--- language: - en license: mit tags: - text-classfication - int8 - Intel® Neural Compressor - PostTrainingStatic - onnx datasets: - glue metrics: - f1 model-index: - name: electra-small-discriminator-mrpc-int8-static results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: F1 type: f1 value: 0.900709219858156 --- # INT8 electra-small-discriminator-mrpc ## Post-training static quantization ### PyTorch 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 [electra-small-discriminator-mrpc](https://huggingface.co/Intel/electra-small-discriminator-mrpc). The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304. #### Test result | |INT8|FP32| |---|:---:|:---:| | **Accuracy (eval-f1)** |0.9007|0.8983| | **Model size (MB)** |14|51.8| #### Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification int8_model = IncQuantizedModelForSequenceClassification.from_pretrained( 'Intel/electra-small-discriminator-mrpc-int8-static', ) ``` ### ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [electra-small-discriminator-mrpc](https://huggingface.co/Intel/electra-small-discriminator-mrpc). The calibration dataloader is the eval dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8. So the real sampling size is 104. #### Test result | |INT8|FP32| |---|:---:|:---:| | **Accuracy (eval-f1)** |0.8993|0.8983| | **Model size (MB)** |32|52| #### Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/electra-small-discriminator-mrpc-int8-static') ```