readme: ---
  license: mit
  tags:
  - int8
  - Intel® Neural Compressor
  - neural-compressor
  - PostTrainingStatic
  datasets: 
  - mnli
  metrics:
  - accuracy
  ---
  
  # INT8 RoBERT large finetuned on MNLI
  
  ### Post-training static quantization
  
  This is an INT8  PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
  
  The original fp32 model comes from the fine-tuned model [roberta-large-mnli](https://huggingface.co/roberta-large-mnli).
  
  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.16.output.dense**, **roberta.encoder.layer.17.output.dense**, **roberta.encoder.layer.18.output.dense**, fall back to fp32 for less than 1% relative accuracy loss.
  
  ### Evaluation result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-acc)** |89.8624|90.5960|
  | **Model size (MB)**  |381M|1.4G|
  
  ### Load with Intel® Neural Compressor:
  
  ```python
  from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification
  
  model_id = "Intel/roberta-base-squad2-int8-static"
  int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id)
  ```
  