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laura2243
/
bert-sota

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:44114
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use laura2243/bert-sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use laura2243/bert-sota with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("laura2243/bert-sota")
    
    sentences = [
        "The city is located in 1889 , along the Nehalem River and Nehalem Bay , near the Pacific Ocean .",
        "Incorporated in 1889 , the city lies along the Pacific Ocean near the Nehalem River and Nehalem Bay .",
        "Along the coast there are almost 2,000 islands , about three quarters of which are uninhabited .",
        "The mammalian fauna of Madagascar is largely endemic and highly distinctive ."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
bert-sota
439 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
laura2243's picture
laura2243
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4d05b80 verified 4 months ago
  • 1_Pooling
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  • eval
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  • .gitattributes
    1.52 kB
    initial commit 4 months ago
  • README.md
    18.3 kB
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  • config.json
    612 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • model.safetensors
    438 MB
    xet
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  • modules.json
    229 Bytes
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  • sentence_bert_config.json
    56 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.41 kB
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  • vocab.txt
    232 kB
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