Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
# coding=utf-8 | |
# Copyright 2020 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 | |
"""MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining""" | |
import csv | |
import os.path | |
import datasets | |
_CITATION = """\ | |
@inproceedings{wen-etal-2020-medal, | |
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining", | |
author = "Wen, Zhi and | |
Lu, Xing Han and | |
Reddy, Siva", | |
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", | |
month = nov, | |
year = "2020", | |
address = "Online", | |
publisher = "Association for Computational Linguistics", | |
url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15", | |
pages = "130--135", | |
abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.", | |
}""" | |
_DESCRIPTION = """\ | |
A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate | |
""" | |
_URLS = { | |
"pretrain": "data/pretrain_subset.zip", | |
"full": "data/full_data.csv.zip" | |
} | |
_FILENAMES = { | |
"train": "train.csv", | |
"test": "test.csv", | |
"valid": "valid.csv", | |
"full": "full_data.csv", | |
} | |
class Medal(datasets.GeneratorBasedBuilder): | |
"""Medal: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining""" | |
VERSION = datasets.Version("4.0.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# datasets.features.FeatureConnectors | |
features=datasets.Features( | |
{ | |
"abstract_id": datasets.Value("int32"), | |
"text": datasets.Value("string"), | |
"location": datasets.Sequence(datasets.Value("int32")), | |
"label": datasets.Sequence(datasets.Value("string")), | |
# These are the features of your dataset like images, labels ... | |
} | |
), | |
# If there's a common (input, target) tuple from the features, | |
# specify them here. They'll be used if as_supervised=True in | |
# builder.as_dataset. | |
supervised_keys=None, | |
# Homepage of the dataset for documentation | |
homepage="https://github.com/BruceWen120/medal", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# dl_manager is a datasets.download.DownloadManager that can be used to | |
# download and extract URLs | |
dl_dir = dl_manager.download_and_extract(_URLS) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["train"]), "split": "train"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["test"]), "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["valid"]), "split": "val"}, | |
), | |
datasets.SplitGenerator( | |
name="full", | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(dl_dir["full"], _FILENAMES["full"]), "split": "full"}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""Yields examples.""" | |
with open(filepath, encoding="utf-8") as f: | |
data = csv.reader(f) | |
# Skip header | |
next(data) | |
if split == "full": | |
id_ = 0 | |
for id_, row in enumerate(data): | |
yield id_, { | |
"abstract_id": -1, | |
"text": row[0], | |
"location": [int(location) for location in row[1].split("|")], | |
"label": row[2].split("|"), | |
} | |
else: | |
for id_, row in enumerate(data): | |
yield id_, { | |
"abstract_id": int(row[0]), | |
"text": row[1], | |
"location": [int(row[2])], | |
"label": [row[3]], | |
} | |