  To facilitate research on text generation, this paper presents a
comprehensive and unified library, TextBox 2.0, focusing on the use of
pre-trained language models (PLMs). To be comprehensive, our library covers
$13$ common text generation tasks and their corresponding $83$ datasets and
further incorporates $45$ PLMs covering general, translation, Chinese,
dialogue, controllable, distilled, prompting, and lightweight PLMs. We also
implement $4$ efficient training strategies and provide $4$ generation
objectives for pre-training new PLMs from scratch. To be unified, we design the
interfaces to support the entire research pipeline (from data loading to
training and evaluation), ensuring that each step can be fulfilled in a unified
way. Despite the rich functionality, it is easy to use our library, either
through the friendly Python API or command line. To validate the effectiveness
of our library, we conduct extensive experiments and exemplify four types of
research scenarios. The project is released at the link:
https://github.com/RUCAIBox/TextBox.
