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This is the fine-tuned BART model for summarizing findings in PET reports.
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## 📑 Abstract
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Purpose: To determine if fine-tuned large language models (LLMs) can generate accurate, personalized impressions for whole-body PET reports.
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This is the fine-tuned BART model for summarizing findings in PET reports.
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To check our fine-tuned large language models (LLMs) for PET report summarization:
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- [BERT2BERT-PET](https://huggingface.co/xtie/Clinicallongformer2roberta-PET-impression)
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- [BART-PET](https://huggingface.co/xtie/BART-PET-impression)
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- [BioBART-PET](https://huggingface.co/xtie/BioBART-PET-impression)
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- [PEGASUS-PET](https://huggingface.co/xtie/PEGASUS-PET-impression)
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- [T5v1.1-PET](https://huggingface.co/xtie/T5v1.1-PET-impression)
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- [Clinical-T5-PET](https://huggingface.co/xtie/ClinicalT5-PET-impression)
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- [Flan-T5-PET](https://huggingface.co/xtie/Flan-T5-PET-impression)
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- [GPT2-XL-PET](https://huggingface.co/xtie/GPT2-PET-impression)
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- [OPT-1.3B-PET](https://huggingface.co/xtie/OPT-PET-impression)
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- [LLaMA-LoRA-PET](https://huggingface.co/xtie/LLaMA-LoRA-PET-impression)
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- [Alpaca-LoRA-PET](https://huggingface.co/xtie/Alpaca-LoRA-PET-impression)
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## 📑 Abstract
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Purpose: To determine if fine-tuned large language models (LLMs) can generate accurate, personalized impressions for whole-body PET reports.
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