Model reference · open weights

splade-distilbert

Embeddings naver Embeddings 1 build Non-commercial 7k dl/mo

splade-distilbert is an open-weight embedding model from naver. splade-v3-distilbert (BF16) weighs 268 MB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • SPLADE-v3-DistilBERT is a feature-extraction model developed by Naver that maps text to a 30522-dimensional sparse vector space for semantic search and sparse retrieval.
  • Based on the DistilBERT architecture, it supports a maximum sequence length of 512 tokens and is designed for English language processing.
  • The model is released under the cc-by-nc-sa-4.0 license.

Summary of the naver/splade-v3-distilbert model card, 2026-10-01

What it is

Released bynaver
Released2024-03-08
VRAM268 MB for the weights

What it runs on

Memory and cards for splade-v3-distilbert (BF16)

268 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What naver says about splade-distilbert

Read the model card

SPLADE-v3-DistilBERT

SPLADE-v3-DistilBERT is the DistilBERT version of naver/splade-v3.

For more details, see our arXiv companion book: https://arxiv.org/abs/2403.06789 To use SPLADE, please visit our GitHub repository: https://github.com/naver/splade

Performance

MRR@10 (MS MARCO dev)avg nDCG@10 (BEIR-13)
naver/splade-v3-distilbert38.750.0

Model Details

This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Description

  • Model Type: SPLADE Sparse Encoder
  • Base model: distilbert/distilbert-base-uncased
  • Maximum Sequence Length: 512 tokens (256 for evaluation reproduction)
  • Output Dimensionality: 30522 dimensions
  • Similarity Function: Dot Product

Full Model Architecture

SparseEncoder(
  (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("naver/splade-v3-distilbert")
# Run inference
queries = ["what causes aging fast"]
documents = [
    "UV-A light, specifically, is what mainly causes tanning, skin aging, and cataracts, UV-B causes sunburn, skin aging and skin cancer, and UV-C is the strongest, and therefore most effective at killing microorganisms. Again â\x80\x93 single words and multiple bullets.",
    "Answers from Ronald Petersen, M.D. Yes, Alzheimer's disease usually worsens slowly. But its speed of progression varies, depending on a person's genetic makeup, environmental factors, age at diagnosis and other medical conditions. Still, anyone diagnosed with Alzheimer's whose symptoms seem to be progressing quickly â\x80\x94 or who experiences a sudden decline â\x80\x94 should see his or her doctor.",
    "Bell's palsy and Extreme tiredness and Extreme fatigue (2 causes) Bell's palsy and Extreme tiredness and Hepatitis (2 causes) Bell's palsy and Extreme tiredness and Liver pain (2 causes) Bell's palsy and Extreme tiredness and Lymph node swelling in children (2 causes)",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[14.4726,  9.4432,  5.6896]])

Citation

If you use our checkpoint, please cite our work:

@misc{lassance2024spladev3,
      title={SPLADE-v3: New baselines for SPLADE},
      author={Carlos Lassance and Hervé Déjean and Thibault Formal and Stéphane Clinchant},
      year={2024},
      eprint={2403.06789},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms