Datasets:

spyysalo
/

bc2gm_corpus

	# 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
	"""BioCreative II gene mention recognition Corpus"""

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{smith2008overview,
	title={Overview of BioCreative II gene mention recognition},
	author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and others},
	journal={Genome biology},
	volume={9},
	number={S2},
	pages={S2},
	year={2008},
	publisher={Springer}
	}
	"""

	_DESCRIPTION = """\
	Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
	In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
	A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
	Here we present brief descriptions of all the methods used and a statistical analysis of the results.
	We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible,
	and furthermore that the best result makes use of the lowest scoring submissions.

	For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/

	The original dataset can be downloaded from: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-ii-corpus/
	This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll
	"""

	_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/"
	_URL = "https://github.com/spyysalo/bc2gm-corpus/raw/master/conll/"
	_TRAINING_FILE = "train.tsv"
	_DEV_FILE = "devel.tsv"
	_TEST_FILE = "test.tsv"


	class Bc2gmCorpusConfig(datasets.BuilderConfig):
	"""BuilderConfig for Bc2gmCorpus"""

	def __init__(self, **kwargs):
	"""BuilderConfig for Bc2gmCorpus.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(Bc2gmCorpusConfig, self).__init__(**kwargs)


	class Bc2gmCorpus(datasets.GeneratorBasedBuilder):
	"""Bc2gmCorpus dataset."""

	BUILDER_CONFIGS = [
	Bc2gmCorpusConfig(name="bc2gm_corpus", version=datasets.Version("1.0.0"), description="bc2gm corpus"),
	]

	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-GENE",
	"I-GENE",
	]
	)
	),
	}
	),
	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,
	}