MiniLM
Collection
Fine-tuned version of Microsoft's MiniLM models, trained on the GLUE MRPC dataset.
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5 items
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INT8 MiniLM-L12-H384 finetuned MRPC
Post-training static quantization
PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor. The original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.
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 module bert.encoder.layer.6.attention.self.key falls back to fp32 to meet the 1% relative accuracy loss.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.9039 | 0.9097 |
| Model size (MB) | 33.5 | 127 |
Load with optimum:
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/MiniLM-L12-H384-uncased-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 Intel/MiniLM-L12-H384-uncased-mrpc.
The calibration dataloader is the eval dataloader. The calibration sampling size is 100.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-f1) | 0.9013 | 0.9097 |
| Model size (MB) | 33 | 128 |
Load ONNX model:
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/MiniLM-L12-H384-uncased-mrpc-int8-static')
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