GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface

• URL: http://arxiv.org/abs/2507.18546v1
• Date: Thu, 24 Jul 2025 16:11:14 GMT
• Title: GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
• Authors: Urchade Zaratiana, Gil Pasternak, Oliver Boyd, George Hurn-Maloney, Ash Lewis,
• Abstract summary: We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction.<n>Our experiments demonstrate competitive performance across extraction and classification tasks with substantial improvements in deployment accessibility.
• Score: 0.873811641236639
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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