Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Swedish
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Source Datasets:
original
License:
File size: 8,067 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""SwedMedNER: A Named Entity Recognition Dataset on medical texts in Swedish"""
import re
import datasets
_CITATION = """\
@inproceedings{almgrenpavlovmogren2016bioner,
title={Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs},
author={Simon Almgren, Sean Pavlov, Olof Mogren},
booktitle={Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016)},
pages={1},
year={2016}
}
"""
_DESCRIPTION = """\
SwedMedNER is a dataset for training and evaluating Named Entity Recognition systems on medical texts in Swedish.
It is derived from medical articles on the Swedish Wikipedia, Läkartidningen, and 1177 Vårdguiden.
"""
_LICENSE = """\
Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)
See http://creativecommons.org/licenses/by-sa/4.0/ for the summary of the license.
"""
_URL = "https://github.com/olofmogren/biomedical-ner-data-swedish"
_DATA_URL = "https://raw.githubusercontent.com/olofmogren/biomedical-ner-data-swedish/master/"
class SwedishMedicalNerConfig(datasets.BuilderConfig):
"""BuilderConfig for SwedMedNER"""
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SwedishMedicalNerConfig, self).__init__(**kwargs)
class SwedishMedicalNer(datasets.GeneratorBasedBuilder):
"""SwedMedNER: A Named Entity Recognition Dataset on medical texts in Swedish"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="wiki", version=VERSION, description="The Swedish Wikipedia part of the dataset"),
datasets.BuilderConfig(name="lt", version=VERSION, description="The Läkartidningen part of the dataset"),
datasets.BuilderConfig(name="1177", version=VERSION, description="The 1177 Vårdguiden part of the dataset"),
]
def _info(self):
if self.config.name == "wiki":
features = datasets.Features(
{
"sid": datasets.Value("string"),
"sentence": datasets.Value("string"),
"entities": datasets.Sequence(
{
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"text": datasets.Value("string"),
"type": datasets.ClassLabel(
names=["Disorder and Finding", "Pharmaceutical Drug", "Body Structure"]
),
}
),
}
)
elif self.config.name == "lt":
features = datasets.Features(
{
"sid": datasets.Value("string"),
"sentence": datasets.Value("string"),
"entities": datasets.Sequence(
{
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"text": datasets.Value("string"),
"type": datasets.ClassLabel(
names=["Disorder and Finding", "Pharmaceutical Drug", "Body Structure"]
),
}
),
}
)
elif self.config.name == "1177":
features = datasets.Features(
{
"sid": datasets.Value("string"),
"sentence": datasets.Value("string"),
"entities": datasets.Sequence(
{
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"text": datasets.Value("string"),
"type": datasets.ClassLabel(
names=["Disorder and Finding", "Pharmaceutical Drug", "Body Structure"]
),
}
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_URL,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"wiki": _DATA_URL + "Wiki_annotated_60.txt",
"lt": _DATA_URL + "LT_annotated_60.txt",
"1177": _DATA_URL + "1177_annotated_sentences.txt",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
if self.config.name == "wiki":
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["wiki"]})
]
elif self.config.name == "lt":
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["lt"]})
]
elif self.config.name == "1177":
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["1177"]})
]
def _generate_examples(self, filepath):
"""Yields examples as (key, example) tuples."""
def find_type(s, e):
if (s == "(") and (e == ")"):
return "Disorder and Finding"
elif (s == "[") and (e == "]"):
return "Pharmaceutical Drug"
elif (s == "{") and (e == "}"):
return "Body Structure"
pattern = r"\[([^\[\]()]+)\]|\(([^\[\]()]+)\)|\{([^\[\]()]+)\}"
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
sentence = row.replace("\n", "")
if self.config.name == "1177":
targets = [
{
"start": m.start(0),
"end": m.end(0),
"text": sentence[m.start(0) + 2 : m.end(0) - 2],
"type": find_type(sentence[m.start(0)], sentence[m.end(0) - 1]),
}
for m in re.finditer(pattern, sentence)
]
yield id_, {
"sid": self.config.name + "_" + str(id_),
"sentence": sentence,
"entities": targets if targets else [],
}
else:
targets = [
{
"start": m.start(0),
"end": m.end(0),
"text": sentence[m.start(0) + 1 : m.end(0) - 1],
"type": find_type(sentence[m.start(0)], sentence[m.end(0) - 1]),
}
for m in re.finditer(pattern, sentence)
]
yield id_, {
"sid": self.config.name + "_" + str(id_),
"sentence": sentence,
"entities": targets if targets else [],
}
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