Independent Research
Splade (Sparse Lexical and Expansion Model)
Hybrid sparse-dense retrieval for enhanced search.
Combines sparse and dense retrieval beneEffective for out-of-vocabulary termsImproves retrieval precisionOpen-source implementations available
Today's score
88.0
Where it ranks today
Best for / Not great for
Best for
- Improving traditional keyword search with semantics
- RAG requiring robust term expansion
- Search engines dealing with typos and variations
- Academic research in information retrieval
Not great for
- Purely dense embedding tasks
- Users seeking simple API endpoints
- Applications without a clear need for sparse retrieval
Why it ranks here
Splade represents a significant advancement in retrieval by bridging sparse and dense models. While not a direct embedding model in the traditional sense, its impact on search and RAG performance, particularly for handling term mismatches, earns it a high spot for those seeking state-of-the-art retrieval accuracy.
30-day trend
Score breakdown
Search trends94
Benchmarks88
Developer buzz87
News mentions89
Pricing
API: $0.00 in · $0.00 out per 1M tokens · Consumer: $0.00/mo
Pricing plans
Popular
Open Source Implementation
Implement and adapt freely.
Free
- Available on GitHub
- Requires ML expertise
- Supports various frameworks (PyTorch)
- Research-focused