Text Classification
Transformers
PyTorch
Safetensors
English
bert
reward model
alignment
preference model
RLHF
text-embeddings-inference
Instructions to use nicholasKluge/RewardModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicholasKluge/RewardModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicholasKluge/RewardModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/RewardModel") model = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/RewardModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12df06db78e0d53ac8e1c766e0e629ab0a1acb21b3a13d7a44da9c447b473aa5
- Size of remote file:
- 867 MB
- SHA256:
- 1b3d650a3141b2e0c2326bf226ade42125fe25070c17c0181b629ef58e54cd18
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