Linkup Research Releases SPARSEUP: A 149M-Parameter Open-Source Sparse Embedding Model
Linkup Research released SPARSEUP, a 149M-parameter open-source sparse embedding model under Apache 2.0, scoring 56.4 nDCG@10 on BEIR-13.
Linkup Research released SPARSEUP, an open sparse embedding model built on a 149M-parameter ModernBERT backbone with weights available on Hugging Face under Apache 2.0. It was fine-tuned from LightOn's LateOn checkpoint using contrastive learning with hard negatives, adding a log(1+ReLU(x-15)) logit shift, per-token top-12 expansion, and byte-BPE case folding to control sparsity. Linkup reports 56.4 average nDCG@10 on BEIR-13, ahead of opensearch-neural-sparse (54.6) and splade-v3 (51.7), though it trails LightOn's DenseOn (57.9) and LateOn (58.9) under identical backbone and data. With a Seismic inverted index it achieves over 97% recall versus exact search at roughly 380 microseconds per query on MS MARCO.