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# 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."""
from __future__ import absolute_import, division, print_function
import json
import logging
import datasets
_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,
)
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."""
logging.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,
},
}
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