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Text embedding models have been popular for information retrieval applications such as semantic search and Question-Answering systems based on Retrieval-Augmented Generation (RAG).
The probabilistic relevance framework: BM25 and beyond
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Linq-Embed-Mistral:Elevating Text Retrieval with Improved GPT Data Through Task-Specific Control and Quality Refinement
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NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
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SFR-Embedding-Mistral:Enhance Text Retrieval with Transfer Learning
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