Fix missing tags in dataset cards (#4931)

Commit from https://github.com/huggingface/datasets/commit/303e906e6c2aeb6821af23264ffb2e653a65aa86

README.md

@@ -1,8 +1,28 @@
 ---
+annotations_creators:
+- crowdsourced
 language:
 - en
+language_creators:
+- found
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: "CoQA: Conversational Question Answering Challenge"
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|race
+- extended|cnn_dailymail
+- extended|wikipedia
+- extended|other
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+- question-answering-other-conversational-qa
 paperswithcode_id: coqa
-pretty_name: Conversational Question Answering Challenge
 ---
 
 # Dataset Card for "coqa"
@@ -34,16 +54,18 @@ pretty_name: Conversational Question Answering Challenge
 ## Dataset Description
 
 - **Homepage:** [https://stanfordnlp.github.io/coqa/](https://stanfordnlp.github.io/coqa/)
-- **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)
+- **Repository:** https://github.com/stanfordnlp/coqa-baselines
+- **Paper:** [CoQA: A Conversational Question Answering Challenge](https://arxiv.org/abs/1808.07042)
+- **Point of Contact:** [Google Group](https://groups.google.com/forum/#!forum/coqa), [Siva Reddy](mailto:siva.reddy@mila.quebec), [Danqi Chen](mailto:danqic@cs.princeton.edu)
 - **Size of downloaded dataset files:** 55.40 MB
 - **Size of the generated dataset:** 18.35 MB
 - **Total amount of disk used:** 73.75 MB
 
 ### Dataset Summary
 
-CoQA: A Conversational Question Answering Challenge
+CoQA is a large-scale dataset for building Conversational Question Answering systems.
+
+Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage.
 
 ### Supported Tasks and Leaderboards
 
@@ -146,22 +168,31 @@ 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)
+CoQA contains passages from seven domains. We make five of these public under the following licenses:
+- Literature and Wikipedia passages are shared under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.
+- Children's stories are collected from [MCTest](https://www.microsoft.com/en-us/research/publication/mctest-challenge-dataset-open-domain-machine-comprehension-text/) which comes with [MSR-LA](https://github.com/mcobzarenco/mctest/blob/master/data/MCTest/LICENSE.pdf) license.
+- Middle/High school exam passages are collected from [RACE](https://arxiv.org/abs/1704.04683) which comes with its [own](http://www.cs.cmu.edu/~glai1/data/race/) license.
+- News passages are collected from the [DeepMind CNN dataset](https://arxiv.org/abs/1506.03340) which comes with [Apache](https://github.com/deepmind/rc-data/blob/master/LICENSE) license. 
 
 ### Citation Information
 
 ```
-@InProceedings{SivaAndAl:Coca,
-       author = {Siva, Reddy and Danqi, Chen and  Christopher D., Manning},
-        title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
-      journal = { arXiv},
-         year = {2018},
-
+@article{reddy-etal-2019-coqa,
+    title = "{C}o{QA}: A Conversational Question Answering Challenge",
+    author = "Reddy, Siva  and
+      Chen, Danqi  and
+      Manning, Christopher D.",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "7",
+    year = "2019",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/Q19-1016",
+    doi = "10.1162/tacl_a_00266",
+    pages = "249--266",
 }
-
 ```
 
-
 ### Contributions
 
 Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@ojasaar](https://github.com/ojasaar), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

coqa.py

@@ -1,4 +1,4 @@
-"""TODO(coqa): Add a description here."""
+"""CoQA dataset."""
 
 
 import json
@@ -6,18 +6,25 @@ import json
 import datasets
 
 
-# TODO(coqa): BibTeX citation
-_CITATION = """\
-@InProceedings{SivaAndAl:Coca,
-       author = {Siva, Reddy and Danqi, Chen and  Christopher D., Manning},
-        title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
-      journal = { arXiv},
-         year = {2018},
+_HOMEPAGE = "https://stanfordnlp.github.io/coqa/"
 
+_CITATION = """\
+@article{reddy-etal-2019-coqa,
+    title = "{C}o{QA}: A Conversational Question Answering Challenge",
+    author = "Reddy, Siva  and
+      Chen, Danqi  and
+      Manning, Christopher D.",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "7",
+    year = "2019",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/Q19-1016",
+    doi = "10.1162/tacl_a_00266",
+    pages = "249--266",
 }
 """
 
-# TODO(coqa):
 _DESCRIPTION = """\
 CoQA: A Conversational Question Answering Challenge
 """
@@ -27,17 +34,12 @@ _DEV_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json"
 
 
 class Coqa(datasets.GeneratorBasedBuilder):
-    """TODO(coqa): Short description of my dataset."""
 
-    # TODO(coqa): Set up version.
     VERSION = datasets.Version("1.0.0")
 
     def _info(self):
-        # TODO(coqa): 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(
                 {
                     "source": datasets.Value("string"),
@@ -52,20 +54,12 @@ class Coqa(datasets.GeneratorBasedBuilder):
                     ),
                 }
             ),
-            # 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://stanfordnlp.github.io/coqa/",
+            homepage=_HOMEPAGE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(coqa): 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 = {"train": _TRAIN_DATA_URL, "dev": _DEV_DATA_URL}
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
@@ -80,7 +74,6 @@ class Coqa(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, filepath, split):
         """Yields examples."""
-        # TODO(coqa): Yields (key, example) tuples from the dataset
         with open(filepath, encoding="utf-8") as f:
             data = json.load(f)
             for row in data["data"]:

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "CoQA: A Conversational Question Answering Challenge\n", "citation": "@InProceedings{SivaAndAl:Coca,\n       author = {Siva, Reddy and Danqi, Chen and  Christopher D., Manning},\n        title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},\n      journal = { arXiv},\n         year = {2018},\n\n}\n", "homepage": "https://stanfordnlp.github.io/coqa/", "license": "", "features": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "story": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answers": {"feature": {"input_text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}, "answer_end": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "coqa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 18014921, "num_examples": 7199, "dataset_name": "coqa"}, "validation": {"name": "validation", "num_bytes": 1227955, "num_examples": 500, "dataset_name": "coqa"}}, "download_checksums": {"https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json": {"num_bytes": 49001836, "checksum": "b0fdb2bc1bd38dd3ca2ce5fa2ac3e02c6288ac914f241ac409a655ffb6619fa6"}, "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json": {"num_bytes": 9090845, "checksum": "dfa367a9733ce53222918d0231d9b3bedc2b8ee831a2845f62dfc70701f2540a"}}, "download_size": 58092681, "dataset_size": 19242876, "size_in_bytes": 77335557}}
+{"default": {"description": "CoQA: A Conversational Question Answering Challenge\n", "citation": "@article{reddy-etal-2019-coqa,\n    title = \"{C}o{QA}: A Conversational Question Answering Challenge\",\n    author = \"Reddy, Siva  and\n      Chen, Danqi  and\n      Manning, Christopher D.\",\n    journal = \"Transactions of the Association for Computational Linguistics\",\n    volume = \"7\",\n    year = \"2019\",\n    address = \"Cambridge, MA\",\n    publisher = \"MIT Press\",\n    url = \"https://aclanthology.org/Q19-1016\",\n    doi = \"10.1162/tacl_a_00266\",\n    pages = \"249--266\",\n}\n", "homepage": "https://stanfordnlp.github.io/coqa/", "license": "", "features": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "story": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answers": {"feature": {"input_text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}, "answer_end": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "coqa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17981459, "num_examples": 7199, "dataset_name": "coqa"}, "validation": {"name": "validation", "num_bytes": 1225518, "num_examples": 500, "dataset_name": "coqa"}}, "download_checksums": {"https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json": {"num_bytes": 49001836, "checksum": "b0fdb2bc1bd38dd3ca2ce5fa2ac3e02c6288ac914f241ac409a655ffb6619fa6"}, "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json": {"num_bytes": 9090845, "checksum": "dfa367a9733ce53222918d0231d9b3bedc2b8ee831a2845f62dfc70701f2540a"}}, "download_size": 58092681, "post_processing_size": null, "dataset_size": 19206977, "size_in_bytes": 77299658}}