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", device_map="auto")# 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3083c708b360a6b53da021232b9c1ed0ad805bf10ed00653fac6c259e930608
- Size of remote file:
- 3.48 GB
- SHA256:
- 9ae362c80bd91e9e73c9aac411565ddadb46ac62ac86d0790b0a5b0319472fef
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