README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - PostTrainingStatic
datasets:
  - glue
metrics:
  - accuracy
model_index:
  - name: sst2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE SST2
          type: glue
          args: sst2
        metric:
          name: Accuracy
          type: accuracy
          value: 0.9254587155963303

INT8 albert-base-v2-sst2

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 Alireza1044/albert-base-v2-sst2.

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 modules albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.module, albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.module fall back to fp32 to meet the 1% relative accuracy loss.

Test result

	INT8	FP32
Accuracy (eval-accuracy)	0.9255	0.9232
Model size (MB)	25	44.6

Load with Intel® Neural Compressor:

from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
    'Intel/albert-base-v2-sst2-int8-static',
)