jina-clip

This model is based on the paper jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images.

Quick Start

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Intended Usage & Model Info

jina-clip-v2 is a general-purpose multilingual multimodal embedding model for text & images.

Multimodal embeddings enable searching and understanding data across different modalities through a coherent representation. They serve as the backbone of neural information retrieval and multimodal GenAI applications.

Built upon jina-clip-v1 and our recently released jina-embeddings-v3, jina-clip-v2 features several significant improvements:

• Improved Performance: v2 shows a 3% performance improvement over v1 in both text-image and text-text retrieval tasks. Similar to v1, v2's text encoder can serve as an effective multilingual long-context dense retriever. It performs on par with our frontier model jina-embeddings-v3 (currently the best multilingual embeddings under 1B parameters on MTEB).
• Multilingual Support: Using the same backbone as jina-embeddings-v3 for the text tower, jina-clip-v2 supports 89 languages for multilingual-image retrieval, showing up to 4% improvement compared to nllb-clip-large-siglip on multilingual image retrieval tasks.
• Higher Image Resolution: v2 now supports 512x512 input image resolution, a significant increase from v1's 224x224. This higher resolution enables better processing of detailed images, improved feature extraction, and more accurate recognition of fine-grained visual elements.
• Matryoshka Representations: v2 allows users to truncate the output dimensions of both text and image embeddings from 1024 down to 64, reducing storage and processing overhead while maintaining strong performance.

Measuring 0.9B parameters, jina-clip-v2 combines two powerful encoders:

• the text encoder Jina-XLM-RoBERTa (the backbone of jina-embeddings-v3) and
• the vision encoder EVA02-L14 (an efficient vision Transformer developed by BAAI).

These encoders are jointly trained to create aligned representations of images and text.

CLIP-like models have established themselves as the backbone for general-purpose multimodal applications. With jina-clip-v2, we're taking these capabilities to the next level, breaking down language barriers to deliver more accurate cross-modal understanding and retrieval. We're confident this release delivers a promise in making multimodal search and retrieval both more powerful and more accessible to developers worldwide.

Training, Data, Parameters

Please refer to our technical report of jina-clip-v2 for the model and training details.

technical report of jina-clip-v1

Faster Inference: FA2, XFormers and bf16

On a CUDA enabled torch environment, the model comes in torch.bfloat16 precision by default. It is highly recommended to install FlashAttention and xFormers to make use of their efficient attention mechanism implementations.

Usage

curl https://api.jina.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer [JINA_AI_API_TOKEN]" \
  -d @- <<EOFEOF
  {
    "model": "jina-clip-v2",
    "dimensions": 1024,
    "task": "retrieval.query",
    "normalized": true,
    "embedding_type": "float",
    "input": [
        {
            "text": "غروب جميل على الشاطئ"
        },
        {
            "text": "海滩上美丽的日落"
        },
        {
            "text": "A beautiful sunset over the beach"
        },
        {
            "text": "Un beau coucher de soleil sur la plage"
        },
        {
            "text": "Ein wunderschöner Sonnenuntergang am Strand"
        },
        {
            "text": "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία"
        },
        {
            "text": "समुद्र तट पर एक खूबसूरत सूर्यास्त"
        },
        {
            "text": "Un bellissimo tramonto sulla spiaggia"
        },
        {
            "text": "浜辺に沈む美しい夕日"
        },
        {
            "text": "해변 위로 아름다운 일몰"
        },
        {
            "image": "https://i.ibb.co/nQNGqL0/beach1.jpg"
        },
        {
            "image": "https://i.ibb.co/r5w8hG8/beach2.jpg"
        }
    ]
  }
EOFEOF

# !pip install transformers einops timm pillow
from transformers import AutoModel

# Initialize the model
model = AutoModel.from_pretrained('jinaai/jina-clip-v2', trus