update README.md.

README.md

@@ -1,3 +1,39 @@
 ---
-license: mit
+license: mlt
+tags:
+- int8
+- Intel® Neural Compressor
+- PostTrainingStatic
+datasets: 
+- mnli
+metrics:
+- accuracy
 ---
+
+# INT8 RoBERT large finetuned on MNLI
+
+### 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 [roberta-large-mnli](https://huggingface.co/roberta-large-mnli).
+
+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.
+
+The linear modules **roberta.encoder.layer.16.output.dense**, **roberta.encoder.layer.17.output.dense**, **roberta.encoder.layer.18.output.dense**, fall back to fp32 for less than 1% relative accuracy loss.
+
+### Evaluation result
+
+|   |INT8|FP32|
+|---|:---:|:---:|
+| **Accuracy (eval-acc)** |89.8624|90.5960|
+| **Model size (MB)**  |381M|1.4G|
+
+### Load with Intel® Neural Compressor:
+
+```python
+from neural_compressor.utils.load_huggingface import OptimizedModel
+int8_model = OptimizedModel.from_pretrained(
+    'Intel/roberta-base-squad2-int8-static',
+)
+```