lightonai/mSlideOn-sparse-vectors

mSlideOn sparse vectors for MS MARCO passage and BEIR

Pre-computed learned sparse vectors from lightonai/mSlideOn (revision ef80160) for the MS MARCO v1 passage corpus and the 29 BEIR v1.0.0 corpora distributed with Anserini, plus their evaluation queries. Document ids, query ids and texts follow Anserini exactly (BEIR documents are encoded as title + " " + text), so the vectors drop into Anserini/Pyserini impact indexes. See the SlideOn & mSlideOn blog post for the models.

Layout

One config per dataset; each row is one document or query:

column	type	content
id	string	Anserini docid / qid
indices	list	active latents (0–32767), sorted by decreasing weight
weights	list	the model's weights, exactly as it outputs them (FP16, no quantization)

Scores are raw dot products between query and document vectors (no log, no normalization).

Choosing the sparsity (Matryoshka budgets)

mSlideOn was trained with Matryoshka budgets of 512, 1024 or 2048 document latents and 128 or 256 query latents. Because each row is sorted by weight, keeping the first k entries is exactly the model's max_active_dims=k output:

• documents store the top 2048 latents (the largest trained document budget; docs with fewer active latents keep all of them). Truncating further (e.g. to 512) reduces index size and latency.
• queries are stored unpruned; truncate to the trained query budgets for faster search.

from datasets import load_dataset

docs = load_dataset("lightonai/mSlideOn-sparse-vectors", "beir-v1.0.0-scifact", split="corpus")
k = 512
docs = docs.map(lambda r: {"indices": r["indices"][:k], "weights": r["weights"][:k]})

Anserini

to_anserini.py (in this repo) truncates to top-k, quantizes weights as round(w * 100) (the SPLADE convention; costs ≤ 0.001 nDCG@10 in our checks) and writes JsonVectorCollection documents and TsvString topics:

python to_anserini.py beir-v1.0.0-scifact/corpus collections/scifact/part0.jsonl.gz --k 512
python to_anserini.py beir-v1.0.0-scifact/queries/test topics.scifact.tsv.gz --query

bin/run.sh io.anserini.index.IndexCollection -collection JsonVectorCollection -input collections/scifact \
  -index indexes/scifact.mslideon -generator DefaultLuceneDocumentGenerator -threads 16 -impact -pretokenized
bin/run.sh io.anserini.search.SearchCollection -index indexes/scifact.mslideon -topics topics.scifact.tsv.gz \
  -topicReader TsvString -output run.scifact.txt -impact -pretokenized -removeQuery -hits 1000

Citation

@misc{moura2026slideon,
  author       = {Moura Junior, Paulo Roberto and Sourty, Raphaël},
  title        = {{SlideOn} \& {mSlideOn}: Sparsified late-interaction and dense retrievers},
  year         = {2026},
  howpublished = {Hugging Face Blog},
  url          = {https://huggingface.co/blog/lightonai/slideon-mslideon}
}