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fbaigt
/
procbert

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

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

  • Libraries
  • Transformers

    How to use fbaigt/procbert with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="fbaigt/procbert")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("fbaigt/procbert")
    model = AutoModel.from_pretrained("fbaigt/procbert")
  • Notebooks
  • Google Colab
  • Kaggle
procbert
437 MB
Ctrl+K
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  • 2 contributors
History: 10 commits
Fan Bai
Update model card
20814e1 over 4 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • README.md
    1.17 kB
    Update model card over 4 years ago
  • config.json
    427 Bytes
    Add model weights and configuation over 4 years ago
  • pytorch_model.bin
    436 MB
    xet
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  • special_tokens_map.json
    156 Bytes
    Add model weights and configuation over 4 years ago
  • tokenizer_config.json
    2 Bytes
    Add model weights and configuation over 4 years ago
  • vocab.txt
    220 kB
    Add model weights and configuation over 4 years ago