Datasets:
swedish_medical_ner

	# 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 [],
	}