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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- text-classfication |
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- int8 |
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- PostTrainingDynamic |
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datasets: |
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- glue |
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metrics: |
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- f1 |
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model-index: |
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- name: camembert-base-mrpc-int8-dynamic |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE MRPC |
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type: glue |
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args: mrpc |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.8842832469775476 |
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--- |
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# INT8 camembert-base-mrpc |
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### Post-training dynamic 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 [camembert-base-mrpc](https://huggingface.co/Intel/camembert-base-mrpc). |
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The linear module **roberta.encoder.layer.6.attention.self.query** falls back to fp32 to meet the 1% relative accuracy loss. |
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### Test result |
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- Batch size = 8 |
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- [Amazon Web Services](https://aws.amazon.com/) c6i.xlarge (Intel ICE Lake: 4 vCPUs, 8g Memory) instance. |
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| |INT8|FP32| |
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|---|:---:|:---:| |
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| **Throughput (samples/sec)** |24.745|13.078| |
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| **Accuracy (eval-f1)** |0.8843|0.8928| |
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| **Model size (MB)** |180|422| |
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### Load with Intel® Neural Compressor (build from source): |
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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/camembert-base-mrpc-int8-dynamic', |
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) |
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``` |
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Notes: |
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- The INT8 model has better performance than the FP32 model when the CPU is fully occupied. Otherwise, there will be the illusion that INT8 is inferior to FP32. |
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