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Datasets:
Gpaiva
/
NERDE

Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
Portuguese
Size:
10K - 100K
Tags:
ner
portuguese-ner
economic-defense
Libraries:
Datasets
License:
Dataset card Viewer Files Community
NERDE
File size: 4,690 Bytes 
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# 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,
            }