upload int8 onnx model

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>

README.md

@@ -29,7 +29,9 @@ model_index:
 ---
 # INT8 albert-base-v2-sst2
 
-###  Post-training static quantization
+##  Post-training static quantization
+
+### PyTorch
 
 This is an INT8  PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
 
@@ -39,14 +41,14 @@ The calibration dataloader is the train dataloader. The default calibration samp
 
 The linear modules **albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.module, albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.module** fall back to fp32 to meet the 1% relative accuracy loss.
 
-### Test result
+#### Test result
 
 |   |INT8|FP32|
 |---|:---:|:---:|
 | **Accuracy (eval-accuracy)** |0.9255|0.9232|
 | **Model size (MB)**  |25|44.6|
 
-### Load with Intel® Neural Compressor:
+#### Load with Intel® Neural Compressor:
 
 ```python
 from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification
@@ -54,3 +56,24 @@ from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassifi
 model_id = "Intel/albert-base-v2-sst2-int8-static"
 int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id)
 ```
+
+### 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 [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).
+
+#### Test result
+
+|   |INT8|FP32|
+|---|:---:|:---:|
+| **Accuracy (eval-f1)** |0.9186|0.9232|
+| **Model size (MB)**  |89|45|
+
+
+#### Load ONNX model:
+
+```python
+from optimum.onnxruntime import ORTModelForSequenceClassification
+model = ORTModelForSequenceClassification.from_pretrained('Intel/albert-base-v2-sst2-int8-static')
+```

model.onnx

@@ -0,0 +1,3 @@
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+oid sha256:df3f220d199dd52af87203ab564efe7584c6fd514fef93d1d5b3772c47c14145
+size 92596546