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") - Inference
- Notebooks
- Google Colab
- Kaggle
A whole conversation instead of just the last query
#2 opened over 2 years ago
by
fractalego
Adding `safetensors` variant of this model
1
#1 opened about 3 years ago
by
SFconvertbot