XLNet
Collection
Original paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
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INT8 xlnet-base-cased-mrpc
Post-training static quantization
PyTorch
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model xlnet-base-cased-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) | 0.8893 | 0.8897 |
| Model size (MB) | 215 | 448 |
Load with Intel® Neural Compressor:
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/xlnet-base-cased-mrpc-int8-static"
int8_model = INCModelForSequenceClassification.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 xlnet-base-cased-mrpc.
The calibration dataloader is the eval dataloader. The calibration sampling size is 100.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.8974 | 0.8986 |
| Model size (MB) | 226 | 448 |
Load ONNX model:
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/xlnet-base-cased-mrpc-int8-static')
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