Update README.md

README.md

@@ -8,23 +8,33 @@ tags:
 datasets: 
 - sst2
 metrics:
-- f1
+- accuracy
 ---
 
-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)
+# INT8 DistilBERT base uncased finetuned SST-2
 
-Test result below comes from [Amazon Web Services](https://aws.amazon.com/) c6i.xlarge (intel ice lake: 4 vCPUs, 8g Memory) instance.
+###  Post-training static quantization
 
-|   |int8|fp32|
+This is an INT8  PyTorch model quantified with [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)
+
+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)**  |47.554|23.046|
-| **Accuracy(f1-score)** |0.9037|0.9106|
+| **Accuracy (eval-accuracy)** |0.9037|0.9106|
 | **Model size (MB)**  |66|255|
 
 
-Load with nlp-toolkit:
+### Load with nlp-toolkit:
 ```python
 from nlp_toolkit import OptimizedModel
 int8_model = OptimizedModel.from_pretrained(