Instructions to use webis/monoelectra-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Lightning IR
How to use webis/monoelectra-base with Lightning IR:
#install from https://github.com/webis-de/lightning-ir from lightning_ir import CrossEncoderModule model = CrossEncoderModule("webis/monoelectra-base") model.score("query", ["doc1", "doc2", "doc3"]) - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
pipeline_tag: text-ranking
library_name: lightning-ir
base_model:
- google/electra-base-discriminator
tags:
- cross-encoder
This model was introduced in the paper Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking.
For code, examples and more, please visit https://github.com/webis-de/msmarco-llm-distillation.