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:
- 6726b8c776bb25472668aa6eb75498c3952bc57e17e27460461d16fabef0a68d
- Size of remote file:
- 4.09 kB
- SHA256:
- e31258ce773b88b8066de7b3863038689fcaaa25ca7099e45b4283397681f1e0
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