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@@ -16,7 +16,7 @@ metrics:
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  ### Post-training dynamic quantization
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- This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
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  The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
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@@ -27,11 +27,11 @@ The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased
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  | **Accuracy (eval-f1)** |0.8997|0.9042|
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  | **Model size (MB)** |174|418|
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- ### Load with Intel® Neural Compressor:
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  ```python
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- from neural_compressor.utils.load_huggingface import OptimizedModel
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- int8_model = OptimizedModel.from_pretrained(
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  'Intel/bert-base-uncased-mrpc-int8-dynamic',
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  )
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  ```
 
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  ### Post-training dynamic quantization
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+ 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).
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  The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
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  | **Accuracy (eval-f1)** |0.8997|0.9042|
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  | **Model size (MB)** |174|418|
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+ ### Load with optimum:
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  ```python
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+ from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification
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+ int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(
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  'Intel/bert-base-uncased-mrpc-int8-dynamic',
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  )
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  ```