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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
"""Introduction to the Bio-Entity Recognition Task at JNLPBA"""
import glob
import os
import re
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{kim2004introduction,
title={Introduction to the bio-entity recognition task at JNLPBA},
author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},
pages={70--75},
year={2004},
organization={Citeseer}
}
"""
_DESCRIPTION = """\
The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search
on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts
were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.
Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
"""
_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"
TRAIN_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz"
VAL_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Evaluation/Genia4ERtest.tar.gz"
TEST_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Tool/JNLPBA2004_eval.tar.gz"
class JNLPBAConfig(datasets.BuilderConfig):
"""BuilderConfig for JNLPBA"""
def __init__(self, **kwargs):
"""BuilderConfig for JNLPBA.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(JNLPBAConfig, self).__init__(**kwargs)
class JNLPBA(datasets.GeneratorBasedBuilder):
"""JNLPBA dataset."""
BUILDER_CONFIGS = [
JNLPBAConfig(name="jnlpba", version=datasets.Version("1.0.0"), description="JNLPBA dataset"),
]
def _info(self):
custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE',
'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES']
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=custom_names
)
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_files = dl_manager.download_and_extract(TRAIN_URL)
val_files = dl_manager.download_and_extract(VAL_URL)
# test_files = dl_manager.download_and_extract(TEST_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_files}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_files}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": val_files}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
filenames = glob.glob(os.path.join(filepath, "Genia4ER*.iob2"))
guid = 0
for filename in filenames:
with open(filename, encoding="utf-8") as f:
if guid >= 0:
guid += 1 # update guid to avoid DuplicatedKeysError
tokens = []
ner_tags = []
for line in f:
if len(re.split(r"###MEDLINE:", line)) == 2:
continue
elif line == "" or line == "\n":
if tokens:
# print(guid, line)
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])
if(splits[1].rstrip()=="B-cell_line"):
ner_tags.append("B-CELL_LINE")
elif(splits[1].rstrip()=="I-cell_line"):
ner_tags.append("I-CELL_LINE")
elif(splits[1].rstrip()=="B-cell_type"):
ner_tags.append("B-CELL_TYPE")
elif(splits[1].rstrip()=="I-cell_type"):
ner_tags.append("I-CELL_TYPE")
elif(splits[1].rstrip()=="B-protein"):
ner_tags.append("B-PROTEIN")
elif(splits[1].rstrip()=="I-protein"):
ner_tags.append("I-PROTEIN")
else:
ner_tags.append(splits[1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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