readme: ---
  license: cc-by-4.0
  tags:
  - int8
  - Intel® Neural Compressor
  - PostTrainingStatic
  datasets: 
  - squad2
  metrics:
  - f1
  ---
  
  # INT8 RoBERT base finetuned on Squad2
  
  ### Post-training static quantization
  
  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 [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2).
  
  The calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the real sampling size is 104.
  
  The linear modules **roberta.encoder.layer.7.output.dense**, **roberta.encoder.layer.8.output.dense**, **roberta.encoder.layer.9.output.dense**, fall back to fp32 for less than 1% relative accuracy loss.
  
  ### Evaluation result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-f1)** |82.3122|82.9231|
  | **Model size (MB)**  |141|474|
  
  ### Load with optimum:
  
  ```python
  from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering
  int8_model = IncQuantizedModelForQuestionAnswering.from_pretrained(
      'Intel/roberta-base-squad2-int8-static',
  )
  ```
  