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.
@article{arxiv.2212.13005,
title = {TextBox 2.0: A Text Generation Library with Pre-trained Language Models},
author = {Tianyi Tang and Junyi Li and Zhipeng Chen and Yiwen Hu and Zhuohao Yu and Wenxun Dai and Zican Dong and Xiaoxue Cheng and Yuhao Wang and Wayne Xin Zhao and Jian-Yun Nie and Ji-Rong Wen},
journal= {arXiv preprint arXiv:2212.13005},
year = {2022}
}