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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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- PostTrainingStatic |
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datasets: |
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- sst2 |
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metrics: |
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- accuracy |
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--- |
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# INT8 DistilBERT base uncased finetuned SST-2 |
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### Post-training static quantization |
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This is an INT8 PyTorch model quantified with [intel/nlp-toolkit](https://github.com/intel/nlp-toolkit) using provider: [Intel® Neural Compressor](https://github.com/intel/neural-compressor). |
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The original fp32 model comes from the fine-tuned model [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) |
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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 |
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the real sampling size is 104. |
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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)** |47.554|23.046| |
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| **Accuracy (eval-accuracy)** |0.9037|0.9106| |
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| **Model size (MB)** |66|255| |
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### Load with nlp-toolkit: |
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```python |
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from nlp_toolkit import OptimizedModel |
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int8_model = OptimizedModel.from_pretrained( |
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'Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static', |
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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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