Text Classification GoEmotions
This model is a fined-tuned version of MiniLMv2-L6-H384 on the on the Jigsaw 1st Kaggle competition dataset using unitary/toxic-bert as teacher model. The quantized version in ONNX format can be found here.
The model with two labels only (toxicity and severe toxicity) is here
Load the Model
from transformers import pipeline
pipe = pipeline(model='minuva/MiniLMv2-toxic-jigsaw', task='text-classification')
pipe("This is pure trash")
# [{'label': 'toxic', 'score': 0.9383478164672852}]
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 48
- eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- warmup_ratio: 0.1
Metrics (comparison with teacher model)
| Teacher (params) | Student (params) | Set (metric) | Score (teacher) | Score (student) |
|---|---|---|---|---|
| unitary/toxic-bert (110M) | MiniLMv2-toxic-jigsaw (23M) | Test (ROC_AUC) | 0.98636 | 0.98600 |
Deployment
Check out fast-nlp-text-toxicity repository for a FastAPI based server to deploy this model in CPU devices.