mxbai-rerank-large-v2

Model Description

mxbai-rerank-large-v2 is the flagship model of Mixedbread's second-generation rerank family, a set of state-of-the-art reranking models that are fully open-source under the Apache 2.0 license. This powerful 1.5B-parameter model delivers best-in-class accuracy and robust multilingual capabilities while maintaining impressive processing speed.

The v2 models represent a significant advancement over the first generation, leveraging a sophisticated three-step reinforcement learning process:

1. GRPO (Guided Reinforcement Prompt Optimization) - Teaching the model to output clear relevance scores
2. Contrastive Learning - Developing fine-grained understanding of query-document relationships
3. Preference Learning - Tuning the model to prioritize the most relevant documents

On benchmarks, mxbai-rerank-large-v2 achieves exceptional results with an NDCG@10 score of 57.49 on BEIR average, 29.79 on Mr.TyDi multilingual datasets Despite its larger parameter count, it processes queries with impressive efficiency (0.89s per query on NFC dataset with A100 GPU), up to 8x faster than comparable models.

When used in combination with a keyword-based search engine such as Elasticsearch, OpenSearch, or Solr, the rerank model can be added to the end of an existing search workflow. This allows users to incorporate semantic relevance into their keyword-based search system without changing the existing infrastructure - an easy, low-complexity method of improving search results with just one line of code.

Example

Last updated: May 6, 2025