Jina Reranker v1 Tiny model is a neural text reranking model, designed to enhance the relevance of search results.
This model is the fastest reranker model in the Jina Reranker suite of models, offering fast and memory-efficient reranking process.
For our most accurate (and larger) reranker models, please see Jina Reranker v1 Base - en or Jina Reranker v1 Turbo - en.
Jina Reranker v1 Tiny 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:

Trained for speed and efficiency: While performing slighly lower than Jina Reranker v1 Base - en on the benchmarks, this model can process (rerank) five times as many documents in the same time.
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 competitively 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 Tiny - enJina AI

Jina Reranker v1 Tiny - en