README.md

metadata

language:
  - ja
tags:
  - sentence-similarity
  - feature-extraction
base_model: sbintuitions/modernbert-ja-70m
widget: []
pipeline_tag: sentence-similarity
license: apache-2.0
datasets:
  - cl-nagoya/ruri-v3-dataset-pt

Ruri: Japanese General Text Embeddings

⚠️Notes: This model is a pretrained version and has not been fine-tuned.
For the fine-tuned version, please use cl-nagoya/ruri-v3-70m!

Fine-tuned Model Series

Ruri v3 is a general-purpose Japanese text embedding model built on top of ModernBERT-Ja. We provide Ruri-v3 in several model sizes. Below is a summary of each model.

ID	#Param.	#Param. 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.

pip install flash-attn --no-build-isolation

Then you can load this model and run inference.

import torch.nn.functional as F
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("cl-nagoya/ruri-v3-pt-70m")

# Ruri v3 employs a 1+3 prefix scheme to distinguish between different types of text inputs:
# "" (empty string) is used for encoding semantic meaning.
# "トピック: " is used for classification, clustering, and encoding topical information.
# "検索クエリ: " is used for queries in retrieval tasks.
# "検索文書: " is used for documents to be retrieved.
sentences = [
    "川べりでサーフボードを持った人たちがいます",
    "サーファーたちが川べりに立っています",
    "トピック: 瑠璃色のサーファー",
    "検索クエリ: 瑠璃色はどんな色？",
    "検索文書: 瑠璃色（るりいろ）は、紫みを帯びた濃い青。名は、半貴石の瑠璃（ラピスラズリ、英: lapis lazuli）による。JIS慣用色名では「こい紫みの青」（略号 dp-pB）と定義している[1][2]。",
]

embeddings = model.encode(sentences, convert_to_tensor=True)
print(embeddings.size())
# [5, 384]

similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
print(similarities)

Citation

@misc{
  Ruri,
  title={{Ruri: Japanese General Text Embeddings}}, 
  author={Hayato Tsukagoshi and Ryohei Sasano},
  year={2024},
  eprint={2409.07737},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2409.07737}, 
}

License

This model is published under the Apache License, Version 2.0.