Update README.md

README.md

@@ -29,8 +29,7 @@ model-index:
 
 ###  Post-training static quantization
 
-This is an INT8  PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
-
+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 [thyagosme/bert-base-uncased-finetuned-swag](https://huggingface.co/thyagosme/bert-base-uncased-finetuned-swag).
 
 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.
@@ -44,11 +43,11 @@ The linear modules **bert.encoder.layer.2.output.dense, bert.encoder.layer.5.int
 | **Accuracy (eval-accuracy)** |0.7838|0.7915|
 | **Model size (MB)**  |133|418|
 
-### Load with Intel® Neural Compressor:
+### Load with optimum:
 
 ```python
-from neural_compressor.utils.load_huggingface import OptimizedModel
-int8_model = OptimizedModel.from_pretrained(
+from optimum.intel.neural_compressor.quantization import IncQuantizedModelForMultipleChoice
+int8_model = IncQuantizedModelForMultipleChoice.from_pretrained(
     'Intel/bert-base-uncased-finetuned-swag-int8-static',
 )
 ```