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:
- 19240793e113db700694b2f7da661027966c224431e3075863e414b1b9887e2c
- Size of remote file:
- 433 MB
- SHA256:
- 5569d11e5bad1d5aded31c9cfa2b7faca36fca2d867e8d5e971fdb9d6271a8b0
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