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INT8 BERT base uncased finetuned on Squad

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 jimypbr/bert-base-uncased-squad.

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 bert.encoder.layer.2.intermediate.dense, bert.encoder.layer.4.intermediate.dense, bert.encoder.layer.9.output.dense, bert.encoder.layer.10.output.dense fall back to fp32 to meet the 1% relative accuracy loss.

Test result

Accuracy (eval-f1) 87.3006 88.1030
Model size (MB) 139 436

Load with Intel® Neural Compressor:

from optimum.intel.neural_compressor import IncQuantizedModelForQuestionAnswering
int8_model = IncQuantizedModelForQuestionAnswering.from_pretrained(
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This model can be loaded on the Inference API on-demand.

Dataset used to train Intel/bert-base-uncased-squad-int8-static