Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

Lajavaness
/
bilingual-embedding-large

Sentence Similarity
sentence-transformers
Safetensors
Transformers
French
English
bilingual
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community
2

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

  • Libraries
  • sentence-transformers

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

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Lajavaness/bilingual-embedding-large", trust_remote_code=True)
    
    sentences = [
        "C'est une personne heureuse",
        "C'est un chien heureux",
        "C'est une personne très heureuse",
        "Aujourd'hui est une journée ensoleillée"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Transformers

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

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Lajavaness/bilingual-embedding-large", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
bilingual-embedding-large / 1_Pooling
594 Bytes
Ctrl+K
Ctrl+K
  • 2 contributors
History: 1 commit
tuan.ljn
Initial commit
5ed2d99 almost 2 years ago
  • .ipynb_checkpoints
    Initial commit almost 2 years ago
  • config.json
    297 Bytes
    Initial commit almost 2 years ago