ruri-base
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探索 AI 模型 15
ruri-base-v2
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查看详情 ↗ruri-large
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查看详情 ↗ruri-reranker-base
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查看详情 ↗ruri-reranker-large
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查看详情 ↗ruri-reranker-small
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查看详情 ↗ruri-small
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查看详情 ↗ruri-v3-130m
cl-nagoya**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-130m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 512 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737
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查看详情 ↗ruri-v3-30m
cl-nagoya**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-30m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 256 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737
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查看详情 ↗ruri-v3-310m
cl-nagoya**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-310m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗ruri-v3-70m
cl-nagoya**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-70m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 384 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737
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查看详情 ↗ruri-v3-reranker-310m
cl-nagoya**Ruri-v3 Reranker** is a general-purpose Japanese reranker model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** with good robustness for vaious domains - **Supports sequence lengths up to 8192 tokens** - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - **Integrated FlashAttention**, following ModernBERT's architecture - **Tokenizer based solely on SentencePiece** Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-310m - **Maximum Sequence Length:** 8192 tokens - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗sup-simcse-ja-base
cl-nagoya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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查看详情 ↗sup-simcse-ja-large
cl-nagoya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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。