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---
language:
- en
license: mit
tags:
- text-classfication
- int8
- PostTrainingDynamic
datasets:
- glue
metrics:
- f1
model-index:
- name: camembert-base-mrpc-int8-dynamic
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: F1
type: f1
value: 0.8842832469775476
---
# INT8 camembert-base-mrpc
### Post-training dynamic quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [camembert-base-mrpc](https://huggingface.co/Intel/camembert-base-mrpc).
The linear module **roberta.encoder.layer.6.attention.self.query** falls back to fp32 to meet the 1% relative accuracy loss.
### Test result
- Batch size = 8
- [Amazon Web Services](https://aws.amazon.com/) c6i.xlarge (Intel ICE Lake: 4 vCPUs, 8g Memory) instance.
| |INT8|FP32|
|---|:---:|:---:|
| **Throughput (samples/sec)** |24.745|13.078|
| **Accuracy (eval-f1)** |0.8843|0.8928|
| **Model size (MB)** |180|422|
### Load with Intel® Neural Compressor (build from source):
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/camembert-base-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.
|