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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""EpiClassify4GARD dataset."""
import csv
import datasets
_DESCRIPTION = """\
INSERT DESCRIPTION
"""
_CITATION = """\
John JN, Sid E, Zhu Q. Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed. AMIA Jt Summits Transl Sci Proc. 2021 May 17;2021:325-334. PMID: 34457147; PMCID: PMC8378621.
"""
_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/ncats/GARD_EpiSet4TextClassification/raw/main/train_short.tsv"
_VAL_DOWNLOAD_URL = "https://huggingface.co/datasets/ncats/GARD_EpiSet4TextClassification/raw/main/val_short.tsv"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/ncats/GARD_EpiSet4TextClassification/raw/main/test.tsv"
class EpiClassify4GARD(datasets.GeneratorBasedBuilder):
"""EpiClassify4GARD text classification dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"abstract": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["1 = IsEpi", "0 = IsNotEpi"]),
}
),
homepage="https://github.com/ncats/epi4GARD/tree/master/Epi4GARD#epi4gard",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
val_path = dl_manager.download_and_extract(_VAL_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path }),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file, quotechar='"', delimiter="\t", quoting=csv.QUOTE_ALL, skipinitialspace=True
)
next(csv_reader)
for id_, row in enumerate(csv_reader):
abstract = row[0]
label = row[1]
yield id_, {"abstract": abstract, "label": int(label)} |