Update README.md

README.md

@@ -1,3 +1,59 @@
 ---
+language:
+- en
 license: apache-2.0
+tags:
+- text-classfication
+- int8
+- PostTrainingStatic
+datasets:
+- conll2003
+metrics:
+- accuracy
+model-index:
+- name: distilbert-base-uncased-finetuned-conll03-english-int8-static
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: Conll2003
+      type: conll2003
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9858650364082395
 ---
+# INT8 distilbert-base-uncased-finetuned-conll03-english
+
+###  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 [elastic/distilbert-base-uncased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-uncased-finetuned-conll03-english).
+
+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)**  |148.144|77.108|
+| **Accuracy (eval-f1)** |0.9859|0.9882|
+| **Model size (MB)**  |64.5|253|
+
+### Load with Intel® Neural Compressor (build from source):
+
+```python
+from neural_compressor.utils.load_huggingface import OptimizedModel
+int8_model = OptimizedModel.from_pretrained(
+    'Intel/distilbert-base-uncased-finetuned-conll03-english-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.
+