Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Chinese
Size:
1K<n<10K
License:
File size: 5,247 Bytes
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# 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 = """\
Tags: PER(人名), LOC(地点名), GPE(行政区名), ORG(机构名)
Label Tag Meaning
PER PER.NAM 名字（张三）
PER.NOM 代称、类别名（穷人）
LOC LOC.NAM 特指名称（紫玉山庄）
LOC.NOM 泛称（大峡谷、宾馆）
GPE GPE.NAM 行政区的名称（北京）
ORG ORG.NAM 特定机构名称（通惠医院）
ORG.NOM 泛指名称、统称（文艺公司）
"""
_HOMEPAGE_URL = "https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/Weibo"
_CITATION = None
_TRAIN_URL = "https://raw.githubusercontent.com/OYE93/Chinese-NLP-Corpus/master/NER/Weibo/weiboNER_2nd_conll.train"
_TEST_URL = "https://raw.githubusercontent.com/OYE93/Chinese-NLP-Corpus/master/NER/Weibo/weiboNER_2nd_conll.test"
_VALID_URL = "https://raw.githubusercontent.com/OYE93/Chinese-NLP-Corpus/master/NER/Weibo/weiboNER_2nd_conll.dev"
class WeiboNERCorpus(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-GPE.NAM",
"B-GPE.NOM",
"B-LOC.NAM",
"B-LOC.NOM",
"B-ORG.NAM",
"B-ORG.NOM",
"B-PER.NAM",
"B-PER.NOM",
"I-GPE.NAM",
"I-GPE.NOM",
"I-LOC.NAM",
"I-LOC.NOM",
"I-ORG.NAM",
"I-ORG.NOM",
"I-PER.NAM",
"I-PER.NOM",
"O",
]
)
),
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_URL)
valid_path = dl_manager.download_and_extract(_VALID_URL)
test_path = dl_manager.download_and_extract(_TEST_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_path": train_path},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_path": valid_path},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_path": test_path},
),
]
def _generate_examples(self, data_path):
sentence_counter = 0
with open(data_path, encoding="utf-8") as f:
current_words = []
current_labels = []
for row in f:
row = row.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
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