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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- multiple-choice |
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- int8 |
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- Intel® Neural Compressor |
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- PostTrainingStatic |
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datasets: |
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- swag |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert-base-uncased-finetuned-swag-int8-static |
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results: |
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- task: |
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name: Multiple-choice |
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type: multiple-choice |
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dataset: |
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name: Swag |
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type: swag |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7838148474693298 |
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--- |
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# INT8 bert-base-uncased-finetuned-swag |
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### Post-training static quantization |
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This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). |
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The original fp32 model comes from the fine-tuned model [thyagosme/bert-base-uncased-finetuned-swag](https://huggingface.co/thyagosme/bert-base-uncased-finetuned-swag). |
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The calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the real sampling size is 104. |
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The linear modules **bert.encoder.layer.2.output.dense, bert.encoder.layer.5.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-accuracy)** |0.7838|0.7915| |
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| **Model size (MB)** |133|418| |
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### Load with optimum: |
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```python |
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from optimum.intel import INCModelForMultipleChoice |
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model_id = "Intel/bert-base-uncased-finetuned-swag-int8-static" |
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int8_model = INCModelForMultipleChoice.from_pretrained(model_id) |
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``` |
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