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://www.youtube.com/playlist?list=PLJ0mhzMcRuDUlTkzBfAftOqiJRxYTTjXH.

翻译：我们介绍YATO，一个面向深度学习文本分析的开源、易用工具包。与现有高度工程化的工具包和平台不同，YATO轻量级且对跨学科领域研究者友好。通过层次化结构设计，YATO支持三类广泛使用特征的自由组合：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自定义神经网络特征。凭借灵活性与易用性优势，YATO可促进最新NLP模型的快速复现与优化，并推动NLP技术的跨学科应用。代码、示例及文档已在https://github.com/jiesutd/YATO 公开。演示视频可通过https://www.youtube.com/playlist?list=PLJ0mhzMcRuDUlTkzBfAftOqiJRxYTTjXH 获取。