jina-colbert-v1-en
| Property | Value |
|---|---|
| Parameter Count | 137M |
| License | Apache 2.0 |
| Language | English |
| Training Data | MS MARCO |
What is jina-colbert-v1-en?
Jina-ColBERT is an advanced neural search model that combines the efficiency of ColBERT architecture with the extended context capabilities of JinaBERT. Built by Jina AI, this model stands out for its ability to handle up to 8,000 tokens in context length while maintaining fast and accurate retrieval performance.
Implementation Details
The model is based on the JinaBERT architecture, implementing symmetric bidirectional ALiBi for extended sequence length support. It was trained on the MSMARCO passage ranking dataset, following similar procedures to ColBERTv2 but using jina-bert-v2-base-en as its backbone instead of bert-base-uncased.
- Supports 8k context length for document processing
- Utilizes ColBERT's late-interaction mechanism
- Implements efficient passage retrieval techniques
- Maintains competitive performance with ColBERTv2
Core Capabilities
- Long-form document indexing (up to 8,192 tokens)
- Efficient passage retrieval and ranking
- Zero-shot transfer to various domains
- Competitive performance on BEIR benchmark suite
- Superior performance on long-context datasets
Frequently Asked Questions
Q: What makes this model unique?
The model's unique feature is its ability to handle 8k context length while maintaining competitive retrieval performance, making it especially suitable for long-document processing tasks. It achieves this while matching or exceeding ColBERTv2's performance on standard benchmarks.
Q: What are the recommended use cases?
The model is ideal for building neural search systems, particularly those dealing with long documents. It excels in passage retrieval, document ranking, and zero-shot transfer to various domains, making it suitable for enterprise search, research paper retrieval, and general information retrieval systems.
