Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

nteku1
/
gpt2Reward

Text Classification
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
trl
reward-trainer
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use nteku1/gpt2Reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nteku1/gpt2Reward with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="nteku1/gpt2Reward", device_map="auto")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("nteku1/gpt2Reward")
    model = AutoModelForSequenceClassification.from_pretrained("nteku1/gpt2Reward", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
gpt2Reward / runs
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
nteku1's picture
nteku1
End of training
d3e4901 verified over 1 year ago
  • Nov15_18-08-53_95abacfce220
    End of training over 1 year ago
  • Nov15_18-50-17_95abacfce220
    End of training over 1 year ago
  • Nov15_18-56-07_95abacfce220
    End of training over 1 year ago
  • Nov15_19-03-35_95abacfce220
    End of training over 1 year ago
  • Nov15_19-13-46_95abacfce220
    End of training over 1 year ago