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
Published: 2025-07-24 16:11:14

Abstract: 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.