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README.md
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## Intended uses & limitations
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The intended use of the model is to classify English-language, emoji-containing, short-form text documents as a binary task: non-hateful vs hateful. The model has demonstrated strengths compared to commercial and academic models on classifying emoji-based hate, but is also a strong classifier of text-only hate. Because the model was trained on synthetic, adversarially-generated data, it may have some weaknesses when it comes to empirical emoji-based hate 'in-the-wild'.
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You can interact with this model on [Dynabench](https://dynabench.org/tasks/hs), and find its limitations. We hope to continue improving the model on new adversarial data to better iron out its weaknesses!
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## How to use
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The model can be used with pipeline:
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## Intended uses & limitations
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The intended use of the model is to classify English-language, emoji-containing, short-form text documents as a binary task: non-hateful vs hateful. The model has demonstrated strengths compared to commercial and academic models on classifying emoji-based hate, but is also a strong classifier of text-only hate. Because the model was trained on synthetic, adversarially-generated data, it may have some weaknesses when it comes to empirical emoji-based hate 'in-the-wild'.
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You can interact with this model on [Dynabench](https://dynabench.org/tasks/hs), and find its limitations. We hope to continue improving the model on new adversarial data to better iron out its remaining weaknesses!
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## How to use
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The model can be used with pipeline:
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