Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
# 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 | |
from datasets.tasks import TextClassification | |
_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, | |
task_templates=[TextClassification(text_column="abstract", label_column="label")], | |
) | |
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)} |