New GLiNER2-PII model excels at multilingual PII extraction

Researchers have developed GLiNER2-PII, a compact 0.3 billion parameter model designed for multilingual personally identifiable information (PII) extraction. This model, adapted from GLiNER2, can identify 42 different types of PII at the character-span level. To overcome data scarcity and privacy concerns, a synthetic multilingual corpus was created using a constraint-driven generation pipeline. GLiNER2-PII demonstrated superior performance on the SPY benchmark compared to other systems, including OpenAI's Privacy Filter, and has been released on Hugging Face. AI

影响 This new model offers improved multilingual PII detection, potentially enhancing data privacy and security in various applications.

排序理由 The cluster describes a new research paper detailing a novel model for PII extraction, including its methodology, performance, and public release. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• GLiNER2
• GLiNER2-PII
• Hugging Face
• OpenAI
• Personally Identifiable Information
• SPY benchmark

• 论文
• 模型发布
• 安全

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