README.md

metadata

language:
  - en
license: mit
tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - PostTrainingStatic
datasets:
  - glue
metrics:
  - f1
model-index:
  - name: roberta-base-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: 0.924693520140105

INT8 roberta-base-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 roberta-base-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 embedding module roberta.embeddings.token_type_embeddings falls back to fp32 due to RuntimeError('Expect weight, indices, and offsets to be contiguous.')

Test result

	INT8	FP32
Accuracy (eval-f1)	0.9247	0.9138
Model size (MB)	121	476

Load with Intel® Neural Compressor:

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