Update README.md

README.md

@@ -1,3 +1,60 @@
 ---
+language:
+- en
 license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+model_index:
+- name: sst2
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE SST2
+      type: glue
+      args: sst2
+    metric:
+      name: Accuracy
+      type: accuracy
+      value: 0.9254587155963303
 ---
+# INT8 albert-base-v2-sst2
+
+###  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 [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).
+
+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 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
+
+- 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)**  |13.464|11.854|
+| **Accuracy (eval-accuracy)** |0.9255|0.9232|
+| **Model size (MB)**  |25|44.6|
+
+### Load with Intel® Neural Compressor (build from source):
+
+```python
+from neural_compressor.utils.load_huggingface import OptimizedModel
+int8_model = OptimizedModel.from_pretrained(
+    'Intel/albert-base-v2-sst2-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.
+