YATO: Yet Another deep learning based Text analysis Open toolkit

Zeqiang Wang, Yile Wang, Jiageng Wu, Zhiyang Teng, Jie Yang

Abstract: 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. A demo video is also available at https://youtu.be/tSjjf5BzfQg.