ruri-base
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ruri-base-v2
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View details ↗ruri-large
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View details ↗ruri-large-v2
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View details ↗ruri-reranker-base
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View details ↗ruri-reranker-large
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View details ↗ruri-reranker-small
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View details ↗ruri-small
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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗sup-simcse-ja-large
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