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English(EN) GLiNER2-PII: A Multilingual Model for Personally Identifiable Information Extraction

新的GLiNER2-PII模型在多语言PII提取方面表现出色

研究人员开发了GLiNER2-PII,一个紧凑的0.3十亿参数模型,用于多语言个人身份信息(PII)提取。该模型改编自GLiNER2,能够识别42种不同类型的PII,并达到字符跨度级别。为了克服数据稀缺和隐私问题,使用了一个受约束的生成管道创建了一个合成多语言语料库。与包括OpenAI的Privacy Filter在内的其他系统相比,GLiNER2-PII在SPY基准测试中表现出优越的性能,并且已在Hugging Face上发布。 AI

影响 这个新模型提供了改进的多语言PII检测能力,有可能增强各种应用中的数据隐私和安全性。

排序理由 该集群描述了一篇新的研究论文,详细介绍了一种新颖的PII提取模型,包括其方法、性能和公开发布。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的GLiNER2-PII模型在多语言PII提取方面表现出色

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该集群描述了一篇新的研究论文,详细介绍了一种新颖的PII提取模型,包括其方法、性能和公开发布。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · George Hurn-Maloney ·

    GLiNER2-PII:用于个人身份信息提取的多语言模型

    Reliable detection of personally identifiable information (PII) is increasingly important across modern data-processing systems, yet the task remains difficult: PII spans are heterogeneous, locale-dependent, context-sensitive, and often embedded in noisy or semi-structured docume…