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DChak2000
/
reward-model

Text Classification
Transformers
Safetensors
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use DChak2000/reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use DChak2000/reward-model with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="DChak2000/reward-model")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("DChak2000/reward-model")
    model = AutoModelForSequenceClassification.from_pretrained("DChak2000/reward-model")
  • Notebooks
  • Google Colab
  • Kaggle
reward-model
439 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
DChak2000's picture
DChak2000
Upload tokenizer
939b692 verified 4 months ago
  • .gitattributes
    1.52 kB
    initial commit 4 months ago
  • README.md
    5.17 kB
    Upload BertForSequenceClassification 4 months ago
  • config.json
    843 Bytes
    Upload BertForSequenceClassification 4 months ago
  • model.safetensors
    438 MB
    xet
    Upload BertForSequenceClassification 4 months ago
  • tokenizer.json
    712 kB
    Upload tokenizer 4 months ago
  • tokenizer_config.json
    322 Bytes
    Upload tokenizer 4 months ago