We introduce YATO, an open-source toolkit for text analysis with deep learning. It focuses on fundamental sequence labeling and sequence classification tasks on text. Designed in a hierarchical structure, YATO supports free combinations of three types of features including 1) traditional neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT, RoBERTa, ELECTRA, etc.); and 3) user-customed neural features via a simple configurable file. Benefiting from the advantages of flexibility and ease of use, YATO can facilitate reproducing and refinement of state-of-the-art NLP models, and promote the cross-disciplinary applications of NLP techniques. Source code, examples, and documentation are publicly available at https://github.com/jiesutd/YATO.
翻译:我们引入了YATO,这是一个开放源码工具,用于进行深层学习的文本分析,重点是基本顺序标签和文本的顺序分类任务,按照等级结构设计,YATO支持三种特征的免费组合,包括:1)传统神经网络(CNN、RNN等);2)预先培训的语言模型(BERT、ROBERTA、ELECTRA等);3)通过简单的可配置文档,用户定制的神经特征。从灵活和易于使用的优势中受益,YATO可以促进最新NLP模型的复制和完善,促进NLP技术的跨学科应用。源代码、示例和文件可在https://github.com/jiesutd/YATO上公开查阅。