jina-embeddings

About

What jina-embeddings is

Quick Start Blog | Azure | AWS SageMaker | API Intended Usage & Model Info jina-embeddings-v3 is a multilingual multi-task text embedding model designed for a variety of NLP applications. Based on the Jina-XLM-RoBERTa architecture, this model supports Rotary Position Embeddings to handle long input sequences up to 8192 tokens. Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently. Key Features: - Extended Sequence Length: Supports up to 8192 tokens with RoPE. - Task-Specific Embedding: Customize embeddings through the task argument with the following options: - retrieval.query: Used for query embeddings in asymmetric retrieval tasks - retrieval.passage: Used for passage embeddings in asymmetric retrieval tasks - separation: Used for embeddings in clustering and re-ranking applications - classification: Used for embeddings in classification tasks - text-matching: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks - Matryoshka Embeddings: Supports flexible embedding sizes (32, 64, 128, 256, 512, 768, 1024), allowing for truncating embeddings to fit your application. Supported Languages: While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ⚠️ Important Notice: We fixed a bug in the encode function #60 where Matryoshka embedding truncation occurred after normalization, leading to non-normalized truncated embeddings. This issue has been resolved in the latest code revision. If you have encoded data using the previous version and wish to maintain consistency, please use the specific code revision when loading the model: AutoModel.frompretrained('jinaai/jina-embeddings-v3', coderevision='da863dd04a4e5dce6814c6625adfba87b83838aa', ...) Usage <details<summaryApply mean pooling when integrating the model.</summary Why Use Mean Pooling? Mean poolin

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys jina-embeddings for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-embeddings below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"jina-embeddings","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Commercial licence needed

Sources

Weights & code

Explore

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