Instructions to use Evan-Lin/deberta-reward-r with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evan-Lin/deberta-reward-r with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Evan-Lin/deberta-reward-r")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Evan-Lin/deberta-reward-r") model = AutoModelForSequenceClassification.from_pretrained("Evan-Lin/deberta-reward-r", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e82227590b64bfb875ed5d2643cea142780b48a29b458c831b5b88a8dc89cad7
- Size of remote file:
- 738 MB
- SHA256:
- 170dc432724ba213ace50456fbe06d33faec68dc347fad930487a8f15123b630
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