readme: ---
  license: apache-2.0
  tags:
  - int8
  - Intel® Neural Compressor
  - PostTrainingStatic
  datasets: 
  - squad
  metrics:
  - f1
  ---
  
  # INT8 DistilBERT base cased finetuned on Squad
  
  ### 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 [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad).
  
  The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.
  
  The linear module **distilbert.transformer.layer.1.ffn.lin2** falls back to fp32 to meet the 1% relative accuracy loss.
  
  ### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-f1)** |86.0005|86.8373|
  | **Model size (MB)**  |71.2|249|
  
  ### Load with optimum:
  
  ```python
  from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering
  int8_model = IncQuantizedModelForQuestionAnswering(
      'Intel/distilbert-base-cased-distilled-squad-int8-static',
  )
  ```
  