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wesleymorris
/
summary-roberta-content

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

Instructions to use wesleymorris/summary-roberta-content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use wesleymorris/summary-roberta-content with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="wesleymorris/summary-roberta-content")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("wesleymorris/summary-roberta-content")
    model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/summary-roberta-content", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
summary-roberta-content
999 MB
Ctrl+K
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  • 2 contributors
History: 3 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
8d403c1 over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    28 Bytes
    initial commit over 3 years ago
  • config.json
    796 Bytes
    Upload 7 files over 3 years ago
  • merges.txt
    456 kB
    Upload 7 files over 3 years ago
  • model.safetensors
    499 MB
    xet
    Adding `safetensors` variant of this model over 3 years ago
  • pytorch_model.bin
    499 MB
    xet
    Upload 7 files over 3 years ago
  • special_tokens_map.json
    957 Bytes
    Upload 7 files over 3 years ago
  • tokenizer_config.json
    1.34 kB
    Upload 7 files over 3 years ago
  • training_args.bin
    3.39 kB
    xet
    Upload 7 files over 3 years ago
  • vocab.json
    999 kB
    Upload 7 files over 3 years ago