TextBox 2.0: A Text Generation Library with Pre-trained Language Models
This library facilitates research on text generation by providing a unified tool for researchers, but it is incremental as it builds upon existing methods and datasets.
The paper introduces TextBox 2.0, a library for text generation research that integrates 13 tasks, 83 datasets, and 45 pre-trained language models, and demonstrates its effectiveness through extensive experiments.
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.