Edit model card

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
Downloads last month
66
Hosted inference API
Text Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.