Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K<n<100K
License:
File size: 4,758 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
"""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,
}
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