jina-colbert-v2
Maintainer: jinaai
64
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The jina-colbert-v2 model is a new version of the JinaColBERT retrieval model developed by Jina AI. It builds upon the capabilities of the previous jina-colbert-v1-en model by adding multilingual support, improved efficiency and performance, and new Matryoshka embeddings that allow flexible trade-offs between precision and efficiency. Like its predecessor, jina-colbert-v2 uses a token-level late interaction approach to achieve high-quality retrieval results.
The model is an upgrade from the English-only jina-colbert-v1-en, with expanded support for dozens of languages while maintaining strong performance on major global languages. It also includes the improved efficiency, performance, and explainability benefits of the JinaBERT architecture and ALiBi that were introduced in the previous version.
Model inputs and outputs
Inputs
- Text to be encoded, up to 8192 tokens in length
Outputs
- Contextual token-level embeddings, with options for 128, 96, or 64 dimensions
- Ranking scores for retrieval, leveraging the late interaction mechanism
Capabilities
The jina-colbert-v2 model offers superior retrieval performance compared to the jina-colbert-v1-en model, particularly for longer documents. Its multilingual capabilities and flexible embeddings make it a versatile tool for a variety of neural search applications, including long-form document retrieval, semantic search, and question answering.
What can I use it for?
The jina-colbert-v2 model can be used to power neural search systems that require high-quality retrieval from large text corpora, including use cases like:
- Enterprise search: Indexing and retrieving relevant documents from an organization's knowledge base
- E-commerce search: Improving product and content discovery on online marketplaces
- Question answering: Retrieving the most relevant passages to answer user queries
The model's support for long input sequences and multiple languages makes it particularly well-suited for handling complex, multilingual search tasks.
Things to try
Some key things to explore with the jina-colbert-v2 model include:
- Evaluating the different embedding sizes: The model offers 128, 96, and 64-dimensional embeddings, allowing you to experiment with the trade-off between precision and efficiency.
- Leveraging the Matryoshka embeddings: The model's Matryoshka embeddings enable flexible retrieval, where you can balance between precision and speed as needed.
- Integrating the model into a broader neural search pipeline: The
jina-colbert-v2model can be used in conjunction with other components like rerankers and language models to create a end-to-end neural search system.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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