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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d0d5687b7ff6ea4f462ee79a49c4a51ca412bb64d430cd603e969a77e01457c
- Size of remote file:
- 3.82 kB
- SHA256:
- e3acd68d4fc46ad568d4bba0a878036b85755818a7179a426ec308b4d1d9c2df
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