Free Lesson
Cross-encoders: An End-to-end Guide
Part of The Frontier of AI Search
60 min
Jun 17, 2026 12:00 PM
Virtual (Zoom)
In this video
What you'll learn
What are cross-encoders and their uses?
As a form of ranking classifier, cross encoders are an essential tool for search relevance ranking.
Trade-offs using cross-encoders vs. standard vector search
Compared to regular vector search (with bi-encoders), cross-encoders greatly improve semantic ranking, but at a cost.
An end-to-end walkthrough of using cross-encoders (in Vespa)
Exporting the cross-encoder model (ONNX format), tokenizing doc and query text, & connecting the dots in a rank profile.
Tips and tricks to keep latency and cost in check
Optimization tricks like using multi-stage ranking and running the cross-encoder on a GPU.
Why this topic matters
Cross-encoders make for great re-rankers, because they capture many nuances of the query-to-document relationship. But they are expensive to run on a large number of documents! For best performance, you need to keep this computation close to the data, which is where Vespa is helpful. Radu will introduce cross-encoders and provide an end-to-end walk through of how to implement them for production.
You'll learn from
Previously at
