Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/reward-model-deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenAssistant/reward-model-deberta-v3-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-base") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f88d9c907e506061cf43209ae9204aabe8c7a7cc458c37a84c17d9adda03cff
- Size of remote file:
- 3.44 kB
- SHA256:
- 30ca00b0e391e47b7926352fa66f9123794a61f02fe1fcfa905a2af50c701baf
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