Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/reward-model-deberta-v3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenAssistant/reward-model-deberta-v3-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-large") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f8b9c7c69fa32a92863c9b97afaae43afb0cb77417059bf5782b101dffdf8ec
- Size of remote file:
- 3.44 kB
- SHA256:
- b3b682c3419f7b6258e04c56902b36cd4b54c4c8ff5d1f32213cbe7a011b6375
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