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intfloat
/
e5-base-unsupervised

Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
bert
Sentence Transformers
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • sentence-transformers

    How to use intfloat/e5-base-unsupervised with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("intfloat/e5-base-unsupervised")
    
    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
e5-base-unsupervised
877 MB
Ctrl+K
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  • 2 contributors
History: 8 commits
intfloat's picture
intfloat
add sentence similarity pipeline tag
6003a5b almost 3 years ago
  • 1_Pooling
    add sentence-transformers support almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    5.09 kB
    add sentence similarity pipeline tag almost 3 years ago
  • config.json
    700 Bytes
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  • model.safetensors
    438 MB
    xet
    Adding `safetensors` variant of this model (#1) almost 3 years ago
  • modules.json
    387 Bytes
    add sentence-transformers support almost 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "torch.LongStorage",
    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict"

    What is a pickle import?

    438 MB
    xet
    upload models over 3 years ago
  • sentence_bert_config.json
    57 Bytes
    add sentence-transformers support almost 3 years ago
  • special_tokens_map.json
    112 Bytes
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  • tokenizer.json
    466 kB
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  • tokenizer_config.json
    363 Bytes
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  • vocab.txt
    232 kB
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