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language: |
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- en |
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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](minuva/MiniLMv2-toxic-jijgsaw-onnx). The model with two labels only (toxicity and severe toxicity) is [here](minuva/MiniLMv2-toxic-jijgsaw-lite-onnx) |
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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-jijgsaw', 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-L6-H384-goemotions-v2 (23M) | Test (ROC_AUC) | 0.98636 | 0.98600 | |
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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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