https://store-images.s-microsoft.com/image/apps.10812.8fd2d717-3cf5-4b4e-ab18-fd393c0785c1.698a0d10-df3d-4c43-a52e-ccccad9c6004.29467337-edd5-4d0c-89c7-82ca374cb649
Jina Reranker v1 Tiny - en
Jina AI
Jina Reranker v1 Tiny - en
Jina AI
Jina Reranker v1 Tiny - en
Jina AI
A fast neural text reranking model supporting 8192 sequence length.
- 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.