Linkup Research Releases SPARSEUP: A 149M-Parameter Open-Source Sparse Embedding Model
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 Sentence Transformers with trust_remote_code=True. Why
Linkup Research released an open-source sparse embedding model with 149 million parameters that outputs vocabulary weights rather than single vectors, making it readable and compatible with inverted indexes. The model achieved a benchmark score of 56.4 on BEIR-13, which the team claims is the strongest performance among public sparse encoders under 150 million parameters. Sparse models differ from the more common dense embedding approaches because they can match rare words more effectively and allow each dimension to map to an actual token. The model was built using techniques including logit shifting, per-position top-k filtering, and case folding to prevent the output from becoming overly dense with stopwords.
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