Sparseup-fr: A French replication of Linkup's sparse embedding model

Sparseup-fr-v0 is a French sparse retrieval embedding model that replicates Linkup’s Sparseup methodology by combining the multilingual mLateOn-unsupervised backbone with a trimmed French-focused mmBERT masked-language-model head of 50,240 tokens, resulting in a 150-million-parameter model. Trained for one epoch on French LightOn data using a single RTX 6000 Pro GPU over roughly 55 hours, it was evaluated on a cleaned NanoBEIR-fr benchmark designed to reduce training-data leakage and remove suspicious examples. Across both document truncation and sliding-window evaluation settings, late-interaction mLateOn models remained strongest overall, followed by dense models and then sparse approaches, while Sparseup-fr achieved the highest average performance among the compared French-capable open sparse models. It exceeded Splade-Camembert by more than three nDCG@10 points and OpenSearch’s multilingual sparse model by about two points, leading on eight of 13 benchmark datasets, although model-size differences limit direct equivalence. The authors also report a checkpoint-upload issue caused by oversized README metadata and propose further experiments involving additional data, weight merging, larger French variants, and a possible multilingual model, while releasing the initial model and cleaned benchmark.

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