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KURE-Sparse-base

Feature Extraction
sentence-transformers
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Korean
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milco
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learned-sparse-retrieval
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metadata 
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - splade
  - learned-sparse-retrieval
  - feature-extraction
language:
  - ko
  - en
base_model: omai-research/milco-650m
pipeline_tag: feature-extraction
library_name: sentence-transformers
license: apache-2.0

πŸ”Ž KURE-Sparse-base

KURE-Sparse-base is a Korean-English bilingual learned sparse retrieval model. It shows state-of-the-art performance among sparse models supporting Korean, averging 0.7984 nDCG@10 on MTEB(kor, v2) Retrieval. It also outperforms 8B dense models, such as Qwen3-Embedding-8B and comsat-embed-ko-8b-preview.

Key Characteristics

  • Learned Sparse Retrieval (LSR): every vector lives in a fixed vocabulary, so documents can be indexed in an inverted index and matched terms are inspectable.
  • Dual view: represented with 30,522 English pivot terms (projected from the multilingual encoder) + 250,002 source-language tokens.
  • Long documents: up to 8,192 tokens.
  • Sparse at both ends: on average 100 active dimensions per query and 1,897 per document.

Usage 

pip install -U sentence-transformers

from sentence_transformers import SparseEncoder

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

query = "ν›ˆλ―Όμ •μŒμ€ μ–Έμ œ λ§Œλ“€μ–΄μ‘Œλ‚˜μš”?"
documents = [
    "μ„Έμ’…λŒ€μ™•μ€ 1443년에 ν›ˆλ―Όμ •μŒμ„ μ°½μ œν•˜κ³  1446년에 이λ₯Ό λ°˜ν¬ν•˜μ˜€λ‹€.",
    "κΉ€μΉ˜λŠ” λ°°μΆ”λ‚˜ 무λ₯Ό μ†ŒκΈˆμ— 절인 λ’€ 고좧가루와 μ “κ°ˆμ„ λ„£μ–΄ λ°œνš¨μ‹œν‚¨ μŒμ‹μ΄λ‹€.",
    "ν•œλΌμ‚°μ€ ν•΄λ°œ 1,947m둜 λ‚¨ν•œμ—μ„œ κ°€μž₯ 높은 산이며 μ œμ£Όλ„ 쀑앙에 μžλ¦¬ν•œλ‹€.",
]

query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# torch.Size([280524]) torch.Size([3, 280524])

# Dot-product scores (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[23.9621,  2.0522,  1.0599]])

Evaluation

KURE-Sparse-base 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 nDCG@10 vs. average FLOPs of sparse retrievers on MTEB(kor, v2) Retrieval

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-base 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-base},
}

@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}
}

@inproceedings{nguyen2026milco,
  title={MILCO: Learned Sparse Retrieval Across Languages via a Multilingual Connector},
  author={Nguyen, Thong and Lei, Yibin and Ju, Jia-Huei and Yang, Eugene and Yates, Andrew},
  booktitle={International Conference on Learning Representations},
  year={2026}
}