Datasets:
covid_qa_deepset

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""COVID-QA: A Question Answering Dataset for COVID-19."""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{moller2020covid,
	title={COVID-QA: A Question Answering Dataset for COVID-19},
	author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
	booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
	year={2020}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical \
	experts on scientific articles related to COVID-19.
	"""

	_HOMEPAGE = "https://github.com/deepset-ai/COVID-QA"

	_LICENSE = "Apache License 2.0"

	_URL = "https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/question-answering/"
	_URLs = {"covid_qa_deepset": _URL + "COVID-QA.json"}


	class CovidQADeepset(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="covid_qa_deepset", version=VERSION, description="COVID-QA deepset"),
	]

	def _info(self):
	features = datasets.Features(
	{
	"document_id": datasets.Value("int32"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"is_impossible": datasets.Value("bool"),
	"id": datasets.Value("int32"),
	"answers": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	url = _URLs[self.config.name]
	downloaded_filepath = dl_manager.download_and_extract(url)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_filepath},
	),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	covid_qa = json.load(f)
	for article in covid_qa["data"]:
	for paragraph in article["paragraphs"]:
	context = paragraph["context"].strip()
	document_id = paragraph["document_id"]
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	is_impossible = qa["is_impossible"]
	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_, {
	"document_id": document_id,
	"context": context,
	"question": question,
	"is_impossible": is_impossible,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}