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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 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"
# _URL = "https://cdn-lfs.huggingface.co/datasets/leonadase/fdner/a39f75df8cb9e419024417c36d7a21acfe79f7fdd2f31a4a9f0658adf734c2f1"
_URL = "https://cdn-lfs.huggingface.co/datasets/leonadase/fdner/26cceb62fd684d1624bfd00f22553b742b6f3f3c945a8b64ea2d84a8a6ea2f96"
_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,
}
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