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textattack
/
distilbert-base-uncased-WNLI

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

Instructions to use textattack/distilbert-base-uncased-WNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use textattack/distilbert-base-uncased-WNLI with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="textattack/distilbert-base-uncased-WNLI")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-uncased-WNLI")
    model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-uncased-WNLI")
  • Notebooks
  • Google Colab
  • Kaggle
distilbert-base-uncased-WNLI
536 MB
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History: 26 commits
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SFconvertbot
Adding `safetensors` variant of this model
56f86f8 over 2 years ago
  • .gitattributes
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  • README.md
    629 Bytes
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  • config.json
    490 Bytes
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  • eval_results_wnli.txt
    73 Bytes
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  • log.txt
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  • model.safetensors
    268 MB
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  • pytorch_model.bin
    268 MB
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  • special_tokens_map.json
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  • tokenizer_config.json
    48 Bytes
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  • train_args.json
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  • training_args.bin
    1.06 kB
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  • vocab.txt
    232 kB
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