Datasets:
medal

	# 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]],
	}