Datasets:
sberquad

	# coding=utf-8
	"""SberQUAD: Sber Question Answering Dataset."""

	import os
	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@article{Efimov_2020,
	title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis},
	ISBN={9783030582197},
	ISSN={1611-3349},
	url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},
	DOI={10.1007/978-3-030-58219-7_1},
	journal={Experimental IR Meets Multilinguality, Multimodality, and Interaction},
	publisher={Springer International Publishing},
	author={Efimov, Pavel and Chertok, Andrey and Boytsov, Leonid and Braslavski, Pavel},
	year={2020},
	pages={3–15}
	}
	"""


	_DESCRIPTION = """\
	Sber Question Answering Dataset (SberQuAD) is a reading comprehension \
	dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
	articles, where the answer to every question is a segment of text, or span, \
	from the corresponding reading passage, or the question might be unanswerable. \
	Russian original analogue presented in Sberbank Data Science Journey 2017.
	"""

	_URLS = {"train": os.path.join("data", "train_v1.0.json.gz"), "dev": os.path.join("data", "dev_v1.0.json.gz"), "test": os.path.join("data", "origin_test.json.gz")}


	class Sberquad(datasets.GeneratorBasedBuilder):
	"""SberQUAD: Sber Question Answering Dataset. Version 1.0."""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [datasets.BuilderConfig(name="sberquad", version=VERSION, description=_DESCRIPTION)]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"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"),
	}
	),
	}
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLS)
	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):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	key = 0
	with open(filepath, encoding="utf-8") as f:
	squad = json.load(f)
	for article in squad["data"]:
	title = article.get("title", "")
	for paragraph in article["paragraphs"]:
	context = paragraph["context"]
	for qa in paragraph["qas"]:
	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"] for answer in qa["answers"]]
	yield key, {
	"title": title,
	"context": context,
	"question": qa["question"],
	"id": qa["id"],
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}
	key += 1