Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Korean
Size:
1K<n<10K
License:
File size: 5,981 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.
"""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)
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