README.md

metadata

language:
  - en
license: mit
tags:
  - text-classfication
  - int8
  - neural-compressor
  - Intel® Neural Compressor
  - PostTrainingStatic
  - onnx
datasets:
  - glue
metrics:
  - f1
model-index:
  - name: xlnet-base-cased-mrpc-int8-static
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - name: F1
            type: f1
            value: 0.8892794376098417

INT8 xlnet-base-cased-mrpc

Post-training static quantization

PyTorch

This is an INT8 PyTorch model quantized with Intel® Neural Compressor.

The original fp32 model comes from the fine-tuned model xlnet-base-cased-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.

Test result

	INT8	FP32
Accuracy (eval-f1)	0.8893	0.8897
Model size (MB)	215	448

Load with Intel® Neural Compressor:

from optimum.intel import INCModelForSequenceClassification

model_id = "Intel/xlnet-base-cased-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 xlnet-base-cased-mrpc.

The calibration dataloader is the eval dataloader. The calibration sampling size is 100.

Test result

	INT8	FP32
Accuracy (eval-f1)	0.8974	0.8986
Model size (MB)	226	448

Load ONNX model:

from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/xlnet-base-cased-mrpc-int8-static')