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Lajavaness
/
bilingual-embedding-base

Sentence Similarity
sentence-transformers
Safetensors
Transformers
bilingual
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use Lajavaness/bilingual-embedding-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Lajavaness/bilingual-embedding-base with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Lajavaness/bilingual-embedding-base", trust_remote_code=True)
    
    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]
  • Transformers

    How to use Lajavaness/bilingual-embedding-base with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Lajavaness/bilingual-embedding-base", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
bilingual-embedding-base
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  • 2 contributors
History: 20 commits
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dangvantuan
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  • sentence_bert_config.json
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