GLiNER2: An Efficient Multi-Task Information Extraction System with
  Schema-Driven Interface
Authors: name: Urchade Zaratiana, name: Gil Pasternak, name: Oliver Boyd, name: George Hurn-Maloney, name: Ash Lewis

Information extraction (IE) is fundamental to numerous NLP applications, yet
existing solutions often require specialized models for different tasks or rely
on computationally expensive large language models. We present GLiNER2, a
unified framework that enhances the original GLiNER architecture to support
named entity recognition, text classification, and hierarchical structured data
extraction within a single efficient model. Built pretrained transformer
encoder architecture, GLiNER2 maintains CPU efficiency and compact size while
introducing multi-task composition through an intuitive schema-based interface.
Our experiments demonstrate competitive performance across extraction and
classification tasks with substantial improvements in deployment accessibility
compared to LLM-based alternatives. We release GLiNER2 as an open-source
pip-installable library with pre-trained models and documentation at
https://github.com/fastino-ai/GLiNER2.