What is a Cross-Encoder?
A type of encoder that jointly processes query-document pairs to determine relevance.
More about Cross-Encoder:
Cross-Encoders take a query and a document as input and process them together through a single model to calculate relevance. Unlike bi-encoders, which generate embeddings independently, cross-encoders allow for finer-grained relevance scoring by considering the interaction between query and document.
Cross-encoders are often used in tasks requiring high accuracy, such as re-ranking candidates retrieved by dense retrieval or hybrid search.
Frequently Asked Questions
What are the advantages of cross-encoders?
They provide higher relevance accuracy by analyzing query-document interactions directly.
When should cross-encoders be used over bi-encoders?
Cross-encoders are better for re-ranking results, while bi-encoders are more efficient for large-scale retrieval.
From the blog
Fixing your Image Alt tags and SEO issues with AI
Optimizing your website's SEO can be complex and time-consuming, especially when it comes to image alt tags, title tags, and structured data. Sitetag, an AI-powered SEO tool, makes this process effortless. With just one script tag, Sitetag automatically enhances your website’s SEO elements, ensuring better search visibility and improved user experience—all without the manual work. Ready to simplify your SEO? Discover how Sitetag can transform your site today.
Herman Schutte
Founder
How AI Chatbots Can Save You 100s Of Hours In Customer Support
Dive into the transformative power of AI chatbots in customer support. Learn how businesses can save significant time and enhance customer satisfaction, with a look at tools like SiteSpeakAI.
Herman Schutte
Founder