.. FairDiverse documentation master file. .. title:: FairDiverse v0.0.1 ========================================================= `HomePage `_ | Introduction ------------------------- FairDiverse is a unified, comprehensive and efficient benchmark toolkit for fairnes-aware and diversity-aware IR models. It aims to help the researchers to reproduce and develop IR models. In the lastest release, our library includes 30+ algorithms covering four major categories: - Pre-processing models - In-processing models - Post-processing models - IR base models We design a unified pipelines. .. image:: C:/lab/P-fairness_project/img/pipeline.png :width: 600 :align: center For the usage, we use following steps: .. image:: C:/lab/P-fairness_project/img/usage.png :width: 600 :align: center The utilized parameters in each config files can be found in following docs: Parameter Descriptions ------------------------- .. toctree:: :maxdepth: 2 :caption: Recommendation Parameters parameters/recommendation/data_preprocessing parameters/recommendation/evaluation parameters/recommendation/new_dataset parameters/recommendation/In-processing parameters/recommendation/Post-processing .. toctree:: :maxdepth: 2 :caption: Search Parameters parameters/search/new_dataset parameters/search/data_preprocessing parameters/search/Pre-processing parameters/search/Post-processing Custom Your Models (APIs) ---------------------------- For the develop your own recommendation model, you can use following steps: .. image:: C:/lab/P-fairness_project/img/rec_develop_steps.png :width: 600 :align: center .. toctree:: :maxdepth: 2 :caption: Recommendation develop APIs APIs/recommendation/recommendation.reranker APIs/recommendation/recommendation.trainer APIs/recommendation/recommendation.rank_model.Abstract_Ranker APIs/recommendation/recommendation.rerank_model.Abstract_Reranker .. toctree:: :maxdepth: 2 :caption: Recommendation other APIs APIs/recommendation/recommendation.evaluator APIs/recommendation/recommendation.llm_rec APIs/recommendation/recommendation.metric APIs/recommendation/recommendation.process_data APIs/recommendation/recommendation.sampler APIs/recommendation/recommendation.utils.group_utils .. toctree:: :maxdepth: 4 :caption: Search develop APIs APIs/search/search.trainer_preprocessing_ranker APIs/search/search.preprocessing_model.fair_model APIs/search/search.postprocessing_model.base APIs/search/search.trainer.base .. toctree:: :maxdepth: 4 :caption: Search other APIs APIs/search/search.fairness_evaluator APIs/search/search.preprocessing_model.utils APIs/search/search.ranker_model.ranker APIs/search/search.ranklib_ranker APIs/search/search.utils.loss APIs/search/search.utils.process_dataset APIs/search/search.utils.utils APIs/search/search.llm_model.api_llm APIs/search/search.utils.div_type The Team ------------------ FairDiverse is developed and maintained by `RUC, UvA`. Here is the list of our lead developers in each development phase. ====================== =============== ============================================= Time Version Lead Developers ====================== =============== ============================================= Nov. 2024 ~ Feb. 2025 v0.0.1 `Chen Xu `_, `Zhirui Deng `_, `Clara Rus `_ ====================== =============== ============================================= License ------------ FairDiverse uses `MIT License `_.