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import csv |
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from ast import literal_eval |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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@inproceedings{poostchi-etal-2018-bilstm, |
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title = "{B}i{LSTM}-{CRF} for {P}ersian Named-Entity Recognition {A}rman{P}erso{NERC}orpus: the First Entity-Annotated {P}ersian Dataset", |
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author = "Poostchi, Hanieh and |
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Zare Borzeshi, Ehsan and |
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Piccardi, Massimo", |
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booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", |
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month = may, |
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year = "2018", |
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address = "Miyazaki, Japan", |
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publisher = "European Language Resources Association (ELRA)", |
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url = "https://aclanthology.org/L18-1701", |
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} |
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""" |
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_DESCRIPTION = """""" |
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_DOWNLOAD_URLS = { |
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"train": "https://huggingface.co/datasets/hezarai/arman-ner/resolve/main/arman-ner_train.csv", |
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"test": "https://huggingface.co/datasets/hezarai/arman-ner/resolve/main/arman-ner_test.csv", |
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} |
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class ArmanNERConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(ArmanNERConfig, self).__init__(**kwargs) |
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class ArmanNER(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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ArmanNERConfig( |
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name="Arman-NER", |
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version=datasets.Version("1.0.0"), |
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description=_DESCRIPTION, |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"B-pro", |
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"I-pro", |
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"B-pers", |
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"I-pers", |
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"B-org", |
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"I-org", |
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"B-loc", |
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"I-loc", |
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"B-fac", |
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"I-fac", |
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"B-event", |
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"I-event" |
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] |
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) |
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), |
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} |
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), |
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homepage="https://huggingface.co/datasets/hezarai/arman-ner", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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""" |
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Return SplitGenerators. |
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""" |
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train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"]) |
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test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": test_path} |
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), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as csv_file: |
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csv_reader = csv.reader( |
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csv_file, quotechar='"', skipinitialspace=True |
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) |
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next(csv_reader, None) |
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for id_, row in enumerate(csv_reader): |
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tokens, ner_tags = row |
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tokens = literal_eval(tokens) |
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ner_tags = literal_eval(ner_tags) |
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yield id_, {"tokens": tokens, "ner_tags": ner_tags} |
