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1024m
/
SMM4H-Task6-BartL-B20

Text Classification
Transformers
Safetensors
bart
Model card Files Files and versions
xet
Community

Instructions to use 1024m/SMM4H-Task6-BartL-B20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use 1024m/SMM4H-Task6-BartL-B20 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="1024m/SMM4H-Task6-BartL-B20")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("1024m/SMM4H-Task6-BartL-B20")
    model = AutoModelForSequenceClassification.from_pretrained("1024m/SMM4H-Task6-BartL-B20")
  • Notebooks
  • Google Colab
  • Kaggle
SMM4H-Task6-BartL-B20
1.63 GB
Ctrl+K
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  • 1 contributor
History: 3 commits
1024m's picture
1024m
Create README.md
fe7b272 verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    5.23 kB
    Create README.md about 2 years ago
  • config.json
    1.73 kB
    Upload folder using huggingface_hub about 2 years ago
  • merges.txt
    456 kB
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  • model.safetensors
    1.63 GB
    xet
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  • special_tokens_map.json
    279 Bytes
    Upload folder using huggingface_hub about 2 years ago
  • tokenizer.json
    2.11 MB
    Upload folder using huggingface_hub about 2 years ago
  • tokenizer_config.json
    1.21 kB
    Upload folder using huggingface_hub about 2 years ago
  • vocab.json
    798 kB
    Upload folder using huggingface_hub about 2 years ago