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README.md
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Unlike traditional dense retrievers trained with binary relevance signals, BioHiCL models semantic similarity using structured multi-label supervision derived from the MeSH ontology, enabling it to capture partial semantic overlap between documents.
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## 💡 Key Features
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Unlike traditional dense retrievers trained with binary relevance signals, BioHiCL models semantic similarity using structured multi-label supervision derived from the MeSH ontology, enabling it to capture partial semantic overlap between documents.
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# > ⚠️ Important: Please ensure that the `transformers` version matches exactly (4.57.3), as other versions may lead to compatibility issues or unexpected behavior.
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## 💡 Key Features
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