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INT8 bart-large-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 bart-large-mrpc.

Test result

INT8 FP32
Accuracy (eval-f1) 0.9051 0.9120
Model size (MB) 547 1556.48

Load with optimum:

from optimum.intel import INCModelForSequenceClassification

model_id = "Intel/bart-large-mrpc-int8-dynamic"
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 bart-large-mrpc.

Test result

INT8 FP32
Accuracy (eval-f1) 0.9236 0.9120
Model size (MB) 764 1555

Load ONNX model:

from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/bart-large-mrpc-int8-dynamic')
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Dataset used to train Intel/bart-large-mrpc-int8-dynamic-inc

Collection including Intel/bart-large-mrpc-int8-dynamic-inc

Evaluation results