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cern6959
/
intfloat_multilingual-e5-base

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

Instructions to use cern6959/intfloat_multilingual-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use cern6959/intfloat_multilingual-e5-base with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("cern6959/intfloat_multilingual-e5-base")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
intfloat_multilingual-e5-base
5.32 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
cern6959's picture
cern6959
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  • 1_Pooling
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  • onnx
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  • openvino
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  • .gitattributes
    1.58 kB
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  • README.md
    179 kB
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  • config.json
    694 Bytes
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  • model.safetensors
    1.11 GB
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  • modules.json
    387 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    1.11 GB
    xet
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  • sentence_bert_config.json
    57 Bytes
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  • sentencepiece.bpe.model
    5.07 MB
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  • special_tokens_map.json
    280 Bytes
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  • tokenizer.json
    17.1 MB
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  • tokenizer_config.json
    418 Bytes
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