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--- |
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license: apache-2.0 |
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tags: |
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- int8 |
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- Intel® Neural Compressor |
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- neural-compressor |
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- PostTrainingStatic |
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datasets: |
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- squad |
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metrics: |
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- f1 |
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--- |
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# INT8 BERT base uncased finetuned on Squad |
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### Post-training static quantization |
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This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). |
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The original fp32 model comes from the fine-tuned model [jimypbr/bert-base-uncased-squad](https://huggingface.co/jimypbr/bert-base-uncased-squad). |
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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. |
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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. |
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### Test result |
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| |INT8|FP32| |
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|---|:---:|:---:| |
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| **Accuracy (eval-f1)** |87.3006|88.1030| |
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| **Model size (MB)** |139|436| |
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### Load with Intel® Neural Compressor: |
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```python |
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from optimum.intel import INCModelForQuestionAnswering |
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model_id = "Intel/bert-base-uncased-squad-int8-static" |
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int8_model = INCModelForQuestionAnswering.from_pretrained(model_id) |
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``` |
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