Fix license/citation information of squadshifts dataset card (#5054)

* Update license information in squadshifts dataset

* Update citation information in squadshifts dataset

* Update metadata JSON

Commit from https://github.com/huggingface/datasets/commit/61258078bcb80113f877e84745ed85ec85bb11d2

README.md

@@ -7,7 +7,7 @@ language_creators:
 - crowdsourced
 - found
 license:
-- unknown
+- cc-by-4.0
 multilinguality:
 - monolingual
 pretty_name: SQuAD-shifts
@@ -256,21 +256,27 @@ The data fields are the same among all splits.
 
 ### Licensing Information
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+All the datasets are distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) license.
 
 ### Citation Information
 
 ```
-@inproceedings{miller2020effect,
-  author = {J. Miller and K. Krauth and B. Recht and L. Schmidt},
-  booktitle = {International Conference on Machine Learning (ICML)},
+@InProceedings{pmlr-v119-miller20a,
   title = {The Effect of Natural Distribution Shift on Question Answering Models},
+  author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
+  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
+  pages = {6905--6916},
   year = {2020},
+  editor = {III, Hal Daumé and Singh, Aarti},
+  volume = {119},
+  series = {Proceedings of Machine Learning Research},
+  month = {13--18 Jul},
+  publisher = {PMLR},
+  pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},
+  url = {https://proceedings.mlr.press/v119/miller20a.html},
 }
-
 ```
 
-
 ### Contributions
 
 Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@millerjohnp](https://github.com/millerjohnp), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

dataset_infos.json

@@ -1 +1 @@
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Schmidt},\n  booktitle = {International Conference on Machine Learning (ICML)},\n  title = {The Effect of Natural Distribution Shift on Question Answering Models},\n  year = {2020},\n}\n", "homepage": "https://modestyachts.github.io/squadshifts-website/index.html", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "squad_shifts", "config_name": "new_wiki", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 7861360, "num_examples": 7938, "dataset_name": "squad_shifts"}}, "download_checksums": {"https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/new_wiki_v1.0.json": {"num_bytes": 3607393, "checksum": "1d50a0ca4cdab88d038b52ea8a2d0510af6038d7f41a0a256b9659237769d0ee"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/nyt_v1.0.json": {"num_bytes": 4550058, "checksum": "fd55ae252cc735a695fab4473d4cd605b154183ac3b3eb63068f53cfc86f36d6"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/reddit_v1.0.json": {"num_bytes": 4170330, "checksum": "b4ac346dbf1816fdec80fdcf91d06a4b754fa51e47960fa8614a94f37ea42a4b"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/amazon_reviews_v1.0.json": {"num_bytes": 4177842, "checksum": "9db7bdee84a91f0fb80b9e74f365042fbbb217cb8e0a7e73bb071245472589b6"}}, "download_size": 16505623, "dataset_size": 7861360, "size_in_bytes": 24366983}, "nyt": {"description": "SquadShifts consists of four new test sets for the Stanford Question Answering Dataset (SQuAD) from four different domains: Wikipedia articles, New York \\ \nTimes articles, Reddit comments, and Amazon product reviews. 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+{"new_wiki": {"description": "\\\nSquadShifts consists of four new test sets for the Stanford Question Answering \\\nDataset (SQuAD) from four different domains: Wikipedia articles, New York \\\nTimes articles, Reddit comments, and Amazon product reviews. Each dataset \\\nwas generated using the same data generating pipeline, Amazon Mechanical \\\nTurk interface, and data cleaning code as the original SQuAD v1.1 dataset. \\\nThe \"new-wikipedia\" dataset measures overfitting on the original SQuAD v1.1 \\\ndataset.  The \"new-york-times\", \"reddit\", and \"amazon\" datasets measure \\\nrobustness to natural distribution shifts. We encourage SQuAD model developers \\\nto also evaluate their methods on these new datasets! \\\n", "citation": "@InProceedings{pmlr-v119-miller20a,\n  title = {The Effect of Natural Distribution Shift on Question Answering Models},\n  author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},\n  booktitle = {Proceedings of the 37th International Conference on Machine Learning},\n  pages = {6905--6916},\n  year = {2020},\n  editor = {III, Hal Daum\u00e9 and Singh, Aarti},\n  volume = {119},\n  series = {Proceedings of Machine Learning Research},\n  month = {13--18 Jul},\n  publisher = {PMLR},\n  pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},\n  url = {https://proceedings.mlr.press/v119/miller20a.html},\n}\n", "homepage": "https://modestyachts.github.io/squadshifts-website/index.html", "license": "CC-BY-4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "squadshifts", "config_name": "new_wiki", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 7865203, "num_examples": 7938, "dataset_name": "squadshifts"}}, "download_checksums": {"https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/new_wiki_v1.0.json": {"num_bytes": 3607393, "checksum": "1d50a0ca4cdab88d038b52ea8a2d0510af6038d7f41a0a256b9659237769d0ee"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/nyt_v1.0.json": {"num_bytes": 4550058, "checksum": "fd55ae252cc735a695fab4473d4cd605b154183ac3b3eb63068f53cfc86f36d6"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/reddit_v1.0.json": {"num_bytes": 4170330, "checksum": "b4ac346dbf1816fdec80fdcf91d06a4b754fa51e47960fa8614a94f37ea42a4b"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/amazon_reviews_v1.0.json": {"num_bytes": 4177842, "checksum": "9db7bdee84a91f0fb80b9e74f365042fbbb217cb8e0a7e73bb071245472589b6"}}, "download_size": 16505623, "post_processing_size": null, "dataset_size": 7865203, "size_in_bytes": 24370826}, "nyt": {"description": "\\\nSquadShifts consists of four new test sets for the Stanford Question Answering \\\nDataset (SQuAD) from four different domains: Wikipedia articles, New York \\\nTimes articles, Reddit comments, and Amazon product reviews. 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We encourage SQuAD model developers \\\nto also evaluate their methods on these new datasets! \\\n", "citation": "@InProceedings{pmlr-v119-miller20a,\n  title = {The Effect of Natural Distribution Shift on Question Answering Models},\n  author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},\n  booktitle = {Proceedings of the 37th International Conference on Machine Learning},\n  pages = {6905--6916},\n  year = {2020},\n  editor = {III, Hal Daum\u00e9 and Singh, Aarti},\n  volume = {119},\n  series = {Proceedings of Machine Learning Research},\n  month = {13--18 Jul},\n  publisher = {PMLR},\n  pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},\n  url = {https://proceedings.mlr.press/v119/miller20a.html},\n}\n", "homepage": "https://modestyachts.github.io/squadshifts-website/index.html", "license": "CC-BY-4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "squadshifts", "config_name": "nyt", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 10792550, "num_examples": 10065, "dataset_name": "squadshifts"}}, "download_checksums": {"https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/new_wiki_v1.0.json": {"num_bytes": 3607393, "checksum": "1d50a0ca4cdab88d038b52ea8a2d0510af6038d7f41a0a256b9659237769d0ee"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/nyt_v1.0.json": {"num_bytes": 4550058, "checksum": "fd55ae252cc735a695fab4473d4cd605b154183ac3b3eb63068f53cfc86f36d6"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/reddit_v1.0.json": {"num_bytes": 4170330, "checksum": "b4ac346dbf1816fdec80fdcf91d06a4b754fa51e47960fa8614a94f37ea42a4b"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/amazon_reviews_v1.0.json": {"num_bytes": 4177842, "checksum": "9db7bdee84a91f0fb80b9e74f365042fbbb217cb8e0a7e73bb071245472589b6"}}, "download_size": 16505623, "post_processing_size": null, "dataset_size": 10792550, "size_in_bytes": 27298173}, "reddit": {"description": "\\\nSquadShifts consists of four new test sets for the Stanford Question Answering \\\nDataset (SQuAD) from four different domains: Wikipedia articles, New York \\\nTimes articles, Reddit comments, and Amazon product reviews. 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We encourage SQuAD model developers \\\nto also evaluate their methods on these new datasets! \\\n", "citation": "@InProceedings{pmlr-v119-miller20a,\n  title = {The Effect of Natural Distribution Shift on Question Answering Models},\n  author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},\n  booktitle = {Proceedings of the 37th International Conference on Machine Learning},\n  pages = {6905--6916},\n  year = {2020},\n  editor = {III, Hal Daum\u00e9 and Singh, Aarti},\n  volume = {119},\n  series = {Proceedings of Machine Learning Research},\n  month = {13--18 Jul},\n  publisher = {PMLR},\n  pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},\n  url = {https://proceedings.mlr.press/v119/miller20a.html},\n}\n", "homepage": "https://modestyachts.github.io/squadshifts-website/index.html", "license": "CC-BY-4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "squadshifts", "config_name": "reddit", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 9473946, "num_examples": 9803, "dataset_name": "squadshifts"}}, "download_checksums": {"https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/new_wiki_v1.0.json": {"num_bytes": 3607393, "checksum": "1d50a0ca4cdab88d038b52ea8a2d0510af6038d7f41a0a256b9659237769d0ee"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/nyt_v1.0.json": {"num_bytes": 4550058, "checksum": "fd55ae252cc735a695fab4473d4cd605b154183ac3b3eb63068f53cfc86f36d6"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/reddit_v1.0.json": {"num_bytes": 4170330, "checksum": "b4ac346dbf1816fdec80fdcf91d06a4b754fa51e47960fa8614a94f37ea42a4b"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/amazon_reviews_v1.0.json": {"num_bytes": 4177842, "checksum": "9db7bdee84a91f0fb80b9e74f365042fbbb217cb8e0a7e73bb071245472589b6"}}, "download_size": 16505623, "post_processing_size": null, "dataset_size": 9473946, "size_in_bytes": 25979569}, "amazon": {"description": "\\\nSquadShifts consists of four new test sets for the Stanford Question Answering \\\nDataset (SQuAD) from four different domains: Wikipedia articles, New York \\\nTimes articles, Reddit comments, and Amazon product reviews. Each dataset \\\nwas generated using the same data generating pipeline, Amazon Mechanical \\\nTurk interface, and data cleaning code as the original SQuAD v1.1 dataset. \\\nThe \"new-wikipedia\" dataset measures overfitting on the original SQuAD v1.1 \\\ndataset.  The \"new-york-times\", \"reddit\", and \"amazon\" datasets measure \\\nrobustness to natural distribution shifts. We encourage SQuAD model developers \\\nto also evaluate their methods on these new datasets! \\\n", "citation": "@InProceedings{pmlr-v119-miller20a,\n  title = {The Effect of Natural Distribution Shift on Question Answering Models},\n  author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},\n  booktitle = {Proceedings of the 37th International Conference on Machine Learning},\n  pages = {6905--6916},\n  year = {2020},\n  editor = {III, Hal Daum\u00e9 and Singh, Aarti},\n  volume = {119},\n  series = {Proceedings of Machine Learning Research},\n  month = {13--18 Jul},\n  publisher = {PMLR},\n  pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},\n  url = {https://proceedings.mlr.press/v119/miller20a.html},\n}\n", "homepage": "https://modestyachts.github.io/squadshifts-website/index.html", "license": "CC-BY-4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "squadshifts", "config_name": "amazon", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 9445004, "num_examples": 9885, "dataset_name": "squadshifts"}}, "download_checksums": {"https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/new_wiki_v1.0.json": {"num_bytes": 3607393, "checksum": "1d50a0ca4cdab88d038b52ea8a2d0510af6038d7f41a0a256b9659237769d0ee"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/nyt_v1.0.json": {"num_bytes": 4550058, "checksum": "fd55ae252cc735a695fab4473d4cd605b154183ac3b3eb63068f53cfc86f36d6"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/reddit_v1.0.json": {"num_bytes": 4170330, "checksum": "b4ac346dbf1816fdec80fdcf91d06a4b754fa51e47960fa8614a94f37ea42a4b"}, "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/amazon_reviews_v1.0.json": {"num_bytes": 4177842, "checksum": "9db7bdee84a91f0fb80b9e74f365042fbbb217cb8e0a7e73bb071245472589b6"}}, "download_size": 16505623, "post_processing_size": null, "dataset_size": 9445004, "size_in_bytes": 25950627}}

squadshifts.py

@@ -25,15 +25,6 @@ import datasets
 logger = datasets.logging.get_logger(__name__)
 
 
-_CITATION = """\
-@inproceedings{miller2020effect,
-  author = {J. Miller and K. Krauth and B. Recht and L. Schmidt},
-  booktitle = {International Conference on Machine Learning (ICML)},
-  title = {The Effect of Natural Distribution Shift on Question Answering Models},
-  year = {2020},
-}
-"""
-
 _DESCRIPTION = r"""\
 SquadShifts consists of four new test sets for the Stanford Question Answering \
 Dataset (SQuAD) from four different domains: Wikipedia articles, New York \
@@ -46,6 +37,25 @@ robustness to natural distribution shifts. We encourage SQuAD model developers \
 to also evaluate their methods on these new datasets! \
 """
 
+_LICENSE = "CC-BY-4.0"
+
+_CITATION = """\
+@InProceedings{pmlr-v119-miller20a,
+  title = {The Effect of Natural Distribution Shift on Question Answering Models},
+  author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
+  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
+  pages = {6905--6916},
+  year = {2020},
+  editor = {III, Hal Daumé and Singh, Aarti},
+  volume = {119},
+  series = {Proceedings of Machine Learning Research},
+  month = {13--18 Jul},
+  publisher = {PMLR},
+  pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},
+  url = {https://proceedings.mlr.press/v119/miller20a.html},
+}
+"""
+
 _URL = "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/"
 _URLS = {
     "new_wiki": _URL + "new_wiki_v1.0.json",
@@ -110,10 +120,8 @@ class SquadShifts(datasets.GeneratorBasedBuilder):
                     ),
                 }
             ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
             homepage="https://modestyachts.github.io/squadshifts-website/index.html",
+            license=_LICENSE,
             citation=_CITATION,
         )