cross-encoder-mmarco-mMiniLMv2-L12-H384-v1
| Property | Value |
|---|---|
| License | Apache License 2.0 |
| Original Author | cross-encoder |
| Current Publisher | corrius |
| Model Hub URL | https://huggingface.co/corrius/cross-encoder-mmarco-mMiniLMv2-L12-H384-v1 |
What is cross-encoder-mmarco-mMiniLMv2-L12-H384-v1?
This is a re-uploaded version of the original mmarco-mMiniLMv2-L12-H384-v1 model, specifically designed for re-ranking tasks in multilingual contexts. The model utilizes the MiniLM architecture with 12 layers and a hidden size of 384 dimensions, optimized for efficient cross-encoding of query-document pairs.
Implementation Details
The model implements a cross-encoder architecture based on mMiniLMv2, which is particularly efficient for re-ranking tasks. It processes query-document pairs simultaneously to produce relevance scores, leveraging the power of transformer-based architectures while maintaining computational efficiency.
- Based on MiniLM architecture with 12 layers
- Hidden size of 384 dimensions
- Optimized for multilingual applications
- Specifically tuned for re-ranking tasks
Core Capabilities
- Document re-ranking in multilingual contexts
- Efficient query-document pair processing
- Cross-encoding for relevance scoring
- Support for multiple languages in information retrieval tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on multilingual re-ranking tasks, utilizing an efficient MiniLM architecture that balances performance with computational requirements. It's particularly valuable for applications requiring document re-ranking across multiple languages.
Q: What are the recommended use cases?
The model is best suited for applications involving document re-ranking, particularly in multilingual information retrieval systems. It can be effectively used in search engines, content recommendation systems, and any application requiring precise ranking of document relevance to queries across different languages.
