Update README.md

README.md

@@ -15,7 +15,9 @@ metrics:
 
 ### QuantizationAwareTraining
 
-This is an INT8  PyTorch model quantized by [intel/nlp-toolkit](https://github.com/intel/nlp-toolkit) using provider: [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)
+This is an INT8  PyTorch model quantized by [intel/nlp-toolkit](https://github.com/intel/nlp-toolkit) using provider: [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).
 
 #### Training hyperparameters
 
@@ -44,11 +46,13 @@ The following hyperparameters were used during training:
 | **Model size (MB)**  |174|418|
 
 ### Load with nlp-toolkit:
+
 ```python
 from nlp_toolkit import OptimizedModel
 int8_model = OptimizedModel.from_pretrained(
-    'Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static',
+    'Intel/bert-base-uncased-mrpc-int8-qat',
 )
 ```
+
 Notes:  
  - The INT8 model has better performance than the FP32 model when the CPU is fully occupied. Otherwise, there will be the illusion that INT8 is inferior to FP32.