readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - neural-compressor
  - PostTrainingStatic
  datasets:
  - glue
  metrics:
  - accuracy
  model_index:
  - name: sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE SST2
        type: glue
        args: sst2
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9254587155963303
  ---
  # INT8 albert-base-v2-sst2
  
  ##  Post-training static quantization
  
  ### PyTorch
  
  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 [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).
  
  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 modules **albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.module, albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.module** fall back to fp32 to meet the 1% relative accuracy loss.
  
  #### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-accuracy)** |0.9255|0.9232|
  | **Model size (MB)**  |25|44.6|
  
  #### Load with Intel® Neural Compressor:
  
  ```python
  from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification
  
  model_id = "Intel/albert-base-v2-sst2-int8-static"
  int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id)
  ```
  
  ### 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 [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).
  
  #### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-f1)** |0.9186|0.9232|
  | **Model size (MB)**  |89|45|
  
  
  #### Load ONNX model:
  
  ```python
  from optimum.onnxruntime import ORTModelForSequenceClassification
  model = ORTModelForSequenceClassification.from_pretrained('Intel/albert-base-v2-sst2-int8-static')
  ```