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"""HateSpeech Corpus for Polish""" |
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import csv |
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import datasets |
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_CITATION = r"""\ |
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@article{troszynski2017czy, |
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title={Czy komputer rozpozna hejtera? Wykorzystanie uczenia maszynowego (ML) w jako{\'s}ciowej analizie danych}, |
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author={Troszy{\'n}ski, Marek and Wawer, Aleksandra}, |
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journal={Przegl{\k{a}}d Socjologii Jako{\'s}ciowej}, |
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volume={13}, |
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number={2}, |
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pages={62--80}, |
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year={2017}, |
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publisher={Uniwersytet {\L}{\'o}dzki, Wydzia{\l} Ekonomiczno-Socjologiczny, Katedra Socjologii~…} |
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} |
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""" |
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_DESCRIPTION = """\ |
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HateSpeech corpus in the current version contains over 2000 posts crawled from public Polish web. They represent various types and degrees of offensive language, expressed toward minorities (eg. ethnical, racial). The data were annotated manually. |
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""" |
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_HOMEPAGE = "http://zil.ipipan.waw.pl/HateSpeech" |
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_LICENSE = "CC BY-NC-SA" |
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_URLs = [ |
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"https://raw.githubusercontent.com/aiembassy/hatespeech-corpus-pl/master/data/fragment_anotatora_2011_ZK.csv", |
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"https://raw.githubusercontent.com/aiembassy/hatespeech-corpus-pl/master/data/fragment_anotatora_2011b.csv", |
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"https://raw.githubusercontent.com/aiembassy/hatespeech-corpus-pl/master/data/fragment_anotatora_2012_luty.csv", |
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] |
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class HateSpeechPl(datasets.GeneratorBasedBuilder): |
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"""HateSpeech Corpus for Polish""" |
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VERSION = datasets.Version("1.1.0") |
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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("uint16"), |
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"text_id": datasets.Value("uint32"), |
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"annotator_id": datasets.Value("uint8"), |
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"minority_id": datasets.Value("uint8"), |
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"negative_emotions": datasets.Value("bool"), |
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"call_to_action": datasets.Value("bool"), |
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"source_of_knowledge": datasets.Value("uint8"), |
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"irony_sarcasm": datasets.Value("bool"), |
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"topic": datasets.Value("uint8"), |
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"text": datasets.Value("string"), |
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"rating": datasets.Value("uint8"), |
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} |
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), |
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supervised_keys=None, |
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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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my_urls = _URLs |
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filepaths = dl_manager.download(my_urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepaths": filepaths, |
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}, |
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), |
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] |
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def _generate_examples(self, filepaths): |
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"""Yields examples.""" |
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for file_id_, filepath in enumerate(filepaths): |
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with open(filepath, encoding="utf-8") as f: |
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csv_reader = csv.DictReader(f, delimiter=",", escapechar="\\") |
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for id_, data in enumerate(csv_reader): |
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yield f"{file_id_}/{id_}", { |
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"id": data["id_fragmentu"], |
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"text_id": data["id_tekstu"], |
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"annotator_id": data["id_anotatora"], |
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"minority_id": data["id_mniejszosci"], |
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"negative_emotions": data["negatywne_emocje"], |
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"call_to_action": data["wezw_ddzial"], |
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"source_of_knowledge": data["typ_ramki"], |
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"irony_sarcasm": data["ironia_sarkazm"], |
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"topic": data["temat"], |
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"text": data["tekst"], |
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"rating": data["ocena"], |
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} |
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