Datasets:
kor_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.
	"""Korean named entity recognition dataset"""


	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@InProceedings{Kim:2016,
	title = "Korean Named Entity Recognition Dataset",
	authors = "Jae-Hoon Kim",
	publisher = "GitHub",
	year = "2016"
	}
	"""


	_DESCRIPTION = """\
	Korean named entity recognition dataset
	"""

	_HOMEPAGE = "https://github.com/kmounlp/NER"

	_LICENSE = "NER License, MIT License for non-commercial use"

	_URL = "https://raw.githubusercontent.com/kmounlp/NER/master/2016klp/ner."
	_URLs = {key: _URL + key for key in ("train", "test", "dev")}


	class KorNER(datasets.GeneratorBasedBuilder):
	"""Korean Named entity recognition dataset"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"annot_text": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"pos_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"SO",
	"SS",
	"VV",
	"XR",
	"VCP",
	"JC",
	"VCN",
	"JKB",
	"MM",
	"SP",
	"XSN",
	"SL",
	"NNP",
	"NP",
	"EP",
	"JKQ",
	"IC",
	"XSA",
	"EC",
	"EF",
	"SE",
	"XPN",
	"ETN",
	"SH",
	"XSV",
	"MAG",
	"SW",
	"ETM",
	"JKO",
	"NNB",
	"MAJ",
	"NNG",
	"JKV",
	"JKC",
	"VA",
	"NR",
	"JKG",
	"VX",
	"SF",
	"JX",
	"JKS",
	"SN",
	]
	)
	),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(names=["I", "O", "B_OG", "B_TI", "B_LC", "B_DT", "B_PS"])
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": downloaded_files["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": downloaded_files["test"],
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": downloaded_files["dev"],
	"split": "validation",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	text = ""
	annot_text = ""
	tokens = []
	pos_tags = []
	ner_tags = []
	for id_, row in enumerate(f):
	row = row.strip()
	if not row:
	yield id_, {
	"text": text,
	"annot_text": annot_text,
	"tokens": tokens,
	"pos_tags": pos_tags,
	"ner_tags": ner_tags,
	}
	tokens.clear()
	pos_tags.clear()
	ner_tags.clear()
	continue
	if row[0] == ";":
	text = row[2:]
	elif row[0] == "$":
	annot_text = row[1:]
	else:
	_, token, pos_tag, ner_tag = row.split("\t")
	tokens.append(token)
	pos_tags.append(pos_tag)
	ner_tags.append(ner_tag)