  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.
