metadata

language: en
license: apache-2.0
tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - PostTrainingStatic
datasets:
  - mrpc
metrics:
  - f1

INT8 BERT base uncased finetuned MRPC

Post-training static quantization

This is an INT8 PyTorch 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 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 bert.encoder.layer.9.output.dense, bert.encoder.layer.10.output.dense falls back to fp32 to meet the 1% relative accuracy loss.

Test result

	INT8	FP32
Accuracy (eval-f1)	0.8997	0.9042
Model size (MB)	120	418

Load with Intel® Neural Compressor:

from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
    'Intel/bert-base-uncased-mrpc-int8-static',
)