jina-embeddings-v3
| Property | Value |
|---|---|
| License | CC BY-NC 4.0 |
| Architecture | Jina-XLM-RoBERTa with RoPE |
| Max Sequence Length | 8192 tokens |
| Languages Supported | 30 languages |
What is jina-embeddings-v3?
jina-embeddings-v3 is a state-of-the-art multilingual text embedding model that leverages task-specific LoRA adapters and Matryoshka embedding capabilities. Built on the Jina-XLM-RoBERTa architecture, it supports extended sequence lengths up to 8192 tokens through Rotary Position Embeddings (RoPE), making it highly versatile for various NLP applications.
Implementation Details
The model features five specialized LoRA adapters for different tasks: retrieval (query and passage), separation, classification, and text-matching. It implements Matryoshka Embeddings, allowing flexible embedding sizes from 32 to 1024 dimensions, which can be adjusted based on specific application requirements.
- Supports 30 carefully tuned languages including major European, Asian, and Middle Eastern languages
- Implements mean pooling for high-quality sentence embeddings
- Features Flash Attention-2 support for compatible GPUs
- Offers ONNX inference capabilities for efficient deployment
Core Capabilities
- Task-specific embedding generation through LoRA adapters
- Flexible embedding dimensionality (32-1024) through Matryoshka architecture
- Extended context handling up to 8192 tokens
- Comprehensive multilingual support with focused tuning on 30 languages
- Fine-tuning capabilities through SentenceTransformers integration
Frequently Asked Questions
Q: What makes this model unique?
The combination of task-specific LoRA adapters, Matryoshka embeddings, and extensive multilingual support makes it highly versatile. The ability to handle long sequences and adjust embedding dimensions on-the-fly sets it apart from traditional embedding models.
Q: What are the recommended use cases?
The model excels in various NLP tasks including information retrieval, document classification, clustering, text similarity matching, and multilingual applications. Its task-specific adapters make it particularly effective for specialized use cases in each of these areas.
