The objective of Information Extraction (IE) is to derive structured representations from unstructured or semi-structured documents. However, developing IE models is complex due to the need of integrating several subtasks. Additionally, representation of data among varied tasks and transforming datasets into task-specific model inputs presents further challenges. To streamline this undertaking for researchers, we introduce PyTorch-IE, a deep-learning-based framework uniquely designed to enable swift, reproducible, and reusable implementations of IE models. PyTorch-IE offers a flexible data model capable of creating complex data structures by integrating interdependent layers of annotations derived from various data types, like plain text or semi-structured text, and even images. We propose task modules to decouple the concerns of data representation and model-specific representations, thereby fostering greater flexibility and reusability of code. PyTorch-IE also extends support for widely used libraries such as PyTorch-Lightning for training, HuggingFace datasets for dataset reading, and Hydra for experiment configuration. Supplementary libraries and GitHub templates for the easy setup of new projects are also provided. By ensuring functionality and versatility, PyTorch-IE provides vital support to the research community engaged in Information Extraction.

信息抽取（IE）旨在从非结构化或半结构化文档中导出结构化表示。本研究提出了PyTorch-IE，一种基于深度学习的框架，旨在实现IE模型的快速、可重复和可重用的实现。PyTorch-IE提供了灵活的数据模型，能够通过集成来自各种数据类型（如纯文本或半结构化文本以及图像）的相互依赖的注释层来创建复杂的数据结构。通过提供对PyTorch-Lightning用于训练、HuggingFace数据集用于数据集读取和Hydra用于实验配置等广泛使用的库的支持，PyTorch-IE为信息抽取领域的研究社区提供了重要的支持。

PyTorch-IE：信息提取的快速可重现原型设计