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--- |
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license: mit |
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tags: |
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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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- mnli |
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metrics: |
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- accuracy |
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--- |
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# INT8 RoBERT large finetuned on MNLI |
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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 [roberta-large-mnli](https://huggingface.co/roberta-large-mnli). |
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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 **roberta.encoder.layer.16.output.dense**, **roberta.encoder.layer.17.output.dense**, **roberta.encoder.layer.18.output.dense**, fall back to fp32 for less than 1% relative accuracy loss. |
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### Evaluation result |
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| |INT8|FP32| |
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|---|:---:|:---:| |
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| **Accuracy (eval-acc)** |89.8624|90.5960| |
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| **Model size (MB)** |381M|1.4G| |
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### Load with Intel® Neural Compressor: |
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```python |
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from neural_compressor.utils.load_huggingface import OptimizedModel |
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int8_model = OptimizedModel.from_pretrained( |
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'Intel/roberta-base-squad2-int8-static', |
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) |
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``` |
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