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"""OffensEval-TR 2020: A Corpus of Turkish Offensive Language on Social Media""" |
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import csv |
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import os |
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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{coltekin2020lrec, |
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author = {Cagri Coltekin}, |
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year = {2020}, |
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title = {A Corpus of Turkish Offensive Language on Social Media}, |
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booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, |
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pages = {6174--6184}, |
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address = {Marseille, France}, |
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url = {https://www.aclweb.org/anthology/2020.lrec-1.758}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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OffensEval-TR 2020 is a Turkish offensive language corpus. The corpus consist of randomly sampled tweets and annotated in a similar way to OffensEval and GermEval. |
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""" |
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_HOMEPAGE = "https://coltekin.github.io/offensive-turkish/" |
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_DOWNLOAD_URL = "https://coltekin.github.io/offensive-turkish/offenseval2020-turkish.zip" |
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_FOLDER_NAME = "offenseval-tr-{split}-v1" |
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class OffensEval2020TRConfig(datasets.BuilderConfig): |
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"""BuilderConfig for OffensEval2020TR.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for OffensEval2020TR. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(OffensEval2020TRConfig, self).__init__(**kwargs) |
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class Offenseval2020TR(datasets.GeneratorBasedBuilder): |
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"""OffensEval-TR 2020: A Corpus of Turkish Offensive Language on Social Media""" |
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BUILDER_CONFIGS = [ |
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OffensEval2020TRConfig( |
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name="offenseval2020-turkish", |
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version=datasets.Version("1.0.0"), |
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description="OffensEval-TR 2020: A Corpus of Turkish Offensive Language on Social Media", |
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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("int32"), |
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"tweet": datasets.Value("string"), |
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"subtask_a": datasets.features.ClassLabel(names=["NOT", "OFF"]), |
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} |
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), |
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supervised_keys=None, |
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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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dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL) |
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data_dir = os.path.join(dl_dir, self.config.name) |
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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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"filepath": os.path.join( |
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data_dir, _FOLDER_NAME.format(split="training"), "offenseval-tr-training-v1.tsv" |
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), |
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"labelpath": None, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join( |
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data_dir, _FOLDER_NAME.format(split="testset"), "offenseval-tr-testset-v1.tsv" |
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), |
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"labelpath": os.path.join( |
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data_dir, _FOLDER_NAME.format(split="testset"), "offenseval-tr-labela-v1.tsv" |
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), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, labelpath): |
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"""Generate OffensEval2020TR examples.""" |
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logger.info("⏳ Generating examples from = %s", filepath) |
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if labelpath: |
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with open(filepath, encoding="utf-8") as f: |
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with open(labelpath, encoding="utf-8") as f2: |
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reader_testset = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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reader_label = csv.DictReader( |
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f2, delimiter=",", quoting=csv.QUOTE_NONE, fieldnames=["id", "subtask_a"] |
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) |
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list_label = list(reader_label) |
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for idx, row in enumerate(reader_testset): |
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row_label = list_label[idx] |
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yield idx, {"id": row["id"], "tweet": row["tweet"], "subtask_a": row_label["subtask_a"]} |
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else: |
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with open(filepath, encoding="utf-8") as f: |
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for idx, row in enumerate(reader): |
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yield idx, {"id": row["id"], "tweet": row["tweet"], "subtask_a": row["subtask_a"]} |
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