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tdm503
/
spcc-finetuned

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1615
loss:TripletLoss
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use tdm503/spcc-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use tdm503/spcc-finetuned with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("tdm503/spcc-finetuned")
    
    sentences = [
        "工事キャンセル日を変更したい",
        "工事予定キャンセルしたため日程変更手続き希望",
        "予定キャンセルした工事日を再調整希望",
        "新規工事日を早めてほしい"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle

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  • 1_Pooling
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  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
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  • model.safetensors
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    xet
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  • modules.json
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  • sentence_bert_config.json
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  • special_tokens_map.json
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  • tokenizer_config.json
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  • vocab.txt
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