Edit model card

INT8 BERT base uncased finetuned MRPC

Post-training dynamic 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/bert-base-uncased-mrpc.

Test result

INT8 FP32
Accuracy (eval-f1) 0.8997 0.9042
Model size (MB) 174 418

Load with optimum:

from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification
int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(
    'Intel/bert-base-uncased-mrpc-int8-dynamic',
)

ONNX

This is an INT8 ONNX model quantized with Intel® Neural Compressor.

The original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.

Test result

INT8 FP32
Accuracy (eval-f1) 0.8958 0.9042
Model size (MB) 107 418

Load ONNX model:

from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/bert-base-uncased-mrpc-int8-dynamic')
Downloads last month
24
Hosted inference API
Text Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.