Update README.md

README.md

@@ -1,3 +1,59 @@
 ---
+language:
+- en
 license: apache-2.0
+tags:
+- multiple-choice
+- int8
+- PostTrainingStatic
+datasets:
+- swag
+metrics:
+- accuracy
+model-index:
+- name: bert-base-uncased-finetuned-swag-int8-static
+  results:
+  - task:
+      name: Multiple-choice
+      type: multiple-choice
+    dataset:
+      name: Swag
+      type: swag
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7838148474693298
 ---
+# INT8 bert-base-uncased-finetuned-swag
+
+###  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 [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.
+
+### 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)**  |16.55|9.333|
+| **Accuracy (eval-accuracy)** |0.7838|0.7915|
+| **Model size (MB)**  |133|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-finetuned-swag-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.
+