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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.
"""MORFITT: A multi-label corpus of French biomedical literature"""
import os
import json
import datasets
_DESCRIPTION = """\
We introduce MORFITT corpus, the first multi-label corpus for the classification of specialties in the medical field in French. MORFITT is composed of 3,624 summaries of scientific articles from PubMed, annotated in 12 specialties. The article details the corpus, the experiments and the preliminary results obtained using a classifier based on the pre-trained language model CamemBERT.
"""
_HOMEPAGE = "https://github.com/qanastek/MORFITT"
_LICENSE = "Apache License 2.0"
_URL = "https://huggingface.co/datasets/qanastek/MORFITT/resolve/main/data.zip"
_CITATION = """\
(comming soon)
"""
class MORFITT(datasets.GeneratorBasedBuilder):
"""MORFITT: A multi-label corpus of French biomedical literature"""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"abstract": datasets.Value("string"),
"labels": datasets.Sequence(datasets.features.ClassLabel(
names=[
'chemistry',
'etiology',
'genetics',
'immunology',
'microbiology',
'parasitology',
'pharmacology',
'physiology',
'psychology',
'surgery',
'veterinary',
'virology',
],
)),
}
)
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.txt"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "dev.txt"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "test.txt"),
},
),
]
def _generate_examples(self, filepath):
f_in = open(f"{filepath}","r")
content = f_in.read()
f_in.close()
for key, line in enumerate(content.split("\n")[1:]):
if len(line) <= 0:
continue
identifier, abstract, labels = line.split("\t")
labels = labels.split("|")
yield key, {
"id": identifier,
"abstract": abstract,
"labels": labels,
} |