiv.org/abs/1706.09254
+
+### Supported Tasks and Leaderboards
+
+- `conditional-text-generation-other-meaning-representtion-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider).
+
+
+This task has an inactive leaderboard which can be found [here](http://www.macs.hw.ac.uk/InteractionLab/E2E/) and ranks models based on the metrics above.
+
+### Languages
+
+The dataset is in english (en).
+
+## Dataset Structure
+
+### Data Instances
+
+Example of one instance:
+
+```
+{'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating.  Prices start at £30.',
+ 'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'}
+```
+
+
+### Data Fields
+
+- `human_reference`: string, the text is natural language that describes the different characteristics in the meaning representation
+- `meaning_representation`: list of slots and values to generate a description from
+
+Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values.
+
+### Data Splits
+
+The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct.
+
+|                            | Tain   | Valid | Test |
+| -----                      | ------ | ----- | ---- |
+| N. Instances               | 33525  | 4299  | 4693 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016).
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs.
+
+#### 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{dusek.etal2020:csl,
+  title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
+  author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
+  year = {2020},
+  month = jan,
+  volume = {59},
+  pages = {123--156},
+  doi = {10.1016/j.csl.2019.06.009},
+  archivePrefix = {arXiv},
+  eprint = {1901.11528},
+  eprinttype = {arxiv},
+  journal = {Computer Speech \& Language}
+```

dataset_infos.json

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+{"default": {"description": "An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:\n\nOnd\u0159ej Du\u0161ek, David M. Howcroft, and Verena Rieser (2019): Semantic Noise Matters for Neural Natural Language Generation. In INLG, Tokyo, Japan.\n", "citation": "@inproceedings{dusek-etal-2019-semantic,\n    title = \"Semantic Noise Matters for Neural Natural Language Generation\",\n    author = \"Du{\u000b{s}}ek, Ond{\u000b{r}}ej  and\n      Howcroft, David M.  and\n      Rieser, Verena\",\n    booktitle = \"Proceedings of the 12th International Conference on Natural Language Generation\",\n    month = oct # \"{--}\" # nov,\n    year = \"2019\",\n    address = \"Tokyo, Japan\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W19-8652\",\n    doi = \"10.18653/v1/W19-8652\",\n    pages = \"421--426\",\n    abstract = \"Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97{\\%}, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.\",\n}\n", "homepage": "https://github.com/tuetschek/e2e-cleaning", "license": "", "features": {"meaning_representation": {"dtype": "string", "id": null, "_type": "Value"}, "human_reference": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "e2e_nlg_cleaned", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7474936, "num_examples": 33525, "dataset_name": "e2e_nlg_cleaned"}, "validation": {"name": "validation", "num_bytes": 1056527, "num_examples": 4299, "dataset_name": "e2e_nlg_cleaned"}, "test": {"name": "test", "num_bytes": 1262597, "num_examples": 4693, "dataset_name": "e2e_nlg_cleaned"}}, "download_checksums": {"https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/train-fixed.no-ol.csv": {"num_bytes": 11100744, "checksum": "12a4f59ec85ddd2586244aaf166f65d1b8cd468b6227e6620108baf118d5b325"}, "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/devel-fixed.no-ol.csv": {"num_bytes": 1581285, "checksum": "bb88df2565826a463f96e93a5ab69a8c6460de54f2e68179eb94f0019f430d4d"}, "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/test-fixed.csv": {"num_bytes": 1915378, "checksum": "99b43c2769a09d62fc5d37dcffaa59d4092bcffdc611f226258681df61269b17"}}, "download_size": 14597407, "post_processing_size": null, "dataset_size": 9794060, "size_in_bytes": 24391467}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2db99952ca11f978e927fcfeec65c24185c7df5a160956d2cc6525f79e317811
+size 1338

e2e_nlg_cleaned.py

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+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""E2E Dataset: New Challenges For End-to-End Generation, cleaned version"""
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{dusek-etal-2019-semantic,
+    title = "Semantic Noise Matters for Neural Natural Language Generation",
+    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
+      Howcroft, David M.  and
+      Rieser, Verena",
+    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
+    month = oct # "{--}" # nov,
+    year = "2019",
+    address = "Tokyo, Japan",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/W19-8652",
+    doi = "10.18653/v1/W19-8652",
+    pages = "421--426"
+}
+"""
+
+_DESCRIPTION = """\
+An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:
+
+Ondřej Dušek, David M. Howcroft, and Verena Rieser (2019): Semantic Noise Matters for Neural Natural Language Generation. In INLG, Tokyo, Japan.
+"""
+
+_URL = "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/"
+_TRAINING_FILE = "train-fixed.no-ol.csv"
+_DEV_FILE = "devel-fixed.no-ol.csv"
+_TEST_FILE = "test-fixed.csv"
+
+_URLS = {
+    "train": f"{_URL}{_TRAINING_FILE}",
+    "dev": f"{_URL}{_DEV_FILE}",
+    "test": f"{_URL}{_TEST_FILE}",
+}
+
+
+class E2eNLGCleaned(datasets.GeneratorBasedBuilder):
+    """E2E dataset, cleaned version."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "meaning_representation": datasets.Value("string"),
+                    "human_reference": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/tuetschek/e2e-cleaning",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for example_idx, example in enumerate(reader):
+                yield example_idx, {
+                    "meaning_representation": example["mr"],
+                    "human_reference": example["ref"],
+                }