Instructions to use webis/set-encoder-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Lightning IR
How to use webis/set-encoder-large with Lightning IR:
#install from https://github.com/webis-de/lightning-ir from lightning_ir import CrossEncoderModule model = CrossEncoderModule("webis/set-encoder-large") model.score("query", ["doc1", "doc2", "doc3"]) - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, library name, link to paper and Github repo
#1
by nielsr HF Staff - opened
This PR improves the model card by adding:
- a link to the paper Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
- the pipeline tag, ensuring people can find it at https://huggingface.co/models?pipeline_tag=feature-extraction
- the library_name
- a link to the Github repository.
christopher changed pull request status to merged