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This is a mirror of japanese-hubert-base, originally released by rinna Co., Ltd. The original model is licensed under the Apache License 2.0. This mirror follows the same license terms. All copyrights remain with the original authors. The model architecture is the same as the original HuBERT Base model, which contains 12 transformer layers with 12 attention heads. The model was trained using code from the official repository, and the detailed training configuration can be found in the same repository and the original paper. The model was trained on approximately 19,000 hours of following Japanese speech corpus ReazonSpeech v1. - ReazonSpeech

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turkish-e5-large

ytu-ce-cosmos

This is a finetune version of model intfloat/multilingual-e5-large-instruct with various Turkish datasets. Recommended Instruct: "Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query" COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department https://cosmos.yildiz.edu.tr/ cosmos@yildiz.edu.tr

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glm-2b

zai-org

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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An independently converted and quantized GGUF of NVIDIA's `nvidia/Nemotron-3-Embed-1B-BF16`, prepared for local embedding inference in LM Studio and llama.cpp-compatible runtimes. This repository is not an official NVIDIA release and is not affiliated with or endorsed by NVIDIA. The model produces 2,048-dimensional, L2-normalized embeddings. Its GGUF metadata declares a 262,144-token maximum context. The release was functionally tested at a 4,096-token context; very large contexts were not validated and require substantially more memory.

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However, SOTA embedding models are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA 4B open-weight multilingual embedding model that outperforms every competitor we benchmarked, and we're launching it here on HuggingFace. This model outperforms `OpenAI text-embedding-large`, `Cohere Embed v4`, `gemini-embedding-001`, and `voyage-4-nano` across finance, healthcare, legal, conversational, manufacturing, code, and STEM. zembed-1 is distilled directly from our SOTA reranker zerank-2 using our zELO methodology, which models relevance scores as adjusted Elo ratings. Standard contrastive training on binary labels can't match this signal. See our blog post for details.

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