Datasets:
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README.md
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InToxiCat is a dataset for the detection of abusive language (defined by the aim to harm someone, individual, group, etc.) in Catalan, produced by the BSC LangTech unit.
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The dataset consists of 29,809 sentences obtained from internet forums annotated as to whether or not they are abusive. The 6047 instances annotated as abusive are further annotated for following features:
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The dataset is split, in a balanced abusive/non-abusive distribution, into 23,847 training samples, 2981 validation samples, and 2981 test samples.
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- group: considered to be a unit based on the same ethnicity, gender or sexual orientation, political affiliation, religious belief or something else.
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- other; e.g. an organization, a situation, an event, or an issue
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The annotation guidelines are published and available on
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#### Who are the annotators?
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The annotators were qualified professionals with
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### Personal and Sensitive Information
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InToxiCat is a dataset for the detection of abusive language (defined by the aim to harm someone, individual, group, etc.) in Catalan, produced by the BSC LangTech unit.
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The dataset consists of 29,809 sentences obtained from internet forums annotated as to whether or not they are abusive. The 6047 instances annotated as abusive are further annotated for the following features: abusive span, target span, target type and the implicit or explicit nature of the abusiveness in the message.
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The dataset is split, in a balanced abusive/non-abusive distribution, into 23,847 training samples, 2981 validation samples, and 2981 test samples.
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- group: considered to be a unit based on the same ethnicity, gender or sexual orientation, political affiliation, religious belief or something else.
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- other; e.g. an organization, a situation, an event, or an issue
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The annotation guidelines are published and available on Zenodo.
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#### Who are the annotators?
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The annotators were qualified professionals with university education and a demonstrably excellent knowledge of Catalan (minimum level C1 or equivalent).
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### Personal and Sensitive Information
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