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jina-reranker-m0: Multilingual Multimodal Document Reranker

Intended Usage & Model Info

jina-reranker-m0 is our new multilingual multimodal reranker model for ranking visual documents across multiple languages: it accepts a query alongside a collection of visually rich document images, including pages with text, figures, tables, infographics, and various layouts across multiple domains and over 29 languages. It outputs a ranked list of documents ordered by their relevance to the input query. Compared to jina-reranker-v2-base-multilingual, jina-reranker-m0 also improves text reranking for multilingual content, long documents, and code searching tasks.

Architecture

jina-reranker-m0 is built on a decoder-only vision language model architecture, specifically:

Base model: Qwen2-VL-2B-Instruct, utilizing its vision encoder, projection layer, and language model
Adaptation: Fine-tuned the language model with LoRA (Low-Rank Adaptation) techniques
Output layer: Post-trained MLP head to generate ranking scores measuring query-document relevance
Training objective: Optimized with pairwise and listwise ranking losses to produce discriminative relevance scores

This represents a significant architectural shift from our previous cross-encoder models:

	jina-reranker-m0	jina-reranker-v2
Architecture	Vision Language Model	Cross-Encoder
Base model	Qwen2-VL-2B	Jina-XLM-RoBERTa
Parameters	2.4 B	278 M
Max context length	10,240 tokens (query + document)	8,192 tokens
Image processing	768 × 28 × 28 patches (dynamic resolution)	❌
Multilingual support	29+ languages	Multiple languages
Tasks supported	Text2Text, Text2Image, Image2Text, Text2Mixed	Text2Text

Capabilities

Multimodal Understanding: Processes both textual and visual content, including pages with mixed text, figures, tables, and various layouts
Long Context Processing: Handles up to 10K tokens, enabling reranking of lengthy documents
Dynamic Image Resolution: Supports images from 56×56 pixels up to 4K resolution with dynamic patch processing
Multilingual Support: Effectively reranks content across 29+ languages, including bidirectional language pairs
Zero-shot Domain Transfer: Performs well on unseen domains and document types without specific fine-tuning
Code Search: Enhanced capabilities for programming language search and technical document ranking

Compared to jina-reranker-v2-base-multilingual, jina-reranker-m0 significantly improves text reranking for multilingual content, long documents, and code searching tasks, while adding powerful new capabilities for visual document understanding.

Usage

The easiest way to use jina-reranker-m0 is to call Jina AI's Reranker API.

curl -X POST \
  https://api.jina.ai/v1/rerank \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer JINA_API_KEY" \
  -d '{
  "model": "jina-reranker-m0",
  "query": "slm markdown",
  "documents": [
    {
      "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png"
    },
    {
      "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"
    },
    {
      "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/wired-preview.png"
    },
    {
      "text": "We present ReaderLM-v2, a compact 1.5 billion parameter language model designed for efficient web content extraction. Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models. The models effectiveness results from two key innovations: (1) a three-stage data synthesis pipeline that generates high quality, diverse training data by iteratively drafting, refining, and critiquing web content extraction; and (2) a unified training framework combining continuous pre-training with multi-objective optimization. Intensive evaluation demonstrates that ReaderLM-v2 outperforms GPT-4o-2024-08-06 and other larger models by 15-20% on carefully curated benchmarks, particularly excelling at documents exceeding 100K tokens, while maintaining significantly lower computational requirements."
    },
    {
      "image": "https://jina.ai/blog-banner/using-deepseek-r1-reasoning-model-in-deepsearch.webp"
    },
    {
      "text": "数据提取么？为什么不用正则啊，你用正则不就全解决了么？"
    },
    {
      "text": "During the California Gold Rush, some merchants made more money selling supplies to miners than the miners made finding gold."
    },
    {
      "text": "Die wichtigsten Beiträge unserer Arbeit sind zweifach: Erstens führen wir eine neuartige dreistufige Datensynthese-Pipeline namens Draft-Refine-Critique ein, die durch iterative Verfeinerung hochwertige Trainingsdaten generiert; und zweitens schlagen wir eine umfassende Trainingsstrategie vor, die kontinuierliches Vortraining zur Längenerweiterung, überwachtes Feintuning mit spezialisierten Kontrollpunkten, direkte Präferenzoptimierung (DPO) und iteratives Self-Play-Tuning kombiniert. Um die weitere Forschung und Anwendung der strukturierten Inhaltsextraktion zu erleichtern, ist das Modell auf Hugging Face öffentlich verfügbar."
    }
  ],
  "return_documents": false
}'

You will receive a JSON response with the relevance scores for each document in relation to the query. The response will look like this:

{
  "model":"jina-reranker-m0",
  "usage": {
    "total_tokens":2813
  },
  "results":[
    {
      "index":1,
      "relevance_score":0.9310624287463884
    },
    {
      "index":4,
      "relevance_score":0.8982678574191957
    },
    {
      "index":0,
      "relevance_score":0.890233167219021
    },
    ...
  ]
}

The relevance_score field indicates the relevance of each document to the query, with higher scores indicating greater relevance.

You can also use the transformers library to interact with the model programmatically.

Before you start, install the transformers libraries:

pip install transformers >= 4.47.3

If you run it on a GPU that support FlashAttention-2. By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100),

pip install flash-attn --no-build-isolation

And then use the following code snippet to load the model:

from transformers import AutoModel

# comment out the flash_attention_2 line if you don't have a compatible GPU
model = AutoModel.from_pretrained(
    'jinaai/jina-reranker-m0',
    torch_dtype="auto",
    trust_remote_code=True,
    attn_implementation="flash_attention_2"
)

model.to('cuda')  # or 'cpu' if no GPU is available
model.eval()

Now you can use the model function compute_score to compute the relevance scores for a query and a list of documents. The function takes a list of sentence pairs, where each pair consists of a query and a document. The model will return a list of scores indicating the relevance of each document to the query.

A. Visual Documents Reranking

For handling the image documents, you can use the following code snippet:

# Example query and documents
query = "slm markdown"
documents = [
    "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png",
    "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png",
    "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/wired-preview.png",
    "https://jina.ai/blog-banner/using-deepseek-r1-reasoning-model-in-deepsearch.webp"
]

# construct sentence pairs
image_pairs = [[query, doc] for doc in documents]

scores = model.compute_score(image_pairs, max_length=2048, doc_type="image")
# [0.49375027418136597, 0.7889736890792847, 0.47813892364501953, 0.5210812091827393]

B. Textual Documents Reranking

query = "slm markdown"
documents = [
    "We present ReaderLM-v2, a compact 1.5 billion parameter language model designed for efficient web content extraction. Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models. The models effectiveness results from two key innovations: (1) a three-stage data synthesis pipeline that generates high quality, diverse training data by iteratively drafting, refining, and critiquing web content extraction; and (2) a unified training framework combining continuous pre-training with multi-objective optimization. Intensive evaluation demonstrates that ReaderLM-v2 outperforms GPT-4o-2024-08-06 and other larger models by 15-20% on carefully curated benchmarks, particularly excelling at documents exceeding 100K tokens, while maintaining significantly lower computational requirements.",
    "数据提取么？为什么不用正则啊，你用正则不就全解决了么？",
    "During the California Gold Rush, some merchants made more money selling supplies to miners than the miners made finding gold.",
    "Die wichtigsten Beiträge unserer Arbeit sind zweifach: Erstens führen wir eine neuartige dreistufige Datensynthese-Pipeline namens Draft-Refine-Critique ein, die durch iterative Verfeinerung hochwertige Trainingsdaten generiert; und zweitens schlagen wir eine umfassende Trainingsstrategie vor, die kontinuierliches Vortraining zur Längenerweiterung, überwachtes Feintuning mit spezialisierten Kontrollpunkten, direkte Präferenzoptimierung (DPO) und iteratives Self-Play-Tuning kombiniert. Um die weitere Forschung und Anwendung der strukturierten Inhaltsextraktion zu erleichtern, ist das Modell auf Hugging Face öffentlich verfügbar.",
]

# construct sentence pairs
text_pairs = [[query, doc] for doc in documents]

scores = model.compute_score(text_pairs, max_length=1024, doc_type="text")

The scores will be a list of floats, where each float represents the relevance score of the corresponding document to the query. Higher scores indicate higher relevance. For instance the returning scores in this case will be:

[0.6839263439178467, 0.4432148039340973, 0.5904013514518738, 0.45481112599372864]

C. Image Querying for Textual Documents

The model also supports querying textual documents with an image query. You can use the following code snippet:

query = "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"

documents = [
    "We present ReaderLM-v2, a compact 1.5 billion parameter language model designed for efficient web content extraction. Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models. The models effectiveness results from two key innovations: (1) a three-stage data synthesis pipeline that generates high quality, diverse training data by iteratively drafting, refining, and critiquing web content extraction; and (2) a unified training framework combining continuous pre-training with multi-objective optimization. Intensive evaluation demonstrates that ReaderLM-v2 outperforms GPT-4o-2024-08-06 and other larger models by 15-20% on carefully curated benchmarks, particularly excelling at documents exceeding 100K tokens, while maintaining significantly lower computational requirements.",
    "数据提取么？为什么不用正则啊，你用正则不就全解决了么？",
    "During the California Gold Rush, some merchants made more money selling supplies to miners than the miners made finding gold.",
    "Die wichtigsten Beiträge unserer Arbeit sind zweifach: Erstens führen wir eine neuartige dreistufige Datensynthese-Pipeline namens Draft-Refine-Critique ein, die durch iterative Verfeinerung hochwertige Trainingsdaten generiert; und zweitens schlagen wir eine umfassende Trainingsstrategie vor, die kontinuierliches Vortraining zur Längenerweiterung, überwachtes Feintuning mit spezialisierten Kontrollpunkten, direkte Präferenzoptimierung (DPO) und iteratives Self-Play-Tuning kombiniert. Um die weitere Forschung und Anwendung der strukturierten Inhaltsextraktion zu erleichtern, ist das Modell auf Hugging Face öffentlich verfügbar.",
]
# reverse the order of the query and document
image_pairs = [[query, doc] for doc in documents]
scores = model.compute_score(image_pairs, max_length=2048, query_type="image", doc_type="text")

# [0.98099285364151, 0.7701883316040039, 0.5637142062187195, 0.9308615922927856]

D. Image Querying for Image Documents

The model also supports querying image documents with an image query. You can use the following code snippet:

query = "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"

documents = [
    "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png",
    "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png",
    "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/wired-preview.png",
    "https://jina.ai/blog-banner/using-deepseek-r1-reasoning-model-in-deepsearch.webp"
]

image_pairs = [[query, doc] for doc in documents]
scores = model.compute_score(image_pairs, max_length=2048, doc_type="image", query_type='image')
# [0.6275860667228699, 0.9922324419021606, 0.8090347051620483, 0.7941296100616455]

Model Performance

Performance of the jina-reranker-m0 on ViDoRe, MBEIR, and Winoground visual retrieval benchmarks showcases its capabilities across diverse multimodal retrieval tasks spanning multiple domains and languages. Each dot represents performance scores for different types of visual documents. The boxplots illustrate the distribution of these scores, with the highlighted numbers indicating the average (mean) performance. For complete benchmark results, please refer to the appendix of this post.

We conduct extensive evaluations on the performance of the model across various visual retrieval benchmarks.

As shown in the figure above, the performance of the jina-reranker-m0 on ViDoRe, MBEIR, and Winoground visual retrieval benchmarks showcases its capabilities across diverse multimodal retrieval tasks spanning multiple domains and languages. Each dot represents performance scores for different types of visual documents. The boxplots illustrate the distribution of these scores, with the highlighted numbers indicating the average (mean) performance.

We also evaluate the performance of the jina-reranker-m0 across four text-to-text reranking benchmarks. Each benchmark may include multiple datasets, languages, or tasks, represented by individual dots inside the boxplot. The boxplot shows the distribution of these scores, with the highlighted number showing the average (mean) performance. While most benchmarks use NDCG@10 as their performance metric, MKQA uses recall@10 instead, as MKQA's annotation data doesn't support NDCG calculation (the official evaluation uses recall, which determines document relevance through heuristics).

For complete benchmark results, please refer to the online results table.

Contact

Join our Discord community and chat with other community members about ideas.

License

jina-reranker-m0 is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to contact us.

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