Datasets:
cosmos_qa

	"""Cosmos QA dataset."""


	import csv
	import json

	import datasets


	_HOMEPAGE = "https://wilburone.github.io/cosmos/"

	_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",
	"test": _URL + "test.jsonl",
	"dev": _URL + "valid.csv",
	}


	class CosmosQa(datasets.GeneratorBasedBuilder):
	"""Cosmos QA dataset."""

	VERSION = datasets.Version("0.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answer0": datasets.Value("string"),
	"answer1": datasets.Value("string"),
	"answer2": datasets.Value("string"),
	"answer3": datasets.Value("string"),
	"label": datasets.Value("int32"),
	}
	),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = _URLS
	dl_dir = dl_manager.download_and_extract(urls_to_download)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	if split == "test":
	for id_, row in enumerate(f):
	data = json.loads(row)
	yield id_, {
	"id": data["id"],
	"context": data["context"],
	"question": data["question"],
	"answer0": data["answer0"],
	"answer1": data["answer1"],
	"answer2": data["answer2"],
	"answer3": data["answer3"],
	"label": int(data.get("label", -1)),
	}
	else:
	data = csv.DictReader(f)
	for id_, row in enumerate(data):
	yield id_, {
	"id": row["id"],
	"context": row["context"],
	"question": row["question"],
	"answer0": row["answer0"],
	"answer1": row["answer1"],
	"answer2": row["answer2"],
	"answer3": row["answer3"],
	"label": int(row.get("label", -1)),
	}