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Datasets:
hezarai
/
arman-ner

Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
Persian
Size:
10K - 100K
Libraries:
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pandas
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1
arman-ner
File size: 3,619 Bytes 
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import csv
from ast import literal_eval

import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """
@inproceedings{poostchi-etal-2018-bilstm,
    title = "{B}i{LSTM}-{CRF} for {P}ersian Named-Entity Recognition {A}rman{P}erso{NERC}orpus: the First Entity-Annotated {P}ersian Dataset",
    author = "Poostchi, Hanieh  and
      Zare Borzeshi, Ehsan  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1701",
}
"""

_DESCRIPTION = """"""

_DOWNLOAD_URLS = {
    "train": "https://huggingface.co./datasets/hezarai/arman-ner/resolve/main/arman-ner_train.csv",
    "test": "https://huggingface.co./datasets/hezarai/arman-ner/resolve/main/arman-ner_test.csv",
}


class ArmanNERConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(ArmanNERConfig, self).__init__(**kwargs)


class ArmanNER(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        ArmanNERConfig(
            name="Arman-NER",
            version=datasets.Version("1.0.0"),
            description=_DESCRIPTION,
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-pro",
                                "I-pro",
                                "B-pers",
                                "I-pers",
                                "B-org",
                                "I-org",
                                "B-loc",
                                "I-loc",
                                "B-fac",
                                "I-fac",
                                "B-event",
                                "I-event"
                            ]
                        )
                    ),
                }
            ),
            homepage="https://huggingface.co./datasets/hezarai/arman-ner",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """
        Return SplitGenerators.
        """

        train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
        test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
            ),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(
                csv_file, quotechar='"', skipinitialspace=True
            )

            next(csv_reader, None)

            for id_, row in enumerate(csv_reader):
                tokens, ner_tags = row
                # Optional preprocessing here
                tokens = literal_eval(tokens)
                ner_tags = literal_eval(ner_tags)
                yield id_, {"tokens": tokens, "ner_tags": ner_tags}