readme: ---
  language: en
  license: apache-2.0
  tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - PostTrainingStatic
  datasets: 
  - sst2
  metrics:
  - accuracy
  ---
  
  # INT8 DistilBERT base uncased finetuned SST-2
  
  ## Post-training static quantization
  
  ### PyTorch
  
  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-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).
  
  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.
  
  #### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-accuracy)** |0.9037|0.9106|
  | **Model size (MB)**  |65|255|
  
  #### Load with optimum:
  
  ```python
  from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification
  int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(
      'Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static',
  )
  ```
  
  ### ONNX
  
  This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
  
  The original fp32 model comes from the fine-tuned model [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).
  
  
  #### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-f1)** |0.9060|0.9106|
  | **Model size (MB)**  |80|256|
  
  #### Load ONNX model:
  
  ```python
  from optimum.onnxruntime import ORTModelForSequenceClassification
  model = ORTModelForSequenceClassification.from_pretrained('Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static')
  ```