metadata
language:
- en
Text Classification GoEmotions
This model is a fined-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large on the on the Jigsaw 1st Kaggle competition dataset using unitary/toxic-bert as teacher model.
Load the Model
from transformers import pipeline
pipe = pipeline(model='Ngit/MiniLM-L6-toxic-all-labels', 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-L6-H384-goemotions-v2 (23M) | Test (ROC_AUC) | 0.98636 | 0.98600 |
Deployment
Check this repository to see how to deploy in a serveless enviroment with onnnxruntime