INT8 BERT base uncased finetuned MRPC
QuantizationAwareTraining
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.9142 | 0.9042 |
| Model size (MB) | 107 | 418 |
Load with optimum:
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification
int8_model = IncQuantizedModelForSequenceClassification(
'Intel/bert-base-uncased-mrpc-int8-qat',
)
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- train_batch_size: 8
- eval_batch_size: 8
- eval_steps: 100
- load_best_model_at_end: True
- metric_for_best_model: f1
- early_stopping_patience = 6
- early_stopping_threshold = 0.001
