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@@ -185,7 +185,7 @@ Summary:"""
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  ## 📊 **Training Data**
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- BSG CyLlama was trained on [19,174 scientific abstracts](https://huggingface.co/datasets/jimnoneill/BSG_CyLlama-training) organized for cyclical corpus summarization:
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  - **Corpus Groups**: Documents clustered by research themes
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  - **Cyclical Training**: Model learned to process document series
@@ -229,7 +229,7 @@ python bsg_cyllama_demo.py
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  ```bibtex
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  @misc{bsg-cyllama-2025,
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  title={BSG CyLlama: Biomedical Summary Generation through Cyclical Llama},
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- author={BSG Research Team},
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  year={2025},
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  url={https://huggingface.co/jimnoneill/BSG_CyLlama},
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  note={Novel cyclical embedding averaging methodology for corpus-level summarization}
 
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  ## 📊 **Training Data**
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+ BSG CyLlama was trained on [19,174 clusters of scientific abstracts](https://huggingface.co/datasets/jimnoneill/BSG_CyLlama-training) organized for cyclical corpus summarization:
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  - **Corpus Groups**: Documents clustered by research themes
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  - **Cyclical Training**: Model learned to process document series
 
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  ```bibtex
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  @misc{bsg-cyllama-2025,
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  title={BSG CyLlama: Biomedical Summary Generation through Cyclical Llama},
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+ author={Jamey ONeill},
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  year={2025},
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  url={https://huggingface.co/jimnoneill/BSG_CyLlama},
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  note={Novel cyclical embedding averaging methodology for corpus-level summarization}