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substrate-labs
/
jina-onnx

Fill-Mask
Transformers
ONNX
bert
custom_code
Model card Files Files and versions
xet
Community

Instructions to use substrate-labs/jina-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use substrate-labs/jina-onnx with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("fill-mask", model="substrate-labs/jina-onnx", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMaskedLM
    
    tokenizer = AutoTokenizer.from_pretrained("substrate-labs/jina-onnx", trust_remote_code=True)
    model = AutoModelForMaskedLM.from_pretrained("substrate-labs/jina-onnx", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
jina-onnx
275 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
rob-substrate's picture
rob-substrate
upload engine
dd87bed about 2 years ago
  • .gitattributes
    57 Bytes
    upload engine about 2 years ago
  • config.json
    1.18 kB
    upload engine about 2 years ago
  • model_optimized.onnx
    274 MB
    xet
    upload engine about 2 years ago
  • ort_config.json
    1.27 kB
    upload engine about 2 years ago
  • special_tokens_map.json
    695 Bytes
    upload engine about 2 years ago
  • tokenizer.json
    712 kB
    upload engine about 2 years ago
  • tokenizer_config.json
    1.25 kB
    upload engine about 2 years ago
  • vocab.txt
    232 kB
    upload engine about 2 years ago