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UVA-MSBA
/
M4T8

Text Classification
Transformers
PyTorch
deberta
Model card Files Files and versions
xet
Community
1

Instructions to use UVA-MSBA/M4T8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use UVA-MSBA/M4T8 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="UVA-MSBA/M4T8")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("UVA-MSBA/M4T8")
    model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/M4T8")
  • Notebooks
  • Google Colab
  • Kaggle
M4T8
1.12 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 19 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
abbfa14 verified over 1 year ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • README.md
    1.05 kB
    Update README.md about 3 years ago
  • config.json
    787 Bytes
    Update config.json about 3 years ago
  • model.safetensors
    557 MB
    xet
    Adding `safetensors` variant of this model over 1 year ago
  • pytorch_model.bin
    557 MB
    xet
    Upload pytorch_model.bin about 3 years ago
  • special_tokens_map.json
    124 Bytes
    Create special_tokens_map.json about 3 years ago
  • tokenizer.json
    669 kB
    Create tokenizer.json about 3 years ago
  • tokenizer_config.json
    1.39 kB
    Update tokenizer_config.json about 3 years ago
  • vocab.txt
    798 kB
    Update vocab.txt about 3 years ago