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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.
"""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,
},
}
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