Introducing KoE5, a model with advanced retrieval abilities. It has shown remarkable performance in Korean text retrieval. This is the model card of a 🤗 transformers model that has been pushed on the Hub. - **Developed by:** NLP&AI Lab - **Language(s) (NLP):** Korean, English - **License:** MIT - **Finetuned from model:** intfloat/multilingual-e5-large - **Finetuned dataset:** ko-triplet-v1.0
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View details ↗Introducing Korea University Retrieval Embedding model, KURE-v1 It has shown remarkable performance in Korean text retrieval, speficially overwhelming most multilingual embedding models. To our knowledge, It is one of the best publicly opened Korean retrieval models. This is the model card of a 🤗 transformers model that has been pushed on the Hub. - **Developed by:** NLP&AI Lab - **Language(s) (NLP):** Korean, English - **License:** MIT - **Finetuned from model:** BAAI/bge-m3
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View details ↗**KURE-v2** is a Korean-English bilingual late-interaction (multi-vector) retrieval model built on skt/A.X-Encoder-base. It encodes every token into a 128-dimensional vector and scores query–document pairs with MaxSim. At **154M parameters** it averages nDCG@10 **0.8160** across the nine MTEB(kor, v2) retrieval tasks — the strongest model on that suite, above every single-vector model measured, including one 175× its size. It is trained in two stages: unsupervised contrastive pretraining on 20.7M unlabeled pairs (KURE-v2-unsupervised), then supervised fine-tuning with contrastive loss and KL distillation from a reranker.
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