Datasets:
turku_ner_corpus

	# 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

	import datasets


	_DESCRIPTION = """\
	An open, broad-coverage corpus for Finnish named entity recognition presented in Luoma et al. (2020) A Broad-coverage Corpus for Finnish Named Entity Recognition.
	"""
	_HOMEPAGE_URL = "https://turkunlp.org/fin-ner.html"
	_URL = "https://github.com/TurkuNLP/turku-ner-corpus/archive/v1.0.tar.gz"
	_CITATION = """\
	@inproceedings{luoma-etal-2020-broad,
	title = "A Broad-coverage Corpus for {F}innish Named Entity Recognition",
	author = {Luoma, Jouni and Oinonen, Miika and Pyyk{\"o}nen, Maria and Laippala, Veronika and Pyysalo, Sampo},
	booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
	year = "2020",
	url = "https://www.aclweb.org/anthology/2020.lrec-1.567",
	pages = "4615--4624",
	}
	"""


	class TurkuNERCorpus(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	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=[
	"B-DATE",
	"B-EVENT",
	"B-LOC",
	"B-ORG",
	"B-PER",
	"B-PRO",
	"I-DATE",
	"I-EVENT",
	"I-LOC",
	"I-ORG",
	"I-PER",
	"I-PRO",
	"O",
	]
	)
	),
	},
	),
	supervised_keys=None,
	homepage=_HOMEPAGE_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	archive = dl_manager.download(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"files": dl_manager.iter_archive(archive), "data_type": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"files": dl_manager.iter_archive(archive), "data_type": "valid"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"files": dl_manager.iter_archive(archive), "data_type": "test"},
	),
	]

	def _generate_examples(self, files, data_type):
	if data_type == "train":
	data_path = "turku-ner-corpus-1.0/data/conll/train.tsv"
	elif data_type == "valid":
	data_path = "turku-ner-corpus-1.0/data/conll/dev.tsv"
	elif data_type == "test":
	data_path = "turku-ner-corpus-1.0/data/conll/test.tsv"
	else:
	raise Exception("data_type not understood")

	sentence_counter = 0
	for path, f in files:
	if path == data_path:
	current_words = []
	current_labels = []
	for row in f:
	row = row.decode("utf-8").rstrip()
	row_split = row.split("\t")
	if len(row_split) == 2:
	token, label = row_split
	current_words.append(token)
	current_labels.append(label)
	else:
	if not current_words:
	continue
	assert len(current_words) == len(current_labels), "word len doesnt match label length"
	sentence = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"tokens": current_words,
	"ner_tags": current_labels,
	},
	)
	sentence_counter += 1
	current_words = []
	current_labels = []
	yield sentence

	# if something remains:
	if current_words:
	sentence = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"tokens": current_words,
	"ner_tags": current_labels,
	},
	)
	yield sentence
	break