turkce-embedding-bge-m3
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ernie-3.0-mini-zh
nghuyong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ernie-3.0-nano-zh
nghuyong基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗EmbeddingGemma-2-GGUF
ngquocvinh☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. EmbeddingGemma 2 is a multilingual, multimodal embedding model from Google DeepMind. It maps text and code, images, video, and audio into a shared 768-dimensional vector space and has an 8,192-token shared context window. The upstream checkpoint has a 270M-parameter text path plus optional vision and audio encoders. This release contains a quantized text backbone and a shared BF16 vision/audio projector. Text, image, and audio inputs returned normalized 768-dimensional vectors in local `llama-server` CPU smoke checks. See the official model card for supported task prefixes, modalities, and input guidance. Every value below comes from local measurements of the locked BF16 GGUF and quantized files; the numbers are not copied from the upstream model card. The fixed task evaluation uses the 1,379-pair test split of MTEB STSBenchmark STS, dataset revision `96943a16ea6a35129e253c659081cb59daf81b30`. It covers 2,552 unique sentences with the `task: sentence similarity | query:` prefix, an 8,192-token context, and the CPU `llama.cpp` runtime at commit `9c2e0e491a822adae1f0b1c831adb4160057d24f`. Spearman and Pearson measure correlation between cosine similarity and human scores. Mean and 5th-percentile cosine measure vector agreement with the BF16 GGUF reference. Higher values indicate stronger task correlation or closer vector agreement; smaller files use less disk. This is a task-specific embedding evaluation, not the full MTEB benchmark. Token-level KLD, next-token Top-1, PPL, and RMS probability deltas do not apply to this embedding model.
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View details ↗mmarco-bert-base-italian-uncased
nickprockThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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View details ↗multi-sentence-BERTino
nickprock基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sentence-bert-base-italian-uncased
nickprock基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗canine-s
nielsr基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗lilt-xlm-roberta-base
nielsrThis model is created by combining the Language-Independent Layout Transformer (LiLT) with XLM-RoBERTa, a multilingual RoBERTa model (trained on 100 languages).
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View details ↗tiny-random-kimi-k2.5
nikita-savelyev-cerebras基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗contriever-sentencetransformer
nishimoto基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗e5-small-v2-onnx
nixiesearchThis is a sentence-transformers model: It maps sentences & paragraphs to a N dimensional dense vector space and can be used for tasks like clustering or semantic search.
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View details ↗multilingual-e5-base-onnx
nixiesearch基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗KoE5
nlpai-labIntroducing 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 ↗KURE-Sparse-base
nlpai-lab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗KURE-Sparse-nano
nlpai-lab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗KURE-v1
nlpai-labIntroducing 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
nlpai-lab**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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View details ↗KURE-v3
nlpai-lab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗eurobert-210m-2e4-128sl-full-ft
nomic-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗modernbert-embed-base
nomic-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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