jina-reranker-m0
by jinaai
Multimodal reranker handling text, images, and mixed documents across 29 languages
jinaai/jina-reranker-m0Overview
Jina Reranker M0 is the first production-grade multimodal reranker from Jina AI, handling text-to-text, text-to-image, image-to-text, and text-to-mixed-document reranking in a single model. Built on Qwen2-VL-2B-Instruct, it supports 29+ languages and up to 4K image resolution with dynamic patching.
On Mixpeek, Jina Reranker M0 serves as a universal second-stage reranker for any retrieval pipeline — whether the candidates are text documents, scanned pages, product images, or mixed content. Its 91.02 nDCG@5 on ViDoRe v1 makes it state-of-the-art for visual document reranking.
Architecture
Cross-encoder based on Qwen2-VL-2B-Instruct. 2.4B parameters. Dynamic image patching up to 4K resolution. Outputs relevance scores for text, image, and mixed-modality inputs. 29+ language support.
Key Capabilities
- •SOTA visual document reranking (91.02 nDCG@5 on ViDoRe v1)
- •Text, image, and mixed-modality reranking in one model
- •29+ language support including CJK, Arabic, and European languages
- •Dynamic 4K image resolution for high-detail document pages
- •Code retrieval support (63.55 nDCG@10 on CoIR)
Use Cases on Mixpeek
- •Visual document search: rerank scanned PDF pages by layout+content relevance
- •Multilingual retrieval: rerank across 29 languages with one model
- •Product search: rerank by visual and textual product attributes
- •Code retrieval: rerank code snippets and documentation together
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Use jina-reranker-m0 on Mixpeek
Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.
Open StudioHow It Runs on Mixpeek
On Mixpeek, jina-reranker-m0 runs as a managed extractor inside a processing pipeline. Point a bucket of text classification data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.
Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.
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Specification
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