---
language:
- en
license: mit
tags:
- text-classfication
- int8
- 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

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 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.

Embedding module **roberta.embeddings.token_type_embeddings** is fallbacked to fp32 due to *Unexpected exception RuntimeError('Expect weight, indices, and offsets to be contiguous.')*

### 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)**  |25.737|13.171|
| **Accuracy (eval-f1)** |0.9247|0.9138|
| **Model size (MB)**  |121|476|

### Load with Intel® Neural Compressor (build from source):

```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
    'Intel/roberta-base-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.