Datasets:
ncbi_disease

	# coding=utf-8
	# Copyright 2020 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
	"""NCBI disease corpus: a resource for disease name recognition and concept normalization"""

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{dougan2014ncbi,
	title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
	author={Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
	journal={Journal of biomedical informatics},
	volume={47},
	pages={1--10},
	year={2014},
	publisher={Elsevier}
	}
	"""

	_DESCRIPTION = """\
	This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed
	abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural
	language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions
	and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®).
	Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations.
	Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two
	annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked
	against annotations of the rest of the corpus to assure corpus-wide consistency.

	For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/

	The original dataset can be downloaded from: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBI_corpus.zip
	This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll
	Note: there is a duplicate document (PMID 8528200) in the original data, and the duplicate is recreated in the converted data.
	"""

	_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/"
	_URL = "https://github.com/spyysalo/ncbi-disease/raw/master/conll/"
	_TRAINING_FILE = "train.tsv"
	_DEV_FILE = "devel.tsv"
	_TEST_FILE = "test.tsv"


	class NCBIDiseaseConfig(datasets.BuilderConfig):
	"""BuilderConfig for NCBIDisease"""

	def __init__(self, **kwargs):
	"""BuilderConfig for NCBIDisease.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(NCBIDiseaseConfig, self).__init__(**kwargs)


	class NCBIDisease(datasets.GeneratorBasedBuilder):
	"""NCBIDisease dataset."""

	BUILDER_CONFIGS = [
	NCBIDiseaseConfig(name="ncbi_disease", version=datasets.Version("1.0.0"), description="NCBIDisease dataset"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-Disease",
	"I-Disease",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"dev": f"{_URL}{_DEV_FILE}",
	"test": f"{_URL}{_TEST_FILE}",
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	ner_tags = []
	for line in f:
	if line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	ner_tags = []
	else:
	# tokens are tab separated
	splits = line.split("\t")
	tokens.append(splits[0])
	ner_tags.append(splits[1].rstrip())
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}