Datasets:
squad_it

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


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	# TODO(squad_it): BibTeX citation
	_CITATION = """\
	@InProceedings{10.1007/978-3-030-03840-3_29,
	author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},
	editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
	title={Neural Learning for Question Answering in Italian},
	booktitle={AI*IA 2018 -- Advances in Artificial Intelligence},
	year={2018},
	publisher={Springer International Publishing},
	address={Cham},
	pages={389--402},
	isbn={978-3-030-03840-3}
	}
	"""

	# TODO(squad_it):
	_DESCRIPTION = """\
	SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
	into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
	The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
	split into training and test sets to support the replicability of the benchmarking of QA systems:
	"""

	_URL = "https://github.com/crux82/squad-it/raw/master/"
	_URLS = {
	"train": _URL + "SQuAD_it-train.json.gz",
	"test": _URL + "SQuAD_it-test.json.gz",
	}


	class SquadIt(datasets.GeneratorBasedBuilder):
	"""TODO(squad_it): Short description of my dataset."""

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

	def _info(self):
	# TODO(squad_it): 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="https://github.com/crux82/squad-it",
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(squad_it): 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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(squad_it): 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,
	},
	}