https://store-images.s-microsoft.com/image/apps.10812.5ebc7d81-a63b-4bec-abdf-b717ae401c1a.56cb466c-e72e-418a-a9cb-1078ea2f57b3.27aa74f6-0b56-4c0c-974c-179800f9cdbc

Jina Reranker v1 Base - en

durch Jina AI

A state-of-the-art neural text reranking model supporting 8192 sequence length.

  • 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.