Jina Reranker v1 Base model is a neural text reranking model, designed to enhance the relevance of search results. It complements text embedding models and refines search results by prioritizing documents relevant to a query.
This state-of-the-art reranker model enables a variety of applications that rely on precise search results, improved information retrieval, and better data organization.
Use-cases: Vector search, retrieval augmented generation.
See our embedding models (Jina Embeddings v2) on Azure for state-of-the-art 8k embedding models for vector search.

Hightlights:

Extended context length: This reranker model is capable of handling queries up to 512 tokens and documents as large as 8192 tokens.
High performance across the board: This reranking model ranks at the top compared to its competitors, in terms of 'Mean Reciprocal Rank' (MRR), according to BIER, MTEB, LoCo and an independent benchmark by LlamaIndex. A higher MRR represents a higher chance that the most relevant document to a query is returned with the highest relevance score by a reranking model.

Jina Reranker v1 Base - enJina AI

Jina Reranker v1 Base - en