Datasets:
cmrc2018

	"""TODO(cmrc2018): Add a description here."""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	# TODO(cmrc2018): BibTeX citation
	_CITATION = """\
	@inproceedings{cui-emnlp2019-cmrc2018,
	title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension},
	author = {Cui, Yiming and
	Liu, Ting and
	Che, Wanxiang and
	Xiao, Li and
	Chen, Zhipeng and
	Ma, Wentao and
	Wang, Shijin and
	Hu, Guoping},
	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/D19-1600},
	doi = {10.18653/v1/D19-1600},
	pages = {5886--5891}}
	"""

	# TODO(cmrc2018):
	_DESCRIPTION = """\
	A Span-Extraction dataset for Chinese machine reading comprehension to add language
	diversities in this area. The dataset is composed by near 20,000 real questions annotated
	on Wikipedia paragraphs by human experts. We also annotated a challenge set which
	contains the questions that need comprehensive understanding and multi-sentence
	inference throughout the context.
	"""
	_URL = "https://github.com/ymcui/cmrc2018"
	_TRAIN_FILE = "https://worksheets.codalab.org/rest/bundles/0x15022f0c4d3944a599ab27256686b9ac/contents/blob/"
	_DEV_FILE = "https://worksheets.codalab.org/rest/bundles/0x72252619f67b4346a85e122049c3eabd/contents/blob/"
	_TEST_FILE = "https://worksheets.codalab.org/rest/bundles/0x182c2e71fac94fc2a45cc1a3376879f7/contents/blob/"


	class Cmrc2018(datasets.GeneratorBasedBuilder):
	"""TODO(cmrc2018): Short description of my dataset."""

	# TODO(cmrc2018): Set up version.
	VERSION = datasets.Version("0.1.0")

	def _info(self):
	# TODO(cmrc2018): 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"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}
	),
	# These are the features of your dataset like images, labels ...
	}
	),
	# 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=_URL,
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(cmrc2018): 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_FILE, "dev": _DEV_FILE, "test": _TEST_FILE}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	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):
	"""Yields examples."""
	# TODO(cmrc2018): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for example in data["data"]:
	for paragraph in example["paragraphs"]:
	context = paragraph["context"].strip()
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	id_ = qa["id"]

	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"].strip() for answer in qa["answers"]]

	yield id_, {
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}