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Linkup Research has introduced SPARSEUP, an open-source sparse embedding model that achieves state-of-the-art retrieval performance under 150M parameters using a ModernBERT backbone. By employing logit shifting and per-token top-k expansion, the model effectively mitigates stopword noise while maintaining compatibility with inverted indexes for efficient querying. This release completes a comparative suite alongside recent dense models from LightOn, offering researchers a standardized benchmark for evaluating different retrieval architectures on public datasets.

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Linkup Research Releases SPARSEUP: A 149M-Parameter Open-Source Sparse Embedding Model

MarkTechPostSeptember 19, 20264 min read

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.

Linkup research team releases SPARSEUP, an open-source learned sparse embedding model. The model runs on a 149M-parameter ModernBERT backbone and ships under Apache 2.0. Linkup team reports 56.4 average nDCG@10 on BEIR-13. It calls this the strongest public vocabulary-based sparse encoder it knows of under 150M parameters. Is it deployable? Yes. The weights are on Hugging Face under Apache 2.0. The model loads through Transformers or…

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