File size: 1,316 Bytes
e1c3c09 c7c2381 e1c3c09 c7c2381 d56926a c7c2381 b4d95a9 c7c2381 d56926a c7c2381 bab0264 c7c2381 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
---
license: cc-by-4.0
tags:
- int8
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- squad2
metrics:
- f1
---
# INT8 RoBERT base finetuned on Squad2
### 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 [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2).
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.
The linear modules **roberta.encoder.layer.7.output.dense**, **roberta.encoder.layer.8.output.dense**, **roberta.encoder.layer.9.output.dense**, fall back to fp32 for less than 1% relative accuracy loss.
### Evaluation result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |82.3122|82.9231|
| **Model size (MB)** |141|474|
### Load with optimum:
```python
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering
int8_model = IncQuantizedModelForQuestionAnswering.from_pretrained(
'Intel/roberta-base-squad2-int8-static',
)
```
|