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Qsevent77
/
jina-embeddings-v3

Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
sentence-similarity
mteb
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use Qsevent77/jina-embeddings-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Qsevent77/jina-embeddings-v3 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Qsevent77/jina-embeddings-v3", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Qsevent77/jina-embeddings-v3", trust_remote_code=True, dtype="auto")
  • sentence-transformers

    How to use Qsevent77/jina-embeddings-v3 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Qsevent77/jina-embeddings-v3", trust_remote_code=True)
    
    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
jina-embeddings-v3
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Qsevent77's picture
Qsevent77
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  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
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  • custom_st.py
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  • model.safetensors
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  • modules.json
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.BFloat16Storage"

    What is a pickle import?

    1.14 GB
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
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