Datasets:
quoref

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


	import json
	import os

	import datasets


	# TODO(quoref): BibTeX citation
	_CITATION = """\
	@article{allenai:quoref,
	author = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and Matt Gardner},
	title = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
	journal = {arXiv:1908.05803v2 },
	year = {2019},
	}
	"""

	# TODO(quoref):
	_DESCRIPTION = """\
	Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
	span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
	coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
	"""

	_URL = "https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip"


	class Quoref(datasets.GeneratorBasedBuilder):
	"""TODO(quoref): Short description of my dataset."""

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

	def _info(self):
	# TODO(quoref): 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"),
	"question": datasets.Value("string"),
	"context": datasets.Value("string"),
	"title": datasets.Value("string"),
	"url": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"answer_start": datasets.Value("int32"),
	"text": datasets.Value("string"),
	}
	)
	# 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="https://leaderboard.allenai.org/quoref/submissions/get-started",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(quoref): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)
	data_dir = os.path.join(dl_dir, "quoref-train-dev-v0.1")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "quoref-train-v0.1.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "quoref-dev-v0.1.json")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(quoref): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for article in data["data"]:
	title = article.get("title", "").strip()
	url = article.get("url", "").strip()
	for paragraph in article["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"]]

	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield id_, {
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	"url": url,
	}