Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Chinese
Size:
10K<n<100K
License:
File size: 4,595 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
"""Introduction to People's Daily Dataset"""
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
People's Daily NER Dataset is a commonly used dataset for Chinese NER, with
text from People's Daily (人民日报), the largest official newspaper.
The dataset is in BIO scheme. Entity types are: PER (person), ORG (organization)
and LOC (location).
"""
_URL = "https://raw.githubusercontent.com/OYE93/Chinese-NLP-Corpus/master/NER/People's%20Daily/"
_TRAINING_FILE = "example.train"
_DEV_FILE = "example.dev"
_TEST_FILE = "example.test"
class PeoplesDailyConfig(datasets.BuilderConfig):
"""BuilderConfig for People's Daily NER"""
def __init__(self, **kwargs):
"""BuilderConfig for People's Daily NER.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(PeoplesDailyConfig, self).__init__(**kwargs)
class PeoplesDailyNer(datasets.GeneratorBasedBuilder):
"""People's Daily NER dataset."""
BUILDER_CONFIGS = [
PeoplesDailyConfig(
name="peoples_daily_ner", version=datasets.Version("1.0.0"), description="People's Daily NER dataset"
),
]
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=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
}
),
supervised_keys=None,
homepage="https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/People's%20Daily",
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
line_stripped = line.strip()
if line_stripped == "":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
splits = line_stripped.split(" ")
if len(splits) == 1:
splits.append("O")
tokens.append(splits[0])
ner_tags.append(splits[1])
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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