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--- |
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language: en |
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tags: |
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- summarization |
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- medical |
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library_name: transformers |
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pipeline_tag: summarization |
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--- |
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# Automatic Personalized Impression Generation for PET Reports Using Large Language Models 📄✍ |
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**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 |
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[Read the full paper](https://arxiv.org/abs/2309.10066) |
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<!-- Link to our Arxiv paper --> |
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## 📑 Model Description |
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This is the domain-adapted BARTScore for evaluating the quality of PET impressions. |
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To check our domain-adapted text-generation-based evaluation metrics: |
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- [BARTScore+PET](https://huggingface.co/xtie/BARTScore-PET) |
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- [PEGASUSScore+PET](https://huggingface.co/xtie/PEGASUSScore-PET) |
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- [T5+PET](https://huggingface.co/xtie/T5Score-PET) |
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## 🚀 Usage |
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Clone this GitHub repository in a local folder |
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```bash |
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git clone https://github.com/xtie97/PET-Report-Summarization.git |
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``` |
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Go the the folder containing codes for computing BARTScore and create a new folder called "checkpoints" |
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```bash |
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cd ./PET-Report-Summarization/evaluation_metrics/metrics/BARTScore |
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mkdir checkpoints |
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mkdir checkpoints/bart-large |
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``` |
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Download the model weights and put them in the folder "checkpoints/bart-large". Run the code for computing text-generation-based metrics |
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``` |
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python compute_metrics_text_generation.py |
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``` |
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## 📁 Additional Resources |
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- **Codebase for evaluation metrics:** [GitHub](https://github.com/xtie97/PET-Report-Summarization/tree/main/evaluation_metrics) |
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--- |
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