INT8 BERT base uncased finetuned MRPC
Post-training static quantization
PyTorch
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 Intel/bert-base-uncased-mrpc.
The calibration dataloader is the train dataloader. The calibration sampling size is 1000.
The linear module bert.encoder.layer.9.output.dense falls back to fp32 to meet the 1% relative accuracy loss.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.8959 | 0.9042 |
| Model size (MB) | 119 | 418 |
Load with Intel® Neural Compressor:
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/bert-base-uncased-mrpc-int8-static"
int8_model = INCModelForSequenceClassification.from_pretrained(model_id)
ONNX
This is an INT8 ONNX model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.
The calibration dataloader is the eval dataloader. The calibration sampling size is 100.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.9021 | 0.9042 |
| Model size (MB) | 236 | 418 |
Load ONNX model:
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/bert-base-uncased-mrpc-int8-static')