Text Ranking
Transformers
PyTorch
Safetensors
deberta
pair-ranker
pair_ranker
reward_model
reward-model
RLHF
Instructions to use llm-blender/pair-ranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-blender/pair-ranker with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llm-blender/pair-ranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ceef70ee5c068f2e8ec2ea787d1566d2f5846b612f0e0a3879edffa246dc91cf
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
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