INT8 camembert-base-mrpc
Post-training dynamic quantization
PyTorch
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model camembert-base-mrpc.
The linear module roberta.encoder.layer.6.attention.self.query falls back to fp32 to meet the 1% relative accuracy loss.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.8843 | 0.8928 |
| Model size (MB) | 180 | 422 |
Load with Intel® Neural Compressor:
from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification
model_id = "Intel/camembert-base-mrpc-int8-dynamic"
int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id)
ONNX
This is an INT8 ONNX model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model camembert-base-mrpc.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.8819 | 0.8928 |
| Model size (MB) | 113 | 423 |
Load ONNX model:
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/camembert-base-mrpc-int8-dynamic')
