README.md

---
library_name: transformers
datasets:
- leduckhai/VietMed-Sum
language:
- vi
pipeline_tag: summarization
---

# Real-time Speech Summarization for Medical Conversations

<p align="center">
<img src="RTSS_diagram.png" alt="drawing" width="900"/>
</p>

Please cite this paper: https://arxiv.org/abs/2406.15888

    @article{VietMed_Sum,
    title={Real-time Speech Summarization for Medical Conversations},
    author={Le-Duc, Khai and Nguyen, Khai-Nguyen and Vo-Dang, Long and Hy, Truong-Son},
    journal={arXiv preprint arXiv:2406.15888},
    booktitle={Interspeech 2024},
    url = {https://arxiv.org/abs/2406.15888}
    year={2024}
    }
    
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model summarizes medical dialogues in Vietnamese. It can work in tandem with an ASR system to provide real-time dialogue summary.

- **Developed by:** Khai-Nguyen Nguyen
- **Language(s) (NLP):** Vietnamese
- **Finetuned from model [optional]:** ViT5


## How to Get Started with the Model

Install the pre-requisite packages in Python. 
```python
pip install transformers
```


Use the code below to get started with the model.


```python
from transformers import pipeline

# Initialize the pipeline with the ViT5 model, specify the device to use CUDA for GPU acceleration
pipe = pipeline("text2text-generation", model="monishsystem/medisum_vit5", device='cuda')

# Example text in Vietnamese describing a traditional medicine product
example = "Loại thuốc này chứa các thành phần đông y đặc biệt tốt cho sức khoẻ, giúp tăng cường sinh lý và bổ thận tráng dương, đặc biệt tốt cho người cao tuổi và người có bệnh lý nền"

# Generate a summary for the input text with a maximum length of 50 tokens
summary = pipe(example, max_new_tokens=50)

# Print the generated summary
print(summary)
```