Add license and citation information to cosmos_qa dataset (#4913)

* Add license information to cosmos_qa dataset card

* Add license to script

* Regenerate metadata JSON

* Update citation information

* Fix style

Commit from https://github.com/huggingface/datasets/commit/c47e6a987e838445bd5d8f84cbb17ab55df65535

README.md

@@ -6,7 +6,7 @@ language:
 language_creators:
 - found
 license:
-- unknown
+- cc-by-4.0
 multilinguality:
 - monolingual
 pretty_name: CosmosQA
@@ -167,17 +167,25 @@ 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)
+As reported via email by Yejin Choi, the dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
 
 ### Citation Information
 
 ```
-@inproceedings{cosmos,
-    title={COSMOS QA: Machine Reading Comprehension
-    with Contextual Commonsense Reasoning},
-    author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
-    booktitle ={arXiv:1909.00277v2},
-    year={2019}
+@inproceedings{huang-etal-2019-cosmos,
+    title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
+    author = "Huang, Lifu  and
+      Le Bras, Ronan  and
+      Bhagavatula, Chandra  and
+      Choi, Yejin",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D19-1243",
+    doi = "10.18653/v1/D19-1243",
+    pages = "2391--2401",
 }
 ```
 

cosmos_qa.py

@@ -1,4 +1,4 @@
-"""TODO(cosmos_qa): Add a description here."""
+"""Cosmos QA dataset."""
 
 
 import csv
@@ -7,22 +7,32 @@ import json
 import datasets
 
 
-# TODO(cosmos_qa): BibTeX citation
-_CITATION = """\
-@inproceedings{cosmos,
-    title={COSMOS QA: Machine Reading Comprehension
-    with Contextual Commonsense Reasoning},
-    author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
-    booktitle ={arXiv:1909.00277v2},
-    year={2019}
-}
-"""
+_HOMEPAGE = "https://wilburone.github.io/cosmos/"
 
-# TODO(cosmos_qa):
 _DESCRIPTION = """\
 Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context
 """
 
+_CITATION = """\
+@inproceedings{huang-etal-2019-cosmos,
+    title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
+    author = "Huang, Lifu  and
+      Le Bras, Ronan  and
+      Bhagavatula, Chandra  and
+      Choi, Yejin",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D19-1243",
+    doi = "10.18653/v1/D19-1243",
+    pages = "2391--2401",
+}
+"""
+
+_LICENSE = "CC BY 4.0"
+
 _URL = "https://github.com/wilburOne/cosmosqa/raw/master/data/"
 _URLS = {
     "train": _URL + "train.csv",
@@ -32,17 +42,13 @@ _URLS = {
 
 
 class CosmosQa(datasets.GeneratorBasedBuilder):
-    """TODO(cosmos_qa): Short description of my dataset."""
+    """Cosmos QA dataset."""
 
-    # TODO(cosmos_qa): Set up version.
     VERSION = datasets.Version("0.1.0")
 
     def _info(self):
-        # TODO(cosmos_qa): Specifies the datasets.DatasetInfo object
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
             features=datasets.Features(
                 {
                     "id": datasets.Value("string"),
@@ -52,47 +58,35 @@ class CosmosQa(datasets.GeneratorBasedBuilder):
                     "answer1": datasets.Value("string"),
                     "answer2": datasets.Value("string"),
                     "answer3": datasets.Value("string"),
-                    "label": datasets.Value("int32")
-                    # These are the features of your dataset like images, labels ...
+                    "label": datasets.Value("int32"),
                 }
             ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://wilburone.github.io/cosmos/",
+            homepage=_HOMEPAGE,
             citation=_CITATION,
+            license=_LICENSE,
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(cosmos_qa): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
         urls_to_download = _URLS
         dl_dir = dl_manager.download_and_extract(urls_to_download)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
             ),
         ]
 
     def _generate_examples(self, filepath, split):
         """Yields examples."""
-        # TODO(cosmos_qa): Yields (key, example) tuples from the dataset
         with open(filepath, encoding="utf-8") as f:
             if split == "test":
                 for id_, row in enumerate(f):

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context\n", "citation": "@inproceedings{cosmos,\n    title={COSMOS QA: Machine Reading Comprehension\n    with Contextual Commonsense Reasoning},\n    author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},\n    booktitle ={arXiv:1909.00277v2},\n    year={2019}\n}\n", "homepage": "https://wilburone.github.io/cosmos/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer0": {"dtype": "string", "id": null, "_type": "Value"}, "answer1": {"dtype": "string", "id": null, "_type": "Value"}, "answer2": {"dtype": "string", "id": null, "_type": "Value"}, "answer3": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "cosmos_qa", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 5128447, "num_examples": 6963, "dataset_name": "cosmos_qa"}, "train": {"name": "train", "num_bytes": 17185126, "num_examples": 25262, "dataset_name": "cosmos_qa"}, "validation": {"name": "validation", "num_bytes": 2189979, "num_examples": 2985, "dataset_name": "cosmos_qa"}}, "download_checksums": {"https://github.com/wilburOne/cosmosqa/raw/master/data/train.csv": {"num_bytes": 16660449, "checksum": "d8d5ca1f9f6534b6530550718591af89372d976a8fc419360fab4158dee4d0b2"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/test.jsonl": {"num_bytes": 5610681, "checksum": "70005196dc2588b95de34f1657b25e2c1a4810cfe55b5bb0c0e15580c37b3ed0"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/valid.csv": {"num_bytes": 2128345, "checksum": "a6a94fc1463ca82bb10f98ef68ed535405e6f5c36e044ff8e136b5c19dea63f3"}}, "download_size": 24399475, "dataset_size": 24503552, "size_in_bytes": 48903027}}
+{"default": {"description": "Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context\n", "citation": "@inproceedings{huang-etal-2019-cosmos,\n    title = \"Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning\",\n    author = \"Huang, Lifu  and\n      Le Bras, Ronan  and\n      Bhagavatula, Chandra  and\n      Choi, Yejin\",\n    booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)\",\n    month = nov,\n    year = \"2019\",\n    address = \"Hong Kong, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D19-1243\",\n    doi = \"10.18653/v1/D19-1243\",\n    pages = \"2391--2401\",\n}\n", "homepage": "https://wilburone.github.io/cosmos/", "license": "CC BY 4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer0": {"dtype": "string", "id": null, "_type": "Value"}, "answer1": {"dtype": "string", "id": null, "_type": "Value"}, "answer2": {"dtype": "string", "id": null, "_type": "Value"}, "answer3": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cosmos_qa", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17159918, "num_examples": 25262, "dataset_name": "cosmos_qa"}, "test": {"name": "test", "num_bytes": 5121479, "num_examples": 6963, "dataset_name": "cosmos_qa"}, "validation": {"name": "validation", "num_bytes": 2186987, "num_examples": 2985, "dataset_name": "cosmos_qa"}}, "download_checksums": {"https://github.com/wilburOne/cosmosqa/raw/master/data/train.csv": {"num_bytes": 16660449, "checksum": "d8d5ca1f9f6534b6530550718591af89372d976a8fc419360fab4158dee4d0b2"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/test.jsonl": {"num_bytes": 5610681, "checksum": "70005196dc2588b95de34f1657b25e2c1a4810cfe55b5bb0c0e15580c37b3ed0"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/valid.csv": {"num_bytes": 2128345, "checksum": "a6a94fc1463ca82bb10f98ef68ed535405e6f5c36e044ff8e136b5c19dea63f3"}}, "download_size": 24399475, "post_processing_size": null, "dataset_size": 24468384, "size_in_bytes": 48867859}}