Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-large-v2 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-v2 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-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-large-v2") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-large-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c1d7478e3da1b255391ddee76f20c77d8f1af71f67b89c5e6aff3d65f891758
- Size of remote file:
- 3.45 kB
- SHA256:
- 0d8452e2c6c64cd2471e4702d51827323359a32c28c3f3f978302c19ad5ca032
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