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 expansion per token and case folding to keep its vectors sparse. With the Seismic index, it reaches over 97% recall in about 380 microseconds per query, and it ships under Apache 2.0. The post Linkup Research Releases SPARSEUP: A 149M-Parameter Open-Source Sparse Embedding Model appeared first on MarkTechPost .
Strategic AI Brief
Impact Analysis
Linkup Research has released SPARSEUP, an open-source sparse embedding model built on a 149M-parameter ModernBERT backbone.
Market Signal
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.
Tactical Warning
The model uses a logit shift, top-12 expansion per token and case folding to keep its vectors sparse. With the Seismic index, it reaches over 97% recall in about 380 microseconds per query, and it ships under Apache 2.0. The post Linkup Research Releases SPARSEUP: A 149M-Parameter Open-Source Sparse Embedding Model appeared first on MarkTechPost .