Instructions to use xtie/BART-PET-impression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xtie/BART-PET-impression with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="xtie/BART-PET-impression")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xtie/BART-PET-impression") model = AutoModelForSeq2SeqLM.from_pretrained("xtie/BART-PET-impression", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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Conclusion: Personalized impressions generated by PEGASUS were clinically useful, highlighting its potential to expedite PET reporting.
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[Read the full paper](
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## ๐ Usage
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## ๐ Additional Resources
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- **Finetuned from model:** [Facebook's BART Large
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- **Codebase for training and inference:** [GitHub Repository](https://github.com/xtie97/PET-Report-Summarization)
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Conclusion: Personalized impressions generated by PEGASUS were clinically useful, highlighting its potential to expedite PET reporting.
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[Read the full paper]()
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<!-- Link to our Arxiv paper -->
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## ๐ Usage
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## ๐ Additional Resources
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- **Finetuned from model:** [Facebook's BART Large](https://huggingface.co/facebook/bart-large)
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- **Codebase for training and inference:** [GitHub Repository](https://github.com/xtie97/PET-Report-Summarization)
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