Splade Cocondenser Ensembledistil
S
Splade Cocondenser Ensembledistil
Developed by naver
SPLADE model for passage retrieval, improving sparse neural information retrieval through knowledge distillation
Downloads 606.73k
Release Time : 5/9/2022
Model Overview
This model is a passage retrieval model based on the SPLADE architecture, optimized for retrieval performance using co-condenser and ensemble distillation techniques, particularly suitable for large-scale document retrieval tasks.
Model Features
Sparse Latent Document Expansion
Uses sparse representation methods to expand document and query representations, improving retrieval efficiency
Knowledge Distillation Optimization
Enhances model performance through ensemble distillation techniques, achieving more precise retrieval results
Efficient Passage Retrieval
Optimized for large-scale passage retrieval tasks, balancing retrieval accuracy and efficiency
Model Capabilities
Document Retrieval
Query Expansion
Passage Matching
Information Retrieval
Use Cases
Information Retrieval Systems
Search Engine Enhancement
Used to improve document retrieval capabilities of search engines
Achieved MRR@10 38.3 and R@1000 98.3 performance on the MS MARCO dataset
Question Answering Systems
Serves as the retrieval component for question answering systems to quickly locate relevant documents
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