“李太白少时，梦所用之笔头上生花后天才赡逸，名闻天下。”——王仁裕《开元天宝遗事·梦笔头生花》
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
TextBox 2.0 is an up-to-date text generation library based on Python and PyTorch focusing on building a unified and standardized pipeline for applying pre-trained language models to text generation:
- From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets.
- From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules).
- From a training perspective, we support 4 pre-training objectives and 4 efficient and robust training strategies, such as distributed data parallel and efficient generation.
Compared with the previous version of TextBox, this extension mainly focuses on building a unified, flexible, and standardized framework for better supporting PLM-based text generation models. There are three advantages of TextBox 2.0:
- It is a significant innovation focusing on comprehensive tasks and PLMs.
- It is designed to be unified in implementation and interface.
- It can faithfully reproduce the results reported in existing work.
The Overall Framework of TextBox 2.0
git clone https://github.com/RUCAIBox/TextBox.git && cd TextBox
bash install.shThis is a script template to run TextBox 2.0 in an end-to-end pipeline:
python run_textbox.py --model=<model-name> --dataset=<dataset-name> --model_path=<hf-or-local-path>Substitute --model=<xxx> , --dataset=<xxx> and --model_path=<xxx> with your choices. See Model, Dataset for a full support list. See Pre-trained Model Parameters for more detail of model_path.
The script below will run the Facebook BART-base model on the samsum dataset:
python run_textbox.py --model=BART --dataset=samsum --model_path=facebook/bart-baseFor basic training, we provide a detailed tutorial (here) for setting commonly used parameters like optimizer, scheduler, validation frequency, early stopping, and so on.
TextBox 2.0 provides four pre-training objectives to help users pre-train a model from scratch, including language modeling, masked sequence-to-sequence modeling, denoising auto-encoding, and masked span prediction. See the pre-training doc for a detailed tutorial.
Four useful training methods are provided for improving the optimization of PLMs: distributed data parallel, efficient decoding, hyper-parameter optimization, and repeated experiments. Detailed instructions are provided here.
To support the rapid progress of PLMs on text generation, TextBox 2.0 incorporates 47 models/modules, covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). See the model doc for information on detailed usage instructions of each model, pre-trained model parameters, and generation parameters.
Now we support 13 generation tasks (e.g., translation and story generation) and their corresponding 83 datasets. We also provide the description, basic statistics, training/validation/testing samples, and leaderboard for each dataset. See more details here.
TextBox 2.0 supports 17 automatic metrics of 4 categories and several visualization tools to explore and analyze the generated texts in various dimensions. For evaluation details, see the evaluation doc.
| Releases | Date | Features |
|---|---|---|
| v2.0.1 | 24/12/2022 | TextBox 2.0 |
| v2.0.0 | 20/08/2022 | TextBox 2.0 Beta |
| v0.2.1 | 15/04/2021 | TextBox |
| v0.1.5 | 01/11/2021 | Basic TextBox |
Please let us know if you encounter a bug or have any suggestions by filing an issue.
We welcome all contributions from bug fixes to new features and extensions.
We expect all contributions discussed in the issue tracker and going through PRs.
We thank @LucasTsui0725 for contributing HRED model and several evaluation metrics.
We thank @wxDai for contributing PointerNet and more than 20 language models in transformers API.
TextBox is developed and maintained by AI Box.
TextBox uses MIT License.
If you find TextBox 2.0 useful for your research or development, please cite the following papers:
@article{textbox2.0,
title = "TextBox 2.0: A Text Generation Library with Pre-trained Language Models",
author = "Tang, Tianyi and Li, Junyi and Chen, Zhipeng and Hu, Yiwen and Yu, Zhuohao and Dai, Wenxun and Dong, Zican and Cheng, Xiaoxue and Wang, Yuhao and Zhao, Wayne Xin and Nie, Jian-Yun and Wen, Ji-Rong",
journal = "arXiv preprint arXiv:2212.13005",
url = "http://arxiv.org/abs/2212.13005",
year = "2022"
}
@inproceedings{textbox,
title = "{T}ext{B}ox: A Unified, Modularized, and Extensible Framework for Text Generation",
author = "Li, Junyi and Tang, Tianyi and He, Gaole and Jiang, Jinhao and Hu, Xiaoxuan and Xie, Puzhao and Chen, Zhipeng and Yu, Zhuohao and Zhao, Wayne Xin and Wen, Ji-Rong",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.4",
doi = "10.18653/v1/2021.acl-demo.4",
pages = "30--39",
}