Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
semantic-similarity-classification
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 2,829 Bytes
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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.
"""Medical Question Pairs (MQP) Dataset"""
import csv
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{mccreery2020effective,
title={Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs},
author={Clara H. McCreery and Namit Katariya and Anitha Kannan and Manish Chablani and Xavier Amatriain},
year={2020},
eprint={2008.13546},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
"""
_DESCRIPTION = """\
This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors.
"""
_HOMEPAGE = "https://github.com/curai/medical-question-pair-dataset"
_LICENSE = ""
_URL = "https://raw.githubusercontent.com/curai/medical-question-pair-dataset/master/mqp.csv"
class MedicalQuestionsPairs(datasets.GeneratorBasedBuilder):
"""Medical Question Pairs (MQP) Dataset"""
def _info(self):
features = datasets.Features(
{
"dr_id": datasets.Value("int32"),
"question_1": datasets.Value("string"),
"question_2": datasets.Value("string"),
"label": datasets.features.ClassLabel(num_classes=2, names=[0, 1]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_file = dl_manager.download_and_extract(_URL)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file})]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
data = csv.reader(f)
for id_, row in enumerate(data):
yield id_, {
"dr_id": row[0],
"question_1": row[1],
"question_2": row[2],
"label": row[3],
}
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