Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Portuguese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # 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. | |
| """ | |
| (pt) NERDE: NER na Defesa Econômica | |
| (en) NERDE: NER on Economic Defense | |
| """ | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| #_CITATION = """""" | |
| _DESCRIPTION = """ | |
| (pt) NERDE é um dataset para NER a partir de documentos jurídicos da defesa econômica em português do Brasil, foi criado em colaboração com o Cade e o laboratório LATITUDE/UnB. | |
| (en) NERDE is a NER dataset from economic defense legal documents in Brazilian Portuguese, created in collaboration with Cade and the LATITUDE/UnB laboratory. | |
| """ | |
| _HOMEPAGE = "https://github.com/guipaiva/NERDE" | |
| _TRAINING_FILE = "train.conll" | |
| _DEV_FILE = "dev.conll" | |
| _TEST_FILE = "test.conll" | |
| class NerdeDataset(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name="NERDE", version=VERSION, | |
| description="Economic Defense 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-ORG", | |
| "I-ORG", | |
| "B-PER", | |
| "I-PER", | |
| "B-TEMPO", | |
| "I-TEMPO", | |
| "B-LOC", | |
| "I-LOC", | |
| "B-LEG", | |
| "I-LEG", | |
| "B-DOCS", | |
| "I-DOCS", | |
| "B-VALOR", | |
| "I-VALOR" | |
| ] | |
| ) | |
| ), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| urls_to_download = { | |
| "train": _TRAINING_FILE, | |
| "dev": _DEV_FILE, | |
| "test": _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"], "split": "train"}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": downloaded_files["dev"], "split": "validation"}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": downloaded_files["test"], "split": "test"}, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, split): | |
| """Yields examples.""" | |
| 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 == "" or line == "\n": | |
| if tokens: | |
| yield guid, { | |
| "id": str(guid), | |
| "tokens": tokens, | |
| "ner_tags": ner_tags, | |
| } | |
| guid += 1 | |
| tokens = [] | |
| ner_tags = [] | |
| else: | |
| 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, | |
| } | |