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cl-nagoya
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ruri-pt-base

Sentence Similarity
sentence-transformers
Safetensors
Japanese
bert
feature-extraction
Inference Endpoints
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metadata
language:
  - ja
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
base_model: tohoku-nlp/bert-base-japanese-v3
widget: []
pipeline_tag: sentence-similarity
license: apache-2.0
datasets:
  - cl-nagoya/ruri-dataset-ft

Ruri: Japanese General Text Embeddings

Usage

First install the Sentence Transformers library:

pip install -U sentence-transformers

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-pt-base")

# Don't forget to add the prefix "クエリ: " for query-side or "文章: " for passage-side texts.
sentences = [
    "クエリ: 瑠璃色はどんな色？",
    "文章: 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]。",
    "クエリ: ワシやタカのように、鋭いくちばしと爪を持った大型の鳥類を総称して「何類」というでしょう?",
    "文章: ワシ、タカ、ハゲワシ、ハヤブサ、コンドル、フクロウが代表的である。これらの猛禽類はリンネ前後の時代(17~18世紀)には鷲類・鷹類・隼類及び梟類に分類された。ちなみにリンネは狩りをする鳥を単一の目(もく)にまとめ、vultur(コンドル、ハゲワシ)、falco(ワシ、タカ、ハヤブサなど)、strix(フクロウ)、lanius(モズ)の4属を含めている。",
]

embeddings = model.encode(sentences, convert_to_tensor=True)
print(embeddings.size())
# [4, 768]

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

Benchmarks

JMTEB

Evaluated with JMTEB.

Model #Param. Avg. Retrieval STS Classfification Reranking Clustering PairClassification
cl-nagoya/sup-simcse-ja-base 111M 68.56 49.64 82.05 73.47 91.83 51.79 62.57
cl-nagoya/sup-simcse-ja-large 337M 66.51 37.62 83.18 73.73 91.48 50.56 62.51
cl-nagoya/unsup-simcse-ja-base 111M 65.07 40.23 78.72 73.07 91.16 44.77 62.44
cl-nagoya/unsup-simcse-ja-large 337M 66.27 40.53 80.56 74.66 90.95 48.41 62.49
pkshatech/GLuCoSE-base-ja 133M 70.44 59.02 78.71 76.82 91.90 49.78 66.39
sentence-transformers/LaBSE 472M 64.70 40.12 76.56 72.66 91.63 44.88 62.33
intfloat/multilingual-e5-small 118M 69.52 67.27 80.07 67.62 93.03 46.91 62.19
intfloat/multilingual-e5-base 278M 70.12 68.21 79.84 69.30 92.85 48.26 62.26
intfloat/multilingual-e5-large 560M 71.65 70.98 79.70 72.89 92.96 51.24 62.15
OpenAI/text-embedding-ada-002 - 69.48 64.38 79.02 69.75 93.04 48.30 62.40
OpenAI/text-embedding-3-small - 70.86 66.39 79.46 73.06 92.92 51.06 62.27
OpenAI/text-embedding-3-large - 73.97 74.48 82.52 77.58 93.58 53.32 62.35
Ruri-Small 68M 71.53 69.41 82.79 76.22 93.00 51.19 62.11
Ruri-Base 111M 71.91 69.82 82.87 75.58 92.91 54.16 62.38
Ruri-Large 337M 73.31 73.02 83.13 77.43 92.99 51.82 62.29

Model Details

Model Description

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Training Details

Framework Versions

  • Python: 3.10.13
  • Sentence Transformers: 3.0.0
  • Transformers: 4.41.2
  • PyTorch: 2.3.1+cu118
  • Accelerate: 0.30.1
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

License

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