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hz53
/
bert-base-uncased-rte

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

Instructions to use hz53/bert-base-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use hz53/bert-base-uncased-rte with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="hz53/bert-base-uncased-rte")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("hz53/bert-base-uncased-rte")
    model = AutoModelForSequenceClassification.from_pretrained("hz53/bert-base-uncased-rte")
  • Notebooks
  • Google Colab
  • Kaggle
bert-base-uncased-rte
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  • 1 contributor
History: 3 commits
hz53's picture
hz53
Upload BertForSequenceClassification
6d090fa about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • config.json
    727 Bytes
    Upload BertForSequenceClassification about 3 years ago
  • pytorch_model.bin
    438 MB
    xet
    Upload BertForSequenceClassification about 3 years ago