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McGill-NLP
/
tapas-statcan-large-metadata_encoder-title_and_member

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

Instructions to use McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member")
    model = AutoModel.from_pretrained("McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member")
  • Notebooks
  • Google Colab
  • Kaggle
tapas-statcan-large-metadata_encoder-title_and_member
1.35 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
xhluca's picture
xhluca
Upload with huggingface_hub
109b80c about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • config.json
    1.53 kB
    Upload with huggingface_hub about 3 years ago
  • projection.pt

    Detected Pickle imports (3)

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

    What is a pickle import?

    1.05 MB
    xet
    Upload with huggingface_hub about 3 years ago
  • pytorch_model.bin
    1.35 GB
    xet
    Upload with huggingface_hub about 3 years ago