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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.
"""FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain"""
import os
import json
import datasets
_DESCRIPTION = """\
FrenchMedMCQA
"""
_HOMEPAGE = "https://frenchmedmcqa.github.io"
_LICENSE = "Apache License 2.0"
_URL = "https://drive.google.com/uc?export=download&id=1uH5RvDQnEhfgwC6oXE_xfK63N6Q7slIZ"
_CITATION = """\
@unpublished{labrak:hal-03824241,
TITLE = {{FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain}},
AUTHOR = {Labrak, Yanis and Bazoge, Adrien and Dufour, Richard and Daille, Béatrice and Gourraud, Pierre-Antoine and Morin, Emmanuel and Rouvier, Mickael},
URL = {https://hal.archives-ouvertes.fr/hal-03824241},
NOTE = {working paper or preprint},
YEAR = {2022},
MONTH = Oct,
PDF = {https://hal.archives-ouvertes.fr/hal-03824241/file/LOUHI_2022___QA-3.pdf},
HAL_ID = {hal-03824241},
HAL_VERSION = {v1},
}
"""
class DEFT2023(datasets.GeneratorBasedBuilder):
"""FrenchMedMCQA : A French Multi-Choice Question Answering Corpus for Medical domain"""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"answer_a": datasets.Value("string"),
"answer_b": datasets.Value("string"),
"answer_c": datasets.Value("string"),
"answer_d": datasets.Value("string"),
"answer_e": datasets.Value("string"),
"correct_answers": datasets.Sequence(
datasets.features.ClassLabel(names=["a", "b", "c", "d", "e"]),
),
"type": datasets.Value("string"),
"subject_name": datasets.Value("string"),
"number_correct_answers": datasets.features.ClassLabel(names=["1","2","3","4","5"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL) + "/HuggingFace-Train-Dev-Archive-JSON/"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "dev.json"),
},
),
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# gen_kwargs={
# "filepath": os.path.join(data_dir, "test.json"),
# },
# ),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for key, d in enumerate(data):
yield key, {
"id": d["id"],
"question": d["question"],
"answer_a": d["answers"]["a"],
"answer_b": d["answers"]["b"],
"answer_c": d["answers"]["c"],
"answer_d": d["answers"]["d"],
"answer_e": d["answers"]["e"],
"correct_answers": d["correct_answers"],
"number_correct_answers": str(len(d["correct_answers"])),
"type": d["type"],
"subject_name": d["subject_name"],
}
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