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  ·  Demo: 🤗 Space

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

🚀 Try it live: an interactive demo of the Quality variant, kindly contributed by Apolinário and the Hugging Face open-source team.

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)

Model	Size	ViDoRe v1	v2	v3	VisRAG	VisDoc OOD	IRPAPERS	Avg
BiModernVBERT (dense)	0.25B	67.6	35.7	28.9	60.5	53.4	31.8	46.3
BM25 (caption, Qwen3-VL)	—	67.5	44.1	38.3	76.5	58.0	38.4	53.8
BM25 (unstructured OCR)	—	68.2	41.7	38.7	61.1	51.2	65.7	54.4
V-SPLADE Quality	0.25B	77.4	49.9	40.9	76.4	61.7	54.0	60.1
V-SPLADE Efficient	0.25B	74.6	46.6	37.6	73.0	59.5	47.1	56.4

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)

Model	R@5	R@100	Query latency
BiModernVBERT (same-scale dense)	0.090	0.299	~HNSW
V-SPLADE	0.228	0.520	~HNSW approx

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

Method	Pages/sec
V-SPLADE (ours)	20.19
Qwen3-VL-30B-A3B caption (vLLM, eff. 3B)	0.83
Unstructured OCR (Tesseract hi_res)	0.90

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

Using Sentence Transformers

Install Sentence Transformers (v5.6.0 or later) with image support, and note that the ModernVBERT backbone requires transformers>=5.3.0:

pip install "sentence_transformers[image]>=5.6.0"

Queries are encoded with the inference-free Li-LSR lookup (no transformer forward pass), while document page images run through the full model:

from sentence_transformers import SparseEncoder

model = SparseEncoder("naver/v-splade-efficient", trust_remote_code=True)

queries = ["send signed forms", "records office"]
documents = ["https://raw.githubusercontent.com/naver/v-splade/main/examples/sample_page.png"]

query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# torch.Size([2, 50368]) torch.Size([1, 50368])

similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7757],
#         [0.4524]], device='cuda:0')

# Inspect the top activated tokens of the page image
decoded = model.decode(document_embeddings[0], top_k=5)
print([(token.replace("Ġ", " ").strip(), round(weight, 3)) for token, weight in decoded])
# [('dog', 1.664), ('Records', 1.5), ('puppy', 1.469), ('Bennett', 1.414), ('dogs', 1.398)]

Images can be passed as PIL images, local paths, URLs (as above), or together with text as {"image": ..., "text": ...}. Plain text documents are also supported: model.encode_document(["some passage text"]). The model runs in bfloat16 by default. You can pass model_kwargs={"torch_dtype": "float32"} for full precision.

Using the reference implementation

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 gyuhwung.cho@navercorp.com.