Datasets:
covid_qa_castorini

	# 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.
	"""CovidQA, a question answering dataset specifically designed for COVID-19."""


	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{tang2020rapidly,
	title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
	author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
	journal={arXiv preprint arXiv:2004.11339},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from \
	knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.
	"""

	_HOMEPAGE = "http://covidqa.ai"

	_LICENSE = "Apache License 2.0"

	_URL = "https://raw.githubusercontent.com/castorini/pygaggle/master/data/"
	_URLs = {"covid_qa_castorini": _URL + "kaggle-lit-review-0.2.json"}


	class CovidQaCastorini(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("0.2.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="covid_qa_castorini",
	version=VERSION,
	description="CovidQA, a question answering dataset specifically designed for COVID-19",
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"category_name": datasets.Value("string"),
	"question_query": datasets.Value("string"),
	"keyword_query": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"exact_answer": datasets.Value("string"),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	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_idx, article in enumerate(covid_qa["categories"]):
	category_name = article["name"]
	for paragraph_idx, paragraph in enumerate(article["sub_categories"]):
	question_query = paragraph["nq_name"]
	keyword_query = paragraph["kq_name"]

	ids = [answer["id"] for answer in paragraph["answers"]]
	titles = [answer["title"] for answer in paragraph["answers"]]
	exact_answers = [answer["exact_answer"] for answer in paragraph["answers"]]

	yield f"{article_idx}_{paragraph_idx}", {
	"category_name": category_name,
	"question_query": question_query,
	"keyword_query": keyword_query,
	"answers": {
	"id": ids,
	"title": titles,
	"exact_answer": exact_answers,
	},
	}