jina-reranker-v2-base-multilingual
| Property | Value |
|---|---|
| Parameter Count | 278M |
| License | CC-BY-NC-4.0 |
| Tensor Type | BF16 |
| Max Context Length | 1024 tokens |
What is jina-reranker-v2-base-multilingual?
Jina Reranker v2 is a sophisticated cross-encoder model designed for multilingual text reranking tasks. Developed by Jina AI, this model represents a significant advancement in information retrieval systems, capable of processing and scoring query-document pairs across multiple languages with high accuracy. The model utilizes flash attention mechanism for enhanced performance and can handle contexts up to 1024 tokens.
Implementation Details
The model employs a transformer-based architecture optimized for cross-encoding tasks. It features a sliding window approach for handling long texts, making it particularly effective for extensive document processing. The implementation includes flash attention capabilities for 3x-6x speedup in inference times.
- Multilingual support with strong performance across 26 languages
- Advanced sliding window mechanism for long document processing
- Flash attention integration for accelerated computation
- Flexible API integration options through transformers library
Core Capabilities
- High-performance document reranking across multiple languages
- Superior benchmark performance in MKQA, BEIR, and MLDR evaluations
- Efficient handling of long-form content through chunking
- Support for both research and commercial applications via API
Frequently Asked Questions
Q: What makes this model unique?
The model combines multilingual capabilities with state-of-the-art reranking performance, achieving impressive scores across various benchmarks including MKQA (54.83 nDCG@10) and BEIR (53.17 nDCG@10). Its flash attention mechanism and efficient handling of long documents make it particularly suitable for production environments.
Q: What are the recommended use cases?
The model excels in multilingual search applications, information retrieval systems, and document ranking tasks. It's particularly effective for scenarios requiring cross-lingual document comparison, search result optimization, and content recommendation systems.
