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README.md
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@@ -15,7 +15,17 @@ This model is a fine-tuned version of the [DeBERTa base model](https://huggingfa
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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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## How to use
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### Training data
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The model was trained on:
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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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## How to use
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The model can be used with pipeline:
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```python
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from transformers import pipeline
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classifier = pipeline("text-classification",model='HannahRoseKirk/Hatemoji', return_all_scores=True)
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prediction = classifier("I πππ emoji π", )
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print(prediction)
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"""
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Output
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[[{'label': 'LABEL_0', 'score': 0.9999157190322876}, {'label': 'LABEL_1', 'score': 8.425049600191414e-05}]]
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"""
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```
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### Training data
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The model was trained on:
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