README.md

metadata

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

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.neural_compressor import IncQuantizedModelForSequenceClassification
int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(
    'Intel/bert-base-uncased-mrpc-int8-static',
)

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')