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---
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language:
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- en
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license: apache-2.0
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tags:
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- toxic
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- toxicity
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- offensive language
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- hate speech
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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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# Load the Model
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```py
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from transformers import pipeline
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pipe = pipeline(model='minuva/MiniLMv2-toxic-jigsaw', task='text-classification')
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pipe("This is pure trash")
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# [{'label': 'toxic', 'score': 0.9383478164672852}]
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```
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# Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 6e-05
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- train_batch_size: 48
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- eval_batch_size: 48
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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- warmup_ratio: 0.1
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# Metrics (comparison with teacher model)
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| Teacher (params) | Student (params) | Set (metric) | Score (teacher) | Score (student) |
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|--------------------|-------------|----------|--------| --------|
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| unitary/toxic-bert (110M) | MiniLMv2-toxic-jigsaw (23M) | Test (ROC_AUC) | 0.98636 | 0.98600 |
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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. |
