Datasets:
squad_v1_pt

	"""TODO(squad_v1_pt): Add a description here."""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	# TODO(squad_v1_pt): BibTeX citation
	_CITATION = """\
	@article{2016arXiv160605250R,
	author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
	Konstantin and {Liang}, Percy},
	title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
	journal = {arXiv e-prints},
	year = 2016,
	eid = {arXiv:1606.05250},
	pages = {arXiv:1606.05250},
	archivePrefix = {arXiv},
	eprint = {1606.05250},
	}
	"""

	# TODO(squad_v1_pt):
	_DESCRIPTION = """\
	Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API.
	"""

	_URL = "https://github.com/nunorc/squad-v1.1-pt/raw/master/"
	_URLS = {
	"train": _URL + "train-v1.1-pt.json",
	"dev": _URL + "dev-v1.1-pt.json",
	}


	class SquadV1Pt(datasets.GeneratorBasedBuilder):
	"""TODO(squad_v1_pt): Short description of my dataset."""

	# TODO(squad_v1_pt): Set up version.
	VERSION = datasets.Version("1.1.0")

	def _info(self):
	# TODO(squad_v1_pt): 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"),
	"title": 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="https://github.com/nunorc/squad-v1.1-pt",
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(squad_v1_pt): 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
	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"]}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(squad_v1_pt): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for example in data["data"]:
	title = example.get("title", "").strip()
	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_, {
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}