🔎 KURE-Sparse-nano

KURE-Sparse-nano is a compact (0.1B) Korean-English learned sparse retrieval model. It shows state-of-the-art performance among < 0.5B sparse models supporting Korean, averaging 0.7716 nDCG@10 on MTEB(kor, v2) Retrieval. It also outperforms much larger dense models, such as pplx-embed-v1-4b and harrier-oss-v1-27b.

Key Characteristics

• Learned Sparse Retrieval (LSR): every vector lives in a fixed vocabulary of 36,282 terms, so documents can be indexed in an inverted index and matched terms are inspectable.
• Compact: 0.1B parameters.
• Long documents: up to 8,192 tokens.
• Sparse at both ends: on average 147 active dimensions per query and 843 per document.

Usage

pip install -U sentence-transformers

Use encode_query / encode_document: they add the query and document markers the model was trained with.

from sentence_transformers import SparseEncoder

model = SparseEncoder("nlpai-lab/KURE-Sparse-nano", trust_remote_code=True)

query = "훈민정음은 언제 만들어졌나요?"
documents = [
    "세종대왕은 1443년에 훈민정음을 창제하고 1446년에 이를 반포하였다.",
    "김치는 배추나 무를 소금에 절인 뒤 고춧가루와 젓갈을 넣어 발효시킨 음식이다.",
    "한라산은 해발 1,947m로 남한에서 가장 높은 산이며 제주도 중앙에 자리한다.",
]

query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# torch.Size([36282]) torch.Size([3, 36282])

# Dot-product scores (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[119.3931,  11.5758,   6.4037]])

Evaluation

KURE-Sparse-nano is evaluated on the nine MTEB(kor, v2) Retrieval tasks. We report nDCG@10.

Performance-Sparsity

Average active dimensions per vector and FLOPs, averaged over the nine tasks.

English Retrieval (BEIR)

KURE-Sparse-nano is also evaluated on the 13 English BEIR tasks (BEIR-13) with nDCG@10. SparseUp runs at its own length limits (128 query and 512 document tokens), and the other models at up to 8,192 tokens.

Per-task nDCG@10

Model	Avg	ArguAna	ClimateFEVER	DBPedia	FEVER	FiQA	HotpotQA	NFCorpus	NQ	Quora	SCIDOCS	SciFact	TREC-COVID	Touche-2020
Our Models
KURE-Sparse-base	0.5864	0.5896	0.3959	0.4837	0.9325	0.4720	0.8084	0.3678	0.6758	0.8252	0.1741	0.7183	0.8260	0.3535
KURE-Sparse-nano	0.5317	0.5075	0.2865	0.4214	0.9079	0.4080	0.7476	0.3340	0.5647	0.7825	0.1581	0.7120	0.7637	0.3183
Sparse Models
Linkup-Platform/linkup-sparseup-embed-v1	0.5645	0.5648	0.3747	0.4150	0.8969	0.4687	0.7761	0.3523	0.5717	0.8656	0.1852	0.7376	0.7823	0.3478
omai-research/milco-650m	0.5545	0.6157	0.3079	0.4501	0.8353	0.4262	0.7780	0.3629	0.6495	0.8818	0.1704	0.7020	0.7476	0.2813
telepix/PIXIE-Splade-v1.5	0.4149	0.2185	0.1302	0.3519	0.6475	0.2753	0.5756	0.3325	0.3858	0.6878	0.1468	0.6694	0.7068	0.2650

Citation

@misc{kure-sparse,
  title  = {KURE-Sparse: a Korean-English bilingual learned sparse retriever},
  author = {Jang, Youngjoon and Son, Junyoung and Lee, Taemin and Hong, Seongtae and Lim, Heuiseok},
  year   = {2026},
  url    = {https://huggingface.co/nlpai-lab/KURE-Sparse-nano},
}

@misc{kure-v2,
  title  = {KURE-v2: a Korean-English bilingual late-interaction retriever},
  author = {Jang, Youngjoon and Son, Junyoung and Lee, Taemin and Hong, Seongtae and Lim, Heuiseok},
  year   = {2026},
  url    = {https://huggingface.co/nlpai-lab/KURE-v2},
}

@inproceedings{jang2025kure,
  title={KURE: Embedding Model for Korean-Specific Retrieval},
  author={Jang, Youngjoon and Son, Junyoung and Lee, Taemin and Hong, Seongtae and Park, JeongBae and Lim, Heuiseok},
  booktitle={Annual Conference on Human and Language Technology},
  pages={129--134},
  year={2025},
  organization={Human and Language Technology}
}