Instructions to use Eli-Rhm/Reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eli-Rhm/Reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Eli-Rhm/Reward")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Eli-Rhm/Reward") model = AutoModelForSequenceClassification.from_pretrained("Eli-Rhm/Reward") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9f1a4dbb8b37327b08ca1ae1e7d056588b1298a123965c9533b80b3b2f60049
- Size of remote file:
- 439 MB
- SHA256:
- 1b73964fab5c69b5d1aa120e3d2a6f6295df450ab47590de4043889bc6327450
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