---
language:
- en
license: apache-2.0
tags:
- text-classfication
- int8
- PostTrainingDynamic
datasets:
- glue
metrics:
- f1
model-index:
- name: bart-large-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: 0.9050847457627118
---
# INT8 bart-large-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 [bart-large-mrpc](https://huggingface.co/Intel/bart-large-mrpc).

### 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)**  |6.529|3.261|
| **Accuracy (eval-f1)** |0.9051|0.9120|
| **Model size (MB)**  |547|1556.48|

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

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