README.md

---
language: en
tags:
- summarization
- medical
library_name: transformers
pipeline_tag: summarization
---

# Automatic Personalized Impression Generation for PET Reports Using Large Language Models 📄✍

**Authored by**: Xin Tie, Muheon Shin, Ali Pirasteh, Nevein Ibrahim, Zachary Huemann, Sharon M. Castellino, Kara Kelly, John Garrett, Junjie Hu, Steve Y. Cho, Tyler J. Bradshaw

[Read the full paper](https://arxiv.org/abs/2309.10066)
<!-- Link to our Arxiv paper -->

## 📑 Model Description

This is the domain-adapted BARTScore for evaluating the quality of PET impressions. 

To check our domain-adapted text-generation-based evaluation metrics: 
- [BARTScore+PET](https://huggingface.co/xtie/BARTScore-PET)
- [PEGASUSScore+PET](https://huggingface.co/xtie/PEGASUSScore-PET)
- [T5+PET](https://huggingface.co/xtie/T5Score-PET)



## 🚀 Usage

Clone this GitHub repository in a local folder
```bash
git clone https://github.com/xtie97/PET-Report-Summarization.git
```

Go the the folder containing codes for computing BARTScore and create a new folder called "checkpoints"
```bash
cd ./PET-Report-Summarization/evaluation_metrics/metrics/BARTScore
mkdir checkpoints
mkdir checkpoints/bart-large
```

Download the model weights and put them in the folder "checkpoints/bart-large". Run the code for computing text-generation-based metrics 
```
python compute_metrics_text_generation.py
```

## 📁 Additional Resources
- **Codebase for evaluation metrics:** [GitHub](https://github.com/xtie97/PET-Report-Summarization/tree/main/evaluation_metrics)
---