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# 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]],
}
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