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arXiv · 2209.13877

YATO: Yet Another deep learning based Text analysis Open toolkit

Zeqiang Wang·Yile Wang·Jiageng Wu·Zhiyang Teng·Jie Yang
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Abstract

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.

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Zeqiang Wang, Yile Wang, Jiageng Wu, Zhiyang Teng, Jie Yang. 2022-09-28. YATO: Yet Another deep learning based Text analysis Open toolkit. https://arxiv.org/abs/2209.13877

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