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mlnotes
/
tape

Feature Extraction
Transformers
PyTorch
bert
Model card Files Files and versions
xet
Community
1

Instructions to use mlnotes/tape with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mlnotes/tape with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="mlnotes/tape")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("mlnotes/tape")
    model = AutoModel.from_pretrained("mlnotes/tape")
  • Notebooks
  • Google Colab
  • Kaggle
tape
370 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
mlnotes's picture
mlnotes
ADD pytorch_model with MLM weights.
2c72f2e almost 4 years ago
  • .gitattributes
    1.18 kB
    initial commit about 4 years ago
  • config.json
    691 Bytes
    add model about 4 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    370 MB
    xet
    ADD pytorch_model with MLM weights. almost 4 years ago
  • special_tokens_map.json
    112 Bytes
    add tokenizer about 4 years ago
  • tokenizer_config.json
    370 Bytes
    add tokenizer about 4 years ago
  • vocab.txt
    81 Bytes
    add tokenizer about 4 years ago