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
Update model metadata to set pipeline tag to the new `text-ranking` (#2)
Browse files- Update model metadata to set pipeline tag to the new `text-ranking` (5846a972dfc7ebb8f16776716cee792f6992eb6c)
Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>
README.md
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---
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PairRanker used in llm-blender, trained on deberta-v3-large. This is the ranker model used in experiments in LLM-Blender paper,
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pipeline_tag: text-ranking
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---
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PairRanker used in llm-blender, trained on deberta-v3-large. This is the ranker model used in experiments in LLM-Blender paper,
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