Linkup Research Releases SPARSEUP: A 149M-Parameter Open-Source Sparse Embedding Model
149M parameters. 56.4 nDCG@10. Linkup just shipped the best open sparse encoder under 150M — and it's fast enough for production search.
Why it matters
A practical open-weight embedding model that achieves state-of-the-art sparse retrieval performance at a size practitioners can run locally or on modest infrastructure. Relevant to builders optimizing RAG, semantic search, and retrieval systems where sparse vectors offer speed and interpretability advantages over dense alternatives.
The key facts
12 to knowSPARSEUP: 149M-parameter ModernBERT backbone
56.4 nDCG@10 on BEIR-13 benchmark (claimed best-in-class for sub-150M sparse encoders)
97% recall achieved in ~380 microseconds per query with Seismic index
Techniques: logit shift, top-12 token expansion, case folding for sparsity
Licensed under Apache 2.0 (open-source)
Released by Linkup Research
149M parameters (ModernBERT backbone)
56.4 nDCG@10 on BEIR-13 benchmark
Claims best public result for sparse encoder under 150M parameters
97%+ recall in ~380 microseconds per query with Seismic index
Apache 2.0 license (open-weight)
Uses logit shift, top-12 expansion per token, case folding for sparsity
Go to the source
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Publisher excerpt: Linkup Research has released SPARSEUP, an open-source sparse embedding model built on a 149M-parameter ModernBERT backbone. It scores 56.4 nDCG@10 on BEIR-13, which Linkup calls the best result it knows of for a public sparse encoder under 150M parameters. The model uses a logit shift, top-12…