We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight and user-friendly for researchers from cross-disciplinary areas. Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT, RoBERTa, ELECTRA, etc.); and 3) user-customized neural features via a simple configurable file. Benefiting from the advantages of flexibility and ease of use, YATO can facilitate fast reproduction and refinement of state-of-the-art NLP models, and promote the cross-disciplinary applications of NLP techniques. The code, examples, and documentation are publicly available at https://github.com/jiesutd/YATO. A demo video is also available at https://youtu.be/tSjjf5BzfQg.

翻译：我们介绍了YATO，一个开源、易于使用的深度学习文本分析工具包。与现有高度工程化的工具包和平台不同，YATO设计轻量且对跨学科研究人员友好。通过层次化结构设计，YATO支持三种广泛使用的特征的灵活组合，包括：1）传统神经网络（CNN、RNN等）；2）预训练语言模型（BERT、RoBERTa、ELECTRA等）；3）通过简单配置文件实现的用户自定义神经特征。得益于灵活性与易用性的优势，YATO能够促进最先进自然语言处理模型的快速复现与改进，并推动自然语言处理技术的跨学科应用。相关代码、示例和文档已公开于https://github.com/jiesutd/YATO，演示视频见https://youtu.be/tSjjf5BzfQg。