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---
language:
- en
license: mit
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- neural-compressor
- PostTrainingStatic
datasets:
- glue
metrics:
- f1
model-index:
- name: roberta-base-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: 0.924693520140105
---
# INT8 roberta-base-mrpc
## Post-training static 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 [roberta-base-mrpc](https://huggingface.co/Intel/roberta-base-mrpc).
The calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the real sampling size is 104.
#### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9177|0.9138|
| **Model size (MB)** |127|499|
#### Load with Intel® Neural Compressor:
```python
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/roberta-base-mrpc-int8-static"
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 [roberta-base-mrpc](https://huggingface.co/Intel/roberta-base-mrpc).
The calibration dataloader is the eval dataloader. The calibration sampling size is 100.
#### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9100|0.9138|
| **Model size (MB)** |294|476|
#### Load ONNX model:
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/roberta-base-mrpc-int8-static')
```
|