metadata
language:
- en
license: mit
datasets:
- glue
metrics:
- f1
model-index:
- name: electra-small-discriminator-mrpc-int8-static
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: F1
type: f1
value: 0.900709219858156
INT8 electra-small-discriminator-mrpc
Post-training static quantization
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model electra-small-discriminator-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
- Batch size = 8
- Amazon Web Services c6i.xlarge (Intel ICE Lake: 4 vCPUs, 8g Memory) instance.
INT8 | FP32 | |
---|---|---|
Throughput (samples/sec) | 102.15 | 75.056 |
Accuracy (eval-f1) | 0.9007 | 0.8983 |
Model size (MB) | 14 | 51.8 |
Load with Intel® Neural Compressor (build from source):
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/electra-small-discriminator-mrpc-int8-static',
)
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.