---
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.