metadata
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- PostTrainingDynamic
datasets:
- mrpc
metrics:
- f1
INT8 BERT base uncased finetuned MRPC
Post-training dynamic quantization
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.
Test result
- Batch size = 8
- Amazon Web Services c6i.xlarge (Intel ICE Lake: 4 vCPUs, 8g Memory) instance.
| INT8 | FP32 | |
|---|---|---|
| Throughput (samples/sec) | 24.707 | 11.202 |
| Accuracy (eval-f1) | 0.8997 | 0.9042 |
| Model size (MB) | 174 | 418 |
Load with Intel® Neural Compressor (build from source):
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/bert-base-uncased-mrpc-int8-dynamic',
)
Notes:
- The INT8 model has better performance than the FP32 model when the CPU is fully occupied. Otherwise, there will be the illusion that INT8 is inferior to FP32.