To use a cross-encoder from the Sentence Transformers library for re-ranking search results, you first retrieve an initial set of candidate documents using a fast retrieval method (like BM25 or a bi-encoder model), then apply the cross-encoder to compute relevance scores between the query and each candidate, and finally reorder the results based on those scores. Cross-encoders differ from bi-encoders by jointly processing the query and document text, enabling deeper semantic understanding but at a higher computational cost. This makes them ideal for re-ranking a smaller subset of top candidates (e.g., 100-200 items) after a faster initial retrieval step.
Start by installing the library with pip install sentence-transformers. Load a pre-trained cross-encoder model, such as cross-encoder/ms-marco-MiniLM-L-6-v2, which is optimized for search relevance tasks. Prepare your data by pairing the query with each retrieved document. For example, if your search query is “climate change effects” and you have 100 initial results, create a list of tuples like [(query, doc1), (query, doc2), ..., (query, doc100)]. Pass this list to the model’s predict() method to generate similarity scores. These scores represent how well each document matches the query. Finally, sort the documents in descending order of their scores to produce the re-ranked list.
When implementing this, consider performance trade-offs. Cross-encoders are slower than bi-encoders because they process each query-document pair individually. For a query with 100 documents, a cross-encoder might take 1-2 seconds on a CPU, whereas a bi-encoder could handle thousands in the same time. Use cross-encoders only on the top candidates from the initial retrieval to balance speed and accuracy. Also, ensure text lengths are within the model’s token limit (e.g., 512 tokens) by truncating or splitting longer documents. Choose a model trained on a dataset relevant to your domain (e.g., MS MARCO for general web search) for optimal results.