Datasets:
linnaeus

	# 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
	"""LINNAEUS: A species name identification system for biomedical literature"""

	import os

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{gerner2010linnaeus,
	title={LINNAEUS: a species name identification system for biomedical literature},
	author={Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
	journal={BMC bioinformatics},
	volume={11},
	number={1},
	pages={85},
	year={2010},
	publisher={Springer}
	}
	"""

	_DESCRIPTION = """\
	A novel corpus of full-text documents manually annotated for species mentions.
	"""

	_HOMEPAGE = "http://linnaeus.sourceforge.net/"
	# Source data: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/linnaeus-IOB
	_URL = "data/linnaeus.zip"
	_BIOBERT_NER_DATASET_DIRECTORY = "linnaeus"
	_TRAINING_FILE = "train.tsv"
	_DEV_FILE = "devel.tsv"
	_TEST_FILE = "test.tsv"


	class LinnaeusConfig(datasets.BuilderConfig):
	"""BuilderConfig for Linnaeus"""

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


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

	BUILDER_CONFIGS = [
	LinnaeusConfig(name="linnaeus", version=datasets.Version("1.0.0"), description="Linnaeus 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",
	"I",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"biobert_ner_datasets": _URL,
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)
	dataset_directory = os.path.join(downloaded_files["biobert_ner_datasets"], _BIOBERT_NER_DATASET_DIRECTORY)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dataset_directory, _TRAINING_FILE)}
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dataset_directory, _DEV_FILE)}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dataset_directory, _TEST_FILE)}
	),
	]

	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].split("-")[0])
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}