The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet the demand, we present \our, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. Concretely, PyABSA integrates 29 models and 26 datasets. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexibly extended to considered models, datasets, and other related tasks. Besides, PyABSA highlights its data augmentation and annotation features, which significantly address data scarcity. All are welcome to have a try at \url{https://github.com/yangheng95/PyABSA}.

翻译：方面级情感分析（ABSA）的进步催生了对用户友好型框架的需求，该框架需能大幅降低复现最先进ABSA性能的难度（尤其对新手而言）。为满足这一需求，我们提出PyABSA——基于PyTorch构建的可复现ABSA模块化框架。为促进ABSA研究，PyABSA支持包括方面术语抽取、方面情感分类及端到端方面级情感分析在内的多项ABSA子任务。具体而言，PyABSA集成了29个模型与26个数据集，仅需数行代码即可复现模型在特定数据集上的结果。其模块化设计使得框架可灵活扩展至其他考虑中的模型、数据集及相关任务。此外，PyABSA突出其数据增强与标注功能，显著缓解了数据稀缺问题。诚邀各位试用：\url{https://github.com/yangheng95/PyABSA}。