SPLADE CoCondenser EnsembleDistil
| Property | Value |
|---|---|
| License | cc-by-nc-sa-4.0 |
| Paper | View Paper |
| Performance (MRR@10) | 38.3 on MS MARCO dev |
| Recall@1000 | 98.3 on MS MARCO dev |
What is splade-cocondenser-ensembledistil?
SPLADE CoCondenser EnsembleDistil is a specialized neural model designed for efficient passage retrieval. Developed by NAVER, it implements an innovative approach combining knowledge distillation with ensemble techniques to create a powerful yet efficient information retrieval system.
Implementation Details
The model utilizes BERT-based architecture with specific optimizations for query and document expansion. It implements the SPLADE (Sparse Lexical and Expansion) methodology, which creates sparse representations of both queries and documents, making it particularly efficient for large-scale retrieval tasks.
- Employs knowledge distillation techniques for model optimization
- Implements bag-of-words representation for efficient processing
- Utilizes ensemble learning for improved performance
- Specialized in passage retrieval tasks
Core Capabilities
- Query expansion for enhanced search accuracy
- Document expansion for better content representation
- Efficient passage retrieval with high recall rates
- Sparse representation learning for scalable search
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of SPLADE architecture with knowledge distillation and ensemble techniques, achieving strong performance (38.3 MRR@10) while maintaining efficient sparse representations for practical deployment.
Q: What are the recommended use cases?
The model is particularly well-suited for large-scale passage retrieval tasks, information retrieval systems, and search applications where both accuracy and efficiency are crucial. It's especially effective for applications requiring precise document matching and ranking.
