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. 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-jijgsaw', 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 easily deploy this model in a serverless environment with fast CPU inference and light resource utilization.