Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags: text-classfication
+datasets: 
+- sst2
+---
+
+INT8 DistilBERT base uncased finetuned SST-2 (Post-training static quantization)
+===
+This is an INT8  PyTorch model quantized by [intel/nlp-toolkit](https://github.com/intel/nlp-toolkit) using provider: [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)
+
+Test result below comes from [AWS](https://aws.amazon.com/) c6i.xlarge (intel ice lake: 4 vCPUs, 8g Memory) instance.
+
+|   |fp32|int8|
+|---|:---:|:---:|
+| **Accuracy** |0.9106|0.9037|
+| **Throughput (samples/sec)**  |？|？|
+| **Model size (MB)**  |255|66|
+
+
+Load with optimum:
+```python
+from nlp_toolkit import OptimizedModel
+int8_model = OptimizedModel.from_pretrained(
+    'intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static',
+)
+```
+Notes:  
+ - The INT8 model has better performance than the FP32 model when the CPU is fully loaded. Otherwise, there will be the illusion that INT8 is inferior to FP32.