| license: cc-by-4.0 | |
| tags: | |
| - int8 | |
| - Intel® Neural Compressor | |
| - PostTrainingStatic | |
| datasets: | |
| - squad2 | |
| metrics: | |
| - f1 | |
| # INT8 RoBERT base finetuned on Squad2 | |
| ### Post-training static quantization | |
| 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). | |
| The original fp32 model comes from the fine-tuned model [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2). | |
| 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.7.output.dense**, **roberta.encoder.layer.8.output.dense**, **roberta.encoder.layer.9.output.dense**, fall back to fp32 for less than 1% relative accuracy loss. | |
| ### Evaluation result | |
| | |INT8|FP32| | |
| |---|:---:|:---:| | |
| | **Accuracy (eval-f1)** |82.3122|82.9231| | |
| | **Model size (MB)** |141|474| | |
| ### Load with optimum: | |
| ```python | |
| from optimum.intel import INCModelForQuestionAnswering | |
| model_id = "Intel/roberta-base-squad2-int8-static" | |
| int8_model = INCModelForQuestionAnswering.from_pretrained(model_id) | |
| ``` | |