The practical value of modular design
Rankify's architecture design fully considers the needs of research scenarios, and uses modular components to achieve functional decoupling and flexible configuration. This design idea enables researchers to quickly build the experimental process.
- Functional modularity: data, retrievers, models are relatively independent and can be installed in combination as needed (e.g., separate retriever or reranking modules)
- Experimental flexibility: Support for customized dataset access, allowing replacement of algorithmic implementations in any processing session
- Ease of Deployment: Provide pre-built indexes (Wikipedia/MS MARCO) and Hugging Face dataset interfaces to avoid duplication of infrastructure work
In typical cases, researchers can quickly compare the performance differences of different reordering models while keeping the retriever unchanged; developers can also easily replace the LLM model of the generator module. This design significantly shortens the experimental cycle, and can improve the research efficiency by about 40% based on real-world data.
This answer comes from the articleRankify: a Python toolkit supporting information retrieval and reorderingThe
May not be reproduced without permission:AI productivity tools " Rankify's Modular Design Significantly Improves Research Efficiency
