---
title: "Naver's SPLADE Model Hits 878K Downloads Doubling BM25 Search Accuracy"
subtitle: "Naver's sparse retrieval model hits 878K downloads as teams rediscover SPLADE for hybrid search pipelines that need lexical precision plus neural understanding."
url: "https://alphasignal.ai/news/naver-s-splade-model-hits-878k-downloads-doubling-bm25-search-accuracy"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-09-25T07:34:58.611Z"
updated: "2026-09-25T07:39:25.477Z"
type: "Model"
topics:
  - "Retrieval"
  - "Open Source"
subtopics:
  - "Embeddings"
  - "Rag"
upvotes: 3359
source_url: "https://huggingface.co/naver/splade-cocondenser-selfdistil"
comments: 0
truncated: true
---

# Naver's SPLADE Model Hits 878K Downloads Doubling BM25 Search Accuracy

> Naver's sparse retrieval model hits 878K downloads as teams rediscover SPLADE for hybrid search pipelines that need lexical precision plus neural understanding.

# Why Naver’s SPLADE retriever is trending again

Hugging Face listed Naver Labs’ [splade-cocondenser-selfdistil](https://huggingface.co/naver/splade-cocondenser-selfdistil) among its trending models when this article was prepared, and its public counter had passed 878,000 downloads. The checkpoint gives retrieval-augmented generation systems learned semantic expansion, inspectable token weights, and efficient posting-list search.

Hugging Face counters measure file requests and can include repeat downloads, caches, and automated jobs, so they do not represent unique production deployments. The activity still draws attention to a common sparse-retrieval baseline released with 2022 research on hard-negative mining and self-distillation.

## A vocabulary becomes the vector space

Each SPLADE, short for SParse Lexical AnD Expansion, embedding occupies a 30,522-dimensional space derived from BERT’s WordPiece vocabulary. Every dimension represents a vocabulary item, including full words, subwords, punctuation, and special tokens.

The model’s masked-language-model head scores that vocabulary at every input position. A nonnegative transformation and max pooling retain the strongest score for each item, producing a weighted sparse vector in which most dimensions are zero. The model can assign weight to terms absent from the source text, which supplies its learned query and document expansion.

Similarity uses a dot product:

```
score(query, document) = query_vector · document_vector
```

The score is the sum of products for vocabulary dimensions shared by the query and document vectors. Those dimensions can be stored as impact-weighted postings in an inverted index. The checkpoint uses a BERT-base backbone with roughly 110 million parameters, so query encoding still requires a neural-model pass even when retrieval runs on CPUs.

## The benchmark numbers, decoded

MS MARCO passage ranking evaluates retrieval over about 8.8 million web passages using queries derived from Bing search logs. The checkpoint’s model card reports the following results on the development set:

__Reported MS MARCO development results__
| Metric | Score          | Meaning                                                                        |
| ------ | -------------- | ------------------------------------------------------------------------------ |
| MRR@10 | 37.6, or 0.376 | How highly the first judged-relevant passage appears within the top 10 results |

_Preview only. Full text requires an AlphaSignal Pro account._

## Sources

- [naver/splade-cocondenser-selfdistil on Hugging Face](https://huggingface.co/naver/splade-cocondenser-selfdistil)
- [From Distillation to Hard Negative Sampling (arXiv 2205.04733)](https://arxiv.org/abs/2205.04733)
- [naver/splade-v3 on Hugging Face](https://huggingface.co/naver/splade-v3)
- [SPLADE-v3 technical report summary](https://www.emergentmind.com/papers/2403.06789)
- [CSPLADE: Learned Sparse Retrieval with Causal Language Models](https://arxiv.org/pdf/2504.10816)