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"""An annotated dataset for classifying offensive or acceptable speech.""" |
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import os |
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import csv |
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import datasets |
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_CITATION = """\ |
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@misc{ljubešić2019frenk, |
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title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, |
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author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, |
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year={2019}, |
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eprint={1906.02045}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/1906.02045} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The FRENK Datasets of Socially Unacceptable Discourse in Slovene. |
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""" |
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_HOMEPAGE = "https://www.clarin.si/repository/xmlui/handle/11356/1433" |
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_LICENSE = "CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0" |
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_URL = "https://huggingface.co/datasets/classla/FRENK-hate-sl/resolve/main/data.zip" |
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_CLASS_MAP_MULTICLASS = { |
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'Acceptable speech': 0, |
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'Inappropriate': 1, |
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'Background offensive': 2, |
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'Other offensive': 3, |
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'Background violence': 4, |
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'Other violence': 5, |
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} |
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_CLASS_MAP_BINARY = { |
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'Acceptable': 0, |
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'Offensive': 1, |
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} |
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class FRENKHateSpeechSL(datasets.GeneratorBasedBuilder): |
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"""The FRENK Datasets of Socially Unacceptable Discourse in Slovene""" |
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VERSION = datasets.Version("0.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="binary", version=VERSION, |
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description="Labels are either 'Offensive' or 'Acceptable'."), |
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datasets.BuilderConfig(name="multiclass", version=VERSION, |
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description="Labels are 'Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence'"), |
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] |
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DEFAULT_CONFIG_NAME = "binary" |
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def _info(self): |
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feature_dict = { |
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"text": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"topic": datasets.Value("string"), |
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} |
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if self.config.name == "binary": |
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features = datasets.Features( |
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{ |
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**feature_dict, |
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"label": datasets.ClassLabel(names=["Acceptable", "Offensive"]), |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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**feature_dict, |
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"label": datasets.ClassLabel(names=['Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence']), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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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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data_file = dl_manager.download_and_extract(_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={ |
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'filepath': os.path.join(data_file, "train.tsv"), |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={ |
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'filepath': os.path.join(data_file, "dev.tsv"), |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={ |
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'filepath': os.path.join(data_file, "test.tsv"), |
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} |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Yields examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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reader = csv.reader(f, delimiter="\t") |
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for id_, row in enumerate(reader): |
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if id_ == 0: |
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continue |
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to_return_dict = { |
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"text": row[1], |
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"target": row[4] , |
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"topic": row[5] |
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} |
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yield id_, { |
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**to_return_dict, |
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**{"label": _CLASS_MAP_BINARY[row[3]] if self.config.name == "binary" else _CLASS_MAP_MULTICLASS[row[2]]} |
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} |
