ruri v3 70m EU AI Act Compliance Profile

Ruri: Japanese General Text Embeddings

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.

Model Series

We provide Ruri-v3 in several model sizes. Below is a summary of each model.

|ID| #Param. | #Param.<br>w/o Emb.|Dim.|#Layers|Avg. JMTEB| |-|-|-|-|-|-| |cl-nagoya/ruri-v3-30m|37M|10M|256|10|74.51| |cl-nagoya/ruri-v3-70m|70M|31M|384|13|75.48| |cl-nagoya/ruri-v3-130m|132M|80M|512|19|76.55| |cl-nagoya/ruri-v3-310m|315M|236M|768|25|77.24|

Usage

You can use our models directly with the transformers library v4.48.0 or higher:

pip install -U "transformers>=4.48.0" sentence-transformers

Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2.

Then you can load this model and run inference.

import torch
import torch.nn.functional as F
from sentence_trans

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Base Model