🔎 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.

Model	Params	Avg	AutoRAG	PubHealthQA	Ko-StrategyQA	LawIRKo	SQuADKorV1	Belebele (ko-ko)	MrTidy	MLDR	MIRACL
Our Models
nlpai-lab/KURE-Sparse-base	0.6B	0.7984	0.9445	0.8099	0.8188	0.7244	0.9815	0.9703	0.6255	0.5716	0.7395
nlpai-lab/KURE-Sparse-nano	0.1B	0.7716	0.9360	0.7711	0.7924	0.7226	0.9774	0.9608	0.5618	0.5133	0.7091
Sparse Models
omai-research/milco-650m	0.6B	0.7824	0.9483	0.8219	0.8143	0.6724	0.9700	0.9623	0.6997	0.4318	0.7209
telepix/PIXIE-Splade-v1.5	0.1B	0.7608	0.9412	0.7910	0.7993	0.7667	0.9687	0.9651	0.5132	0.4938	0.6085
yjoonjang/splade-ko-v1	0.1B	0.7526	0.9423	0.7977	0.7583	0.6509	0.9754	0.9576	0.5299	0.5162	0.6451
telepix/PIXIE-Splade-v1.0	0.1B	0.6636	0.9597	0.7860	0.7352	0.5330	0.9417	0.9413	0.2909	0.3991	0.3858
opensearch-project/opensearch-neural-sparse-encoding-multilingual-v1	0.2B	0.5444	0.6482	0.5506	0.5553	0.3554	0.7770	0.7321	0.5526	0.1219	0.6064
Dense (Single-Vector) Models
nvidia/Nemotron-3-Embed-8B-BF16	8.0B	0.8127	0.9311	0.8898	0.8405	0.8481	0.9650	0.9714	0.6540	0.4785	0.7363
sionic-ai/comsat-embed-ko-8b-preview	7.6B	0.7927	0.8518	0.8871	0.8394	0.8164	0.9168	0.9853	0.6253	0.5157	0.6964
Qwen/Qwen3-Embedding-8B	7.6B	0.7826	0.8276	0.8721	0.8363	0.8171	0.9063	0.9824	0.6187	0.5046	0.6783
nvidia/Nemotron-3-Embed-1B-BF16	1.1B	0.7817	0.9074	0.8642	0.8136	0.7758	0.9505	0.9629	0.6084	0.4202	0.7325
Qwen/Qwen3-Embedding-4B	4.0B	0.7737	0.8431	0.8693	0.8270	0.7769	0.9044	0.9522	0.6076	0.5022	0.6803
perplexity-ai/pplx-embed-v1-4b	4.0B	0.7705	0.8321	0.9042	0.8326	0.7736	0.9402	0.9624	0.5778	0.4343	0.6775
microsoft/harrier-oss-v1-27b	27.0B	0.7667	0.8176	0.8971	0.8361	0.8737	0.9204	0.9546	0.5306	0.4046	0.6653
dragonkue/snowflake-arctic-embed-l-v2.0-ko	0.6B	0.7653	0.9093	0.8337	0.8050	0.7735	0.9447	0.9518	0.5712	0.4304	0.6685
codefuse-ai/F2LLM-v2-8B	7.6B	0.7638	0.7678	0.9380	0.8371	0.8405	0.8874	0.9513	0.6162	0.4047	0.6313
telepix/PIXIE-Rune-v1.5	0.6B	0.7618	0.8927	0.8426	0.8064	0.7705	0.9457	0.9617	0.5492	0.4482	0.6393
nlpai-lab/KURE-v1	0.6B	0.7616	0.8708	0.8193	0.7999	0.7426	0.9357	0.9502	0.5909	0.4637	0.6816
nvidia/llama-nemotron-embed-1b-v2	1.2B	0.7564	0.8708	0.8177	0.8055	0.7329	0.9345	0.9519	0.5988	0.3952	0.7004
dragonkue/BGE-m3-ko	0.6B	0.7547	0.8738	0.8155	0.7959	0.7322	0.9414	0.9503	0.6099	0.3899	0.6833
BAAI/bge-m3	0.6B	0.7509	0.8301	0.8041	0.7941	0.7174	0.9038	0.9315	0.6471	0.4287	0.7015
perplexity-ai/pplx-embed-v1-0.6b	0.6B	0.7390	0.8414	0.8255	0.8100	0.7444	0.9174	0.9393	0.5524	0.3179	0.7026
nlpai-lab/KoE5	0.6B	0.7337	0.8434	0.8351	0.8001	0.7756	0.8980	0.9425	0.5841	0.3015	0.6235
Qwen/Qwen3-Embedding-0.6B	0.6B	0.7140	0.8338	0.8107	0.7586	0.7526	0.8490	0.9124	0.4870	0.4199	0.6023

Performance-Sparsity

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

Model	Params	Query dims	Document dims	FLOPs
KURE-Sparse-base	0.6B	100	1,897	36.5
KURE-Sparse-nano	0.1B	147	843	23.0
omai-research/milco-650m	0.6B	105	1,167	32.3
telepix/PIXIE-Splade-v1.5	0.1B	117	1,541	37.3
yjoonjang/splade-ko-v1	0.1B	109	742	29.1
telepix/PIXIE-Splade-v1.0	0.1B	121	625	22.4
opensearch-project/opensearch-neural-sparse-encoding-multilingual-v1	0.2B	29	176	6.8

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.

Model	Params	BEIR-13	FLOPs
Our Models
KURE-Sparse-base	0.6B	0.5864	55.8
KURE-Sparse-nano	0.1B	0.5317	37.0
Sparse Models
Linkup-Platform/linkup-sparseup-embed-v1	0.2B	0.5645	8.5
omai-research/milco-650m	0.6B	0.5545	20.6
telepix/PIXIE-Splade-v1.5	0.1B	0.4149	125.6

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}
}