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---
language:
- en
license: mit
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- neural-compressor
- PostTrainingDynamic
- onnx
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
### PyTorch
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
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.8843|0.8928|
| **Model size (MB)** |180|422|
#### Load with Intel® Neural Compressor:
```python
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/camembert-base-mrpc-int8-dynamic"
int8_model = INCModelForSequenceClassification.from_pretrained(model_id)
```
### ONNX
This is an INT8 ONNX 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).
#### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.8819|0.8928|
| **Model size (MB)** |113|423|
#### Load ONNX model:
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/camembert-base-mrpc-int8-dynamic')
```
|