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A Scalable Framework for Benchmarking Embedding Models for Semantic Medical Tasks
2024-08-20
health informatics
Title + abstract only
View on medRxiv
Show abstract
Text embeddings convert textual information into numerical representations, enabling machines to perform semantic tasks like information retrieval. Despite its potential, the application of text embeddings in healthcare is underexplored in part due to a lack of benchmarking studies using biomedical data. This study provides a flexible framework for benchmarking embedding models to identify those most effective for healthcare-related semantic tasks. We selected thirty embedding models from the mu...
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