使用 Sentence Transformers 訓練與微調 Reranker 重排模型教學
Original: Training and Finetuning Reranker Models with Sentence Transformers
When building RAG (Retrieval-Augmented Generation) systems, relying solely on vector embeddings for semantic search is often not precise…
Hugging Face 釋出全新教學，詳細介紹如何利用 Sentence Transformers 庫訓練與微調 Reranker（重排）模型。Reranker 在 RAG 系統中扮演關鍵角色，能對初步檢索的文檔進行二次精準排序。本文涵蓋資料準備、損失函數選擇、訓練流程及評估方法，幫助開發者針對特定領域優化檢索效果。
When building RAG (Retrieval-Augmented Generation) systems, relying solely on vector embeddings for semantic search is often not precise enough. To improve retrieval quality, the industry widely adopts a "two-stage retrieval" architecture: the first stage uses a fast Bi-Encoder (vector model) to filter down to the top N candidate documents, and the second stage uses a computationally heavier but far more precise Cross-Encoder (also known as a Reranker) to deeply compare these candidates against the query and re-rank them.
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