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nateraw
/
codecarbon-text-classification

Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Model card Files Files and versions
xet
Metrics Training metrics Community
5

Instructions to use nateraw/codecarbon-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nateraw/codecarbon-text-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="nateraw/codecarbon-text-classification")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("nateraw/codecarbon-text-classification")
    model = AutoModelForSequenceClassification.from_pretrained("nateraw/codecarbon-text-classification")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Librarian Bot: Add base_model information to model

#5 opened over 2 years ago by
librarian-bot

Adding `safetensors` variant of this model

#4 opened over 2 years ago by
SFconvertbot

Align label mapping with imdb dataset

1
#3 opened almost 4 years ago by
lewtun
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