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CSI-lab
/
Washington-state-law-embedding-model-Large

Sentence Similarity
sentence-transformers
Safetensors
English
bert
legal
law
WA
feature-extraction
dense
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use CSI-lab/Washington-state-law-embedding-model-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use CSI-lab/Washington-state-law-embedding-model-Large with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("CSI-lab/Washington-state-law-embedding-model-Large")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
Washington-state-law-embedding-model-Large
1.34 GB
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  • 1 contributor
History: 3 commits
CSI-lab's picture
CSI-lab
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f92ee8b verified about 1 month ago
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  • .gitattributes
    1.52 kB
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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
    1.34 GB
    xet
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  • modules.json
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  • sentence_bert_config.json
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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
    1.27 kB
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  • vocab.txt
    232 kB
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