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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abd83352626df0b2e2e812ae87e4fc3cbb3fbeeb89e9ba83ffd7497ed5be83e5
- Size of remote file:
- 559 Bytes
- SHA256:
- 2349a271c10b7c9d605f61c613cb980d22d19b8cdd9f5c88d8eab60988c322bc
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