Splade Cocondenser Selfdistil
S
Splade Cocondenser Selfdistil
Developed by naver
SPLADE model for passage retrieval, improving retrieval effectiveness through sparse latent document expansion and knowledge distillation techniques
Downloads 16.11k
Release Time : 5/9/2022
Model Overview
This model is a passage retrieval model based on the SPLADE architecture, utilizing co-condenser and self-distillation techniques to optimize query expansion and document expansion processes, achieving efficient retrieval on top of the bag-of-words model
Model Features
Sparse Latent Document Expansion
Uses sparse representation methods to expand document vectors, improving retrieval efficiency
Knowledge Distillation Technique
Optimizes model performance through self-distillation methods, enhancing retrieval accuracy
Efficient Passage Retrieval
Achieves high recall and accuracy rates on the MS MARCO dataset
Model Capabilities
Query Expansion
Document Expansion
Passage Retrieval
Information Retrieval
Use Cases
Information Retrieval Systems
Search Engine Enhancement
Used to improve the passage retrieval capability of search engines
Achieves MRR@10 of 37.6 and R@1000 of 98.4 on the MS MARCO development set
Question Answering Systems
Serves as the retrieval component for question answering systems to quickly locate relevant passages
Featured Recommended AI Models