Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Chinese
Size:
10K<n<100K
License:
File size: 4,563 Bytes
ae36bb0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
# 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 logging
import datasets
_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):
logging.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,
}
|