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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- text-classfication |
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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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- mrpc |
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metrics: |
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- f1 |
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--- |
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# INT8 BERT base uncased finetuned MRPC |
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## Post-training static quantization |
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### PyTorch |
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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 [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc). |
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The calibration dataloader is the train dataloader. The calibration sampling size is 1000. |
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The linear module **bert.encoder.layer.9.output.dense** falls 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)** |0.8959|0.9042| |
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| **Model size (MB)** |119|418| |
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#### Load with Intel® Neural Compressor: |
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```python |
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from optimum.intel import INCModelForSequenceClassification |
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model_id = "Intel/bert-base-uncased-mrpc-int8-static" |
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int8_model = INCModelForSequenceClassification.from_pretrained(model_id) |
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``` |
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### ONNX |
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This is an INT8 ONNX 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 [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc). |
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The calibration dataloader is the eval dataloader. The calibration sampling size is 100. |
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#### Test result |
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| |INT8|FP32| |
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|---|:---:|:---:| |
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| **Accuracy (eval-f1)** |0.9021|0.9042| |
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| **Model size (MB)** |236|418| |
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#### Load ONNX model: |
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```python |
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from optimum.onnxruntime import ORTModelForSequenceClassification |
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model = ORTModelForSequenceClassification.from_pretrained('Intel/bert-base-uncased-mrpc-int8-static') |
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``` |
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