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Detomo
/
cl-nagoya-sup-simcse-ja-nss-v1_0_9_1

Sentence Similarity
sentence-transformers
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:366717
loss:CategoricalContrastiveLoss
Model card Files Files and versions
xet
Community
1

Instructions to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_9_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_9_1 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_9_1")
    
    sentences = [
        "科目:コンクリート。名称:免震BPL下部充填コンクリート打設手間。",
        "科目:コンクリート。名称:#F/#FLコンクリート打設手間。",
        "科目:コンクリート。名称:擁壁部コンクリート打設手間。",
        "科目:タイル。名称:EXP_J上床磁器質タイルA。"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
cl-nagoya-sup-simcse-ja-nss-v1_0_9_1
1.52 kB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 1 commit
dtm-hoinv's picture
dtm-hoinv
initial commit
dd0fc3a verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago