File size: 5,505 Bytes
e83e85c 044fc87 e83e85c d084710 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
# 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 the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the Fault_Detection_Ner Task: Language-Independent Named Entity Recognition",
author = "Tian Jie",
year = "2022"
}
"""
_DESCRIPTION = """\
用于故障诊断领域相关知识的命名实体识别语料
"""
_URL = "https://cdn-lfs.huggingface.co/datasets/leonadase/fdner/89a87eacfebc06862ac4b5a356c35430dfdf8ef2f0f2e0d9ff5e02ce6c117474"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "valid.txt"
_TEST_FILE = "test.txt"
class fdnerConfig(datasets.BuilderConfig):
"""BuilderConfig for fdNer"""
def __init__(self, **kwargs):
"""BuilderConfig for fdNer.
Args:
**kwargs: keyword arguments forwarded to super.
"""
logger.info("Generating examples from 1")
super(fdnerConfig, self).__init__(**kwargs)
class fdner(datasets.GeneratorBasedBuilder):
"""fdNer dataset."""
BUILDER_CONFIGS = [
fdnerConfig(name="fdner", version=datasets.Version("1.0.0"), description="fdner dataset"),
]
def _info(self):
logger.info("Generating examples from 1")
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-EN",
"I-EN",
"B-STRUC",
"I-STRUC",
"B-CHA",
"I-CHA",
"B-KIND",
"I-KIND",
"B-ADV",
"I-ADV",
"B-DISA",
"I-DISA",
"B-METH",
"I-METH",
"B-NUM",
"I-NUM",
"B-PRO",
"I-PRO",
"B-THE",
"I-THE",
"B-DEF",
"I-DEF",
"B-FUC",
"I-FUC",
]
)
),
}
),
supervised_keys=None,
# homepage="https://www.aclweb.org/anthology/W03-0419/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
logger.info("Generating examples from 2")
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract(_URL)
data_files = {
"train": os.path.join(downloaded_file, _TRAINING_FILE),
"dev": os.path.join(downloaded_file, _DEV_FILE),
"test": os.path.join(downloaded_file, _TEST_FILE),
}
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_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:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# conll2003 tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
|