Datasets:

DEFT-2023
/

DEFT2023

	# 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://huggingface.co/datasets/DEFT-2023/DEFT2023/resolve/main/DEFT-2023-FULL.zip"

	_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	@unpublished{labrak:hal-03824241,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"]),
	),
	"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)

	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"])),
	}