TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
By Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao Xie, Zhuohao Yu, Wayne Xin Zhao, Ji-rong Wen. 2021
30 citations recorded by the source corpus.
In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework. TextBox aims to support a broad set of text generation tasks and models. In our library, we implement 21 text generation models on 9 benchmark datasets, covering the categories of VAE, GAN, and pretrained language models. Meanwhile, our library maintains sufficient modularity and extensibility by properly decomposing the model architecture, inference, and learning process into highly reusable modules, which allows users to easily incorporate new models into our framework. The above features make TextBox especially suitable for researchers and practitioners to quickly reproduce baseline models and develop new models. TextBox is implemented based on PyTorch, and released under Apache License 2.0 at the link https://github.com/RUCAIBox/TextBox.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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Citations in the corpus
- Pre-Trained Language Models for Text Generation: A Survey (2022, background): “TextBox [128] supports 21 text generation models, including several prevalent PLMs, and diverse generation strategies (e.g., top-k , beam search) and evaluation metrics (e.g., BLEU, Distinct).”
- Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Text generation has become one of the most critical and challenging tasks in natural language processing [102, 138, 139].”