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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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- 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 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 [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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| |INT8|FP32| |
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|---|:---:|:---:| |
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| **Accuracy (eval-accuracy)** |0.9037|0.9106| |
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| **Model size (MB)** |65|255| |
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### Load with optimum: |
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```python |
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from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification |
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int8_model = IncQuantizedModelForSequenceClassification.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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