readme: ---
  language: en
  license: apache-2.0
  tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - QuantizationAwareTraining
  datasets: 
  - mrpc
  metrics:
  - f1
  ---
  
  # INT8 BERT base uncased finetuned MRPC
  
  ### QuantizationAwareTraining
  
  This is an INT8  PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
  
  The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
  
  ### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-f1)** |0.9142|0.9042|
  | **Model size (MB)**  |107|418|
  
  ### Load with optimum:
  
  ```python
  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
  