metadata
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 through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model 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:
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering
int8_model = IncQuantizedModelForQuestionAnswering.from_pretrained(
'Intel/roberta-base-squad2-int8-static',
)