ASReview Dory: Bringing new and exciting models to ASReview LAB

Systematic reviewing is a time-consuming process which can be accelerated through screening prioritisation via active learning. ASReview Dory enables researchers to test, validate, and apply a wide range of embedders and classifiers in systematic literature screening. It extends ASReview LAB, an open source, lightweight, and user-friendly environment with proven default models and extensibility through Python entry points. ASReview Dory adds ready-to-use transformer-based embedders, neural classifiers, and a framework for integrating custom models. Once installed, these models are directly available in ASReview LAB without additional configuration and can be systematically evaluated using the API or ASReview Makita. • Introduces ASReview Dory, extending ASReview LAB with advanced NLP models. • Adds transformer-based embedders and neural classifiers to active learning. • Provides a documented interface for integrating and comparing custom models. • Allows immediate use of advanced models without complex setup. • Enables reproducible large-scale simulations and benchmarking of advanced NLP models.