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:
- a6d6fce611d680ccfcae5d868fc1adde80784df8092e197cca52dc8b90016813
- Size of remote file:
- 1.79 GB
- SHA256:
- 3b82e8cfba027b8a2f1b7f93f15887527c2aacb103fdebdd230a83073cfe72fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.