Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
# 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, | |
}, | |
} | |