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."
Зеленский заявил о запросе от США на участие Киева в ситуации на Ближнем Востоке20:47
，详情可参考旺商聊官方下载
Зеленский заявил о запросе от США на участие Киева в ситуации на Ближнем Востоке20:47
There is a lot of energy right now around sandboxing untrusted code. AI agents generating and executing code, multi-tenant platforms running customer scripts, RL training pipelines evaluating model outputs—basically, you have code you did not write, and you need to run it without letting it compromise the host, other tenants, or itself in unexpected ways.
Global news & analysis