YATO: Yet Another deep learning based Text analysis Open toolkit

TL;DR

YATO is a lightweight, user-friendly open-source toolkit for deep learning-based text analysis that supports flexible combinations of neural features, facilitating rapid model development and cross-disciplinary NLP applications.

Contribution

It introduces a hierarchical, easily configurable toolkit that integrates traditional neural networks, pre-trained language models, and custom features for versatile NLP research.

Findings

Supports fast reproduction and refinement of NLP models

Enables flexible combination of multiple feature types

Promotes cross-disciplinary NLP applications

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.