license: apache-2.0 | |
tags: | |
- int8 | |
- Intel® Neural Compressor | |
- PostTrainingStatic | |
datasets: | |
- squad | |
metrics: | |
- f1 | |
# INT8 DistilBERT base cased finetuned on Squad | |
### Post-training static quantization | |
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). | |
The original fp32 model comes from the fine-tuned model [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad). | |
The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304. | |
The linear module **distilbert.transformer.layer.1.ffn.lin2** falls back to fp32 to meet the 1% relative accuracy loss. | |
### Test result | |
| |INT8|FP32| | |
|---|:---:|:---:| | |
| **Accuracy (eval-f1)** |86.0005|86.8373| | |
| **Model size (MB)** |71.2|249| | |
### Load with optimum: | |
```python | |
from optimum.intel import INCModelForQuestionAnswering | |
model_id = "Intel/distilbert-base-cased-distilled-squad-int8-static" | |
int8_model = INCModelForQuestionAnswering.from_pretrained(model_id) | |
``` | |