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intfloat
/
e5-large-v2

Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community
26

Instructions to use intfloat/e5-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use intfloat/e5-large-v2 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("intfloat/e5-large-v2")
    
    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]
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
e5-large-v2
1.34 GB
Ctrl+K
Ctrl+K
  • 4 contributors
History: 5 commits
Michael Feil
add sbert config
50c3a32 almost 3 years ago
  • 1_Pooling
    add sbert config almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit almost 3 years ago
  • README.md
    65.6 kB
    Update README.md almost 3 years ago
  • config.json
    616 Bytes
    upload model weights almost 3 years ago
  • modules.json
    387 Bytes
    add sbert config almost 3 years ago
  • pytorch_model.bin
    1.34 GB
    xet
    upload model weights almost 3 years ago
  • sentence_bert_config.json
    57 Bytes
    add sbert config almost 3 years ago
  • special_tokens_map.json
    125 Bytes
    upload model weights almost 3 years ago
  • tokenizer.json
    711 kB
    upload model weights almost 3 years ago
  • tokenizer_config.json
    314 Bytes
    upload model weights almost 3 years ago
  • vocab.txt
    232 kB
    upload model weights almost 3 years ago