naver / v-splade-efficient

V-SPLADE: Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search

Paper: arXiv:2605.30917 · Code: github.com/naver/v-splade

This repository hosts the Efficient variant (lower FLOPs). For the higher-quality checkpoint, see naver/v-splade-quality .

Model Summary

V-SPLADE is a 0.25B (250M) inference-free sparse retriever for visual-document retrieval — retrieving image-based document pages (rendered PDFs, slides, scanned reports) from a text query.

• Inference-free — queries are resolved by a learned Bag-of-Words lookup with no neural query encoding at serving time , so retrieval runs on a standard inverted index (Pyserini / PISA) without a GPU.
• Direct visual embedding — document pages are encoded directly into sparse vectors, building indexes over 20× faster than caption- or OCR-based text-extraction pipelines.

Benchmark Performance

Six visual-document benchmarks (NDCG@5)

V-SPLADE Quality improves average NDCG@5 by +13.8pp over the same-scale dense baseline (BiModernVBERT) and by up to +6.3pp over the OCR/caption BM25 baselines.

Production-scale retrieval (18.7M-document corpus)

V-SPLADE more than doubles R@5 over the same-backbone dense retriever at production scale, and retains recall more robustly as the corpus grows from 500K to 18.7M pages.

Document encoding throughput

Measured on a single H100 GPU with 4 CPU cores, using 1,000 sampled documents across the six benchmarks. V-SPLADE is over 20× faster than caption- or OCR-based text-extraction pipelines for index building.

Quick Start

Install (see the code repository for full instructions):

git clone https://github.com/naver/v-splade.git
cd v-splade
python -m venv .venv && source .venv/bin/activate
pip install --upgrade pip
pip install torch torchvision torchaudio \
    --index-url https://download.pytorch.org/whl/cu128
grep -v -E '^(torch|flash-attn)==' requirements.txt > requirements_filtered.txt
pip install -r requirements_filtered.txt
pip install flash-attn==2.8.3 --no-build-isolation --no-cache-dir

Single-image inference (minimal example)

The shortest path to seeing V-SPLADE work on your own page image — encode one image into a sparse vocabulary vector, inspect the top-activated tokens, and score a text query against it:

python examples/quickstart.py \
    --hf_dir  naver/v-splade-efficient \
    --image   examples/sample_page.png \
    --queries "send signed forms" "records office"

Expected output (against the sample page):

[2/3] Encoding image: examples/sample_page.png
      sparse vector shape=(50368,)  nnz=552  max=1.836
      Top-10 activated tokens:
          1.836   'dog'
          1.672   'dogs'
          1.586   'puppy'
          1.570   'Records'
          1.523   'Bennett'
          ...
[3/3] Query-image similarity scores
        score=  0.997   query='send signed forms'
          top matches: forms(0.438), send(0.403), signed(0.156)
        score=  0.594   query='records office'
          top matches: office(0.594)

License

This model and the accompanying code are released under the Apache License 2.0 . See LICENSE in the repository for the full text.

Base model ( ModernVBERT/modernvbert ) and caption generator ( Qwen3-VL-30B-A3B ) are subject to their own licenses; please review them before redistribution or commercial use.

Training data. This model was trained on vidore/colpali_train_set and rlhn/rlhn-680K . rlhn/rlhn-680K is distributed under CC BY-SA 4.0 . vidore/colpali_train_set is a collection of multiple source datasets, each of which remains under its own original license.

Citation

@misc{cho2026vsplade,
  title         = {Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search},
  author        = {Cho, Gyu-Hwung and Lee, Youngjune and Jeong, Kiyoon and Lee, Siyoung and Han, Sanggyu and Dejean, Herv{\'e} and Clinchant, St{\'e}phane and Hwang, Seung-won},
  year          = {2026},
  eprint        = {2605.30917},
  archivePrefix = {arXiv},
  primaryClass  = {cs.IR}
}

Authors

Gyu-Hwung Cho (NAVER Corp. & Seoul National University), Youngjune Lee, Kiyoon Jeong, Siyoung Lee, Sanggyu Han (NAVER Corp.), Hervé Dejean, Stéphane Clinchant (Naver Labs Europe), Seung-won Hwang (Seoul National University, corresponding).

Contact

Issues and pull requests welcome at github.com/naver/v-splade . For research questions, contact the author at [email protected] .