Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Chinese
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
| # 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 = "" | |
| _HOMEPAGE_URL = "" | |
| _CITATION = None | |
| _TRAIN_URL = "https://huggingface.co/datasets/ayuhamaro/ner-model-tune/raw/main/train" | |
| class NlpModelTune(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=[ | |
| "O", | |
| "B-CARDINAL", | |
| "B-DATE", | |
| "B-EVENT", | |
| "B-FAC", | |
| "B-GPE", | |
| "B-LANGUAGE", | |
| "B-LAW", | |
| "B-LOC", | |
| "B-MONEY", | |
| "B-NORP", | |
| "B-ORDINAL", | |
| "B-ORG", | |
| "B-PERCENT", | |
| "B-PERSON", | |
| "B-PRODUCT", | |
| "B-QUANTITY", | |
| "B-TIME", | |
| "B-WORK_OF_ART", | |
| "I-CARDINAL", | |
| "I-DATE", | |
| "I-EVENT", | |
| "I-FAC", | |
| "I-GPE", | |
| "I-LANGUAGE", | |
| "I-LAW", | |
| "I-LOC", | |
| "I-MONEY", | |
| "I-NORP", | |
| "I-ORDINAL", | |
| "I-ORG", | |
| "I-PERCENT", | |
| "I-PERSON", | |
| "I-PRODUCT", | |
| "I-QUANTITY", | |
| "I-TIME", | |
| "I-WORK_OF_ART", | |
| "E-CARDINAL", | |
| "E-DATE", | |
| "E-EVENT", | |
| "E-FAC", | |
| "E-GPE", | |
| "E-LANGUAGE", | |
| "E-LAW", | |
| "E-LOC", | |
| "E-MONEY", | |
| "E-NORP", | |
| "E-ORDINAL", | |
| "E-ORG", | |
| "E-PERCENT", | |
| "E-PERSON", | |
| "E-PRODUCT", | |
| "E-QUANTITY", | |
| "E-TIME", | |
| "E-WORK_OF_ART", | |
| "S-CARDINAL", | |
| "S-DATE", | |
| "S-EVENT", | |
| "S-FAC", | |
| "S-GPE", | |
| "S-LANGUAGE", | |
| "S-LAW", | |
| "S-LOC", | |
| "S-MONEY", | |
| "S-NORP", | |
| "S-ORDINAL", | |
| "S-ORG", | |
| "S-PERCENT", | |
| "S-PERSON", | |
| "S-PRODUCT", | |
| "S-QUANTITY", | |
| "S-TIME", | |
| "S-WORK_OF_ART" | |
| ] | |
| ) | |
| ), | |
| }, | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE_URL, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| train_path = dl_manager.download_and_extract(_TRAIN_URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"data_path": train_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 | |