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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{aghajani-etal-2021-parstwiner, |
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title = "{P}ars{T}wi{NER}: A Corpus for Named Entity Recognition at Informal {P}ersian", |
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author = "Aghajani, MohammadMahdi and |
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Badri, AliAkbar and |
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Beigy, Hamid", |
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booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)", |
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month = nov, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.wnut-1.16", |
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pages = "131--136", |
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abstract = "As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort. ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen{'}s Kappa coefficient, the consistency of annotators is 0.95, a high score. In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture. Experimental results show that the model works well in informal Persian as well as in formal Persian.", |
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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/parstwiner/resolve/main/parstwiner_train.csv", |
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"test": "https://huggingface.co/datasets/hezarai/parstwiner/resolve/main/parstwiner_test.csv", |
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} |
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class ParsTwiNERConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(ParsTwiNERConfig, self).__init__(**kwargs) |
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class ParsTwiNER(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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ParsTwiNERConfig( |
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name="ParsTwiNER", |
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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-POG", |
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"I-POG", |
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"B-PER", |
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"I-PER", |
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"B-ORG", |
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"I-ORG", |
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"B-NAT", |
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"I-NAT", |
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"B-LOC", |
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"I-LOC", |
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"B-EVE", |
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"I-EVE", |
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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://github.com/overfit-ir/parstwiner", |
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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(csv_file, quotechar='"', skipinitialspace=True) |
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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} |
