JaColBERTv2.5
| Property | Value |
|---|---|
| Parameter Count | 111M |
| License | MIT |
| Paper | View Paper |
| Base Model | cl-tohoku/bert-base-japanese-v3 |
| Tensor Type | F32 |
What is JaColBERTv2.5?
JaColBERTv2.5 is an advanced Japanese language model specifically designed for sentence similarity tasks. Built upon the ColBERT architecture, this model represents a significant advancement in Japanese text retrieval systems, achieving state-of-the-art performance while using only 40% of the training data compared to its predecessor.
Implementation Details
The model utilizes RAGatouille library and is built on top of the bert-base-japanese-v3 architecture. It implements an optimized multi-vector retrieval approach, trained on five diverse datasets including MIRACL, MMARCO-japanese, JQaRA, JaGovFaqs-22k, and MMARCO.
- Optimized training recipe for improved performance
- Efficient resource utilization with reduced training data
- Enhanced multi-vector retrieval capabilities
Core Capabilities
- State-of-the-art Japanese sentence similarity matching
- Outperforms previous approaches including JaColBERTv2 and BGE-M3
- Optimized for production deployment with F32 tensor support
- Specialized in Japanese language understanding and retrieval
Frequently Asked Questions
Q: What makes this model unique?
JaColBERTv2.5 stands out for its ability to achieve superior performance while using significantly less training data than previous versions. It specifically excels in Japanese language processing and has demonstrated better results than multilingual models like BGE-M3.
Q: What are the recommended use cases?
The model is ideal for Japanese text retrieval systems, document similarity matching, and semantic search applications. It's particularly well-suited for applications requiring precise sentence similarity measurements in Japanese language contexts.
