Update README.md

README.md

@@ -17,7 +17,7 @@ metrics:
 
 This is an INT8  PyTorch model quantified with [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 [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)
+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.
@@ -33,13 +33,14 @@ The calibration dataloader is the train dataloader. The default calibration samp
 | **Accuracy (eval-accuracy)** |0.9037|0.9106|
 | **Model size (MB)**  |65|255|
 
-
 ### 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',
 )
 ```
+
 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.