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  language:
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  # Text Classification GoEmotions
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- This model is a fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large) on the on the [Jigsaw 1st Kaggle competition](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) dataset using [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) as teacher model.
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  The quantized version in ONNX format can be found [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-onnx).
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  The model with two labels only (toxicity and severe toxicity) is [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-lite)
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  # Deployment
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- Check this [repository](https://github.com/minuva/toxicity-prediction-serverless) to see how to easily deploy this model in a serverless environment with fast CPU inference and light resource utilization.
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+ license: apache-2.0
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  ---
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  # Text Classification GoEmotions
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+ This model is a fined-tuned version of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large) on the on the [Jigsaw 1st Kaggle competition](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) dataset using [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) as teacher model.
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  The quantized version in ONNX format can be found [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-onnx).
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  The model with two labels only (toxicity and severe toxicity) is [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-lite)
 
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  # Deployment
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+ Check out [fast-nlp-text-toxicity repository](https://github.com/minuva/fast-nlp-text-toxicity) for a FastAPI based server to deploy this model in CPU devices.