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 on a fine-tuned 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 diverse IE tasks with substantial improvements in deployment accessibility compared to LLM-based alternatives. We release GLiNER2 as an open-source library available through pip, complete with pre-trained models and comprehensive documentation."
Nurture the relationships that matter most ✨
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3 марта Европейское агентство по делам беженцев назвало Иран одним из потенциальных очагов напряженности и указало на неопределенность рисков после ударов США и Израиля по ядерным объектам исламской республики. Ведомство подчеркнуло, что отток из страны даже 10 процентов населения станет причиной одного из крупнейших миграционных кризисов за последние десятилетия.
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Tested on real merge commits from major open-source repositories. For each merge commit, we replay the merge with both Git and Weave, then compare against the human-authored result.
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