readme: ---
  language: en
  license: apache-2.0
  tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - PostTrainingDynamic
  - onnx
  datasets: 
  - mrpc
  metrics:
  - f1
  ---
  
  # INT8 BERT base uncased finetuned MRPC
  
  ## Post-training dynamic 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 [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
  
  #### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-f1)** |0.8997|0.9042|
  | **Model size (MB)**  |174|418|
  
  #### Load with optimum:
  
  ```python
  from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification
  int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(
      'Intel/bert-base-uncased-mrpc-int8-dynamic',
  )
  ```
  
  ### 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 [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
  
  #### Test result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-f1)** |0.8958|0.9042|
  | **Model size (MB)**  |107|418|
  
  
  #### Load ONNX model:
  
  ```python
  from optimum.onnxruntime import ORTModelForSequenceClassification
  model = ORTModelForSequenceClassification.from_pretrained('Intel/bert-base-uncased-mrpc-int8-dynamic')
  ```
  
  