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:
- 6fc518ba5f7cc2eb5c6cce9cc3b072f8747400f9846fbe7f1ab2997bed86cf5c
- Size of remote file:
- 559 Bytes
- SHA256:
- ea848812c4d01e9c7b1b400921d7f207693ac100d4c729e572925c6e111b123f
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