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README.md
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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
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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={
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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}
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