Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
unknown
Annotations Creators:
unknown
Source Datasets:
unknown
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://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/train.tsv" | |
| _VAL_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/val.tsv" | |
| _TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/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)} |