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cl-nagoya/ruri-v3-30m

Sentence Similarity·cl-nagoya· 226.4K· 10
apache-2.0 Sentence Similarity 36.7M params dataset:cl-nagoya/ruri-v3-dataset-ftarxiv:2409.07737base_model:cl-nagoya/ruri-v3-pt-30mbase_model:finetune:cl-nagoya/ruri-v3-pt-30mlicense:apache-2.0

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 voc

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Model details

Task
Sentence Similarity
Provider
cl-nagoya
Parameters
36.7M
Size
142 MB
License
apache-2.0
Downloads
226.4K
Likes
10
Paper
arXiv:2409.07737
Updated
2025-04-17

About cl-nagoya/ruri-v3-30m

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.

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