Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1M<n<10M
License:
File size: 5,478 Bytes
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# 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-2 is a dataset generated from 620 rare disease abstracts labeled using statistical and rule-base methods.
For more details see *INSERT PAPER* and https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard
"""
_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/epi_extract_datasets/datasets/epi_gold/"
_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("4.0.0"), 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-DIS", #(1)
"I-DIS", #(2)
"B-ABRV", #(3)
"I-ABRV", #(4)
"B-EPI", #(5)
"I-EPI", #(6)
"B-STAT", #(7)
"I-STAT", #(8)
"B-LOC", #(9)
"I-LOC", #(10)
"B-DATE", #(11)
"I-DATE", #(12)
"B-SEX", #(13)
"I-SEX", #(14)
"B-ETHN", #(15)
"I-ETHN", #(16)
]
)
),
}
),
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,
} |