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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Real-time Speech Summarization for Medical Conversations
+
+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)
+```