Retrieve & Re-Rank — Sentence Transformers documentation

In Semantic Search we have shown how to use SentenceTransformer to compute embeddings for queries, sentences, and paragraphs and how to use this for semantic search. For complex search tasks, for example question answering retrieval, the search can significantly be improved by using Retrieve & Re-Rank.

Given a search query, we first use a retrieval system that retrieves a large list of e.g. 100 possible hits which are potentially relevant for the query. For the retrieval, we can use either lexical search, e.g. with a vector engine like Elasticsearch, or we can use dense retrieval with a SentenceTransformer (a.k.a. bi-encoder). However, the retrieval system might retrieve documents that are not that relevant for the search query. Hence, in a second stage, we use a re-ranker based on a CrossEncoder that scores the relevancy of all candidates for the given search query. The output will be a ranked list of hits we can present to the user.

The retriever has to be efficient for large document collections with millions of entries. However, it might return irrelevant candidates. A re-ranker based on a Cross-Encoder can substantially improve the final results for the user. The query and a possible document is passed simultaneously to transformer network, which then outputs a single score between 0 and 1 indicating how relevant the document is for the given query.