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razent
/
cotext-2-cc

Feature Extraction
Transformers
PyTorch
google-tensorflow TensorFlow
JAX
code
t5
Model card Files Files and versions
xet
Community
1

Instructions to use razent/cotext-2-cc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use razent/cotext-2-cc with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="razent/cotext-2-cc")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("razent/cotext-2-cc")
    model = AutoModel.from_pretrained("razent/cotext-2-cc")
  • Notebooks
  • Google Colab
  • Kaggle
cotext-2-cc
2.68 GB
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  • 2 contributors
History: 4 commits
razent's picture
razent
Update README.md
c44d027 about 4 years ago
  • .gitattributes
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  • README.md
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  • config.json
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  • flax_model.msgpack
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  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    892 MB
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  • spiece.model
    792 kB
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  • tf_model.h5
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  • tokenizer.json
    1.39 MB
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