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colorfulscoop
/
sbert-base-ja

Sentence Similarity
sentence-transformers
PyTorch
Japanese
bert
feature-extraction
Model card Files Files and versions
xet
Community
1

Instructions to use colorfulscoop/sbert-base-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use colorfulscoop/sbert-base-ja with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("colorfulscoop/sbert-base-ja")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
sbert-base-ja
443 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
noriyukipy's picture
noriyukipy
Revise URL link
ecb8a98 almost 5 years ago
  • 1_Pooling
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  • .gitattributes
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  • CHANGELOG.md
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  • README.md
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  • added_tokens.json
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  • config.json
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  • config_sentence_transformers.json
    122 Bytes
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  • modules.json
    229 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (4)

    • "torch.LongStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

    What is a pickle import?

    443 MB
    xet
    Add models and model card almost 5 years ago
  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    156 Bytes
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  • spm.model
    803 kB
    xet
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  • tokenizer_config.json
    502 Bytes
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