| license: mit | |
| tags: | |
| - int8 | |
| - Intel® Neural Compressor | |
| - neural-compressor | |
| - PostTrainingStatic | |
| datasets: | |
| - mnli | |
| metrics: | |
| - accuracy | |
| # INT8 RoBERT large finetuned on MNLI | |
| ### Post-training static quantization | |
| This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). | |
| The original fp32 model comes from the fine-tuned model [roberta-large-mnli](https://huggingface.co/roberta-large-mnli). | |
| 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. | |
| 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. | |
| ### Evaluation result | |
| | |INT8|FP32| | |
| |---|:---:|:---:| | |
| | **Accuracy (eval-acc)** |89.8624|90.5960| | |
| | **Model size (MB)** |381M|1.4G| | |
| ### Load with Intel® Neural Compressor: | |
| ```python | |
| from neural_compressor.utils.load_huggingface import OptimizedModel | |
| int8_model = OptimizedModel.from_pretrained( | |
| 'Intel/roberta-base-squad2-int8-static', | |
| ) | |
| ``` | |