  Progress in natural language processing research is catalyzed by the
possibilities given by the widespread software frameworks. This paper
introduces Adaptor library that transposes the traditional model-centric
approach composed of pre-training + fine-tuning steps to objective-centric
approach, composing the training process by applications of selected
objectives. We survey research directions that can benefit from enhanced
objective-centric experimentation in multitask training, custom objectives
development, dynamic training curricula, or domain adaptation. Adaptor aims to
ease reproducibility of these research directions in practice. Finally, we
demonstrate the practical applicability of Adaptor in selected unsupervised
domain adaptation scenarios.
