Update README.md

README.md

@@ -1,3 +1,47 @@
 ---
+language: en
 license: apache-2.0
+tags:
+- text-classfication
+- int8
+- PostTrainingStatic
+datasets: 
+- mrpc
+metrics:
+- f1
 ---
+
+# INT8 BERT base uncased finetuned MRPC
+
+### Post-training static quantization
+
+This is an INT8  PyTorch model quantized with [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).
+
+The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.
+
+The linear module **bert.encoder.layer.9.output.dense, bert.encoder.layer.10.output.dense** falls back to fp32 to meet the 1% relative accuracy loss.
+
+### Test result
+
+- Batch size = 8
+- [Amazon Web Services](https://aws.amazon.com/) c6i.xlarge (Intel ICE Lake: 4 vCPUs, 8g Memory) instance.
+
+|   |INT8|FP32|
+|---|:---:|:---:|
+| **Throughput (samples/sec)**  |23.986|11.202|
+| **Accuracy (eval-f1)** |0.8997|0.9042|
+| **Model size (MB)**  |120|418|
+
+### Load with Intel® Neural Compressor (build from source):
+
+```python
+from neural_compressor.utils.load_huggingface import OptimizedModel
+int8_model = OptimizedModel.from_pretrained(
+    'Intel/bert-base-uncased-mrpc-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.