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 this https URL.
Abstract (translated)
信息提取（IE）是众多自然语言处理应用程序的基础，然而现有的解决方案往往需要为不同的任务构建专用模型，或者依赖于计算成本高昂的大规模语言模型。我们提出了GLiNER2，这是一个统一的框架，它增强了最初的GLiNER架构，以便在一个高效的单一模型中支持命名实体识别、文本分类和分层结构化数据提取。基于预先训练的变压器编码器架构，GLiNER2在保持CPU效率和紧凑体积的同时，通过直观的模式基础接口引入了多任务组合功能。我们的实验展示了它在信息提取和分类任务上的竞争力性能，并且与基于大模型的方法相比，在部署便利性方面有了显著改进。我们以开源形式发布了GLiNER2，并将其作为一个可pip安装的库发布，其中包括预训练模型和文档，访问地址为[此处提供的URL]。
URL
https://arxiv.org/abs/2507.18546
