Datasets:

ncats
/

EpiSet4NER-v1

	# 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
	"""INSERT TITLE"""

	import logging

	import datasets


	_CITATION = """\
	REDO
	@inproceedings{wang2019crossweigh,
	title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
	author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
	booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
	pages={5157--5166},
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	*REWRITE
	EpiSet4NER is a dataset generated from 620 rare disease abstracts labeled using statistical and rule-base methods. The test set was then manually corrected by a rare disease expert.
	For more details see INSERT PAPER and https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard
	"""

	_URL = "https://github.com/NCATS/epi4GARD/raw/master/EpiExtract4GARD/datasets/EpiCustomV3/"
	_TRAINING_FILE = "train.tsv"
	_VAL_FILE = "val.tsv"
	_TEST_FILE = "test.tsv"


	class EpiSetConfig(datasets.BuilderConfig):
	"""BuilderConfig for Conll2003"""

	def __init__(self, **kwargs):
	"""BuilderConfig forConll2003.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(EpiSetConfig, self).__init__(**kwargs)


	class EpiSet(datasets.GeneratorBasedBuilder):
	"""EpiSet4NER by GARD."""

	BUILDER_CONFIGS = [
	EpiSetConfig(name="EpiSet4NER", version=datasets.Version("3.2.1"), description="EpiSet4NER by NIH NCATS GARD"),
	]

	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", #(0)
	"B-LOC", #(1)
	"I-LOC", #(2)
	"B-EPI", #(3)
	"I-EPI", #(4)
	"B-STAT", #(5)
	"I-STAT", #(6)
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"val": f"{_URL}{_VAL_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["val"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	logging.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.startswith("-DOCSTART-") or line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	ner_tags = []
	else:
	# EpiSet tokens are space separated
	splits = line.split("\t")
	tokens.append(splits[0])
	ner_tags.append(splits[1].rstrip())
	# last example
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}