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import json |
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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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_DESCRIPTION = """ InToxiCat is a dataset for the detection of abusive language in Catalan. """ |
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_HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/InToxiCat""" |
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_URL = "https://huggingface.co/datasets/projecte-aina/InToxicat/resolve/main/" |
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_FILE_TRAIN = "train.json" |
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_FILE_DEV = "dev.json" |
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_FILE_TEST = "test.json" |
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class InToxiCatConfig(datasets.BuilderConfig): |
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""" Builder config for the InToxiCat dataset """ |
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def __init__(self, **kwargs): |
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"""BuilderConfig for InToxiCat. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(InToxiCatConfig, self).__init__(**kwargs) |
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class InToxiCat(datasets.GeneratorBasedBuilder): |
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""" InToxiCat Dataset """ |
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BUILDER_CONFIGS = [ |
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InToxiCatConfig( |
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name="InToxiCat", |
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version=datasets.Version("1.0.0"), |
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description="InToxiCat dataset", |
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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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"id": datasets.Value("string"), |
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"context": datasets.Value("string"), |
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"sentence": datasets.Value("string"), |
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"topic": datasets.Value("string"), |
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"keywords": datasets.Sequence(datasets.Value("string")), |
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"context_needed": datasets.Value("string"), |
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"is_abusive": datasets.features.ClassLabel(names=['abusive','not_abusive']), |
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"abusiveness_agreement": datasets.Value("string"), |
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"target_type": datasets.Sequence(datasets.features.ClassLabel(names=['INDIVIDUAL','GROUP','OTHERS'])), |
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"abusive_spans": datasets.Sequence(feature={'text': datasets.Value(dtype='string', id=None), 'index': datasets.Value(dtype='string', id=None)}, length=-1, id=None), |
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"target_spans": datasets.Sequence(feature={'text': datasets.Value(dtype='string', id=None), 'index': datasets.Value(dtype='string', id=None)}, length=-1, id=None), |
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"is_implicit": datasets.Value("string") |
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} |
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), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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urls_to_download = { |
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"train": f"{_FILE_TRAIN}", |
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"dev": f"{_FILE_DEV}", |
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"test": f"{_FILE_TEST}" |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}) |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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data = json.load(open(filepath, 'r')) |
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for id_, example in enumerate(data): |
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yield id_, { |
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"id": example["id"], |
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"context": example["context"], |
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"sentence": example["sentence"], |
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"topic": example["topic"], |
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"keywords": example["key_words"], |
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"context_needed": example["annotation"]["context_needed"] if example["annotation"]["context_needed"] else None, |
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"is_abusive": example["annotation"]["is_abusive"] if example["annotation"]["is_abusive"] else None, |
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"abusiveness_agreement": example["annotation"]["abusiveness_agreement"], |
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"target_type": example["annotation"]["target_type"] if example["annotation"]["target_type"] else None, |
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"abusive_spans": { |
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"text": [text for text, _ in example["annotation"]["abusive_spans"]], |
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"index": [index for _, index in example["annotation"]["abusive_spans"]] |
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} if example["annotation"]["abusive_spans"] != [] else None, |
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"target_spans": { |
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"text": [text for text, _ in example["annotation"]["target_spans"]], |
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"index": [index for _, index in example["annotation"]["target_spans"]] |
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} if example["annotation"]["target_spans"] != [] else None, |
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"is_implicit": example["annotation"]["is_implicit"] if example["annotation"]["is_implicit"] != "" else None |
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} |
