MODEL PROFILE · 模型档案

gliner-pii-large-v1.0

knowledgator · gliner-pii-large-v1.0

A production-grade open-source model for privacy-focused PII, PHI, and PCI detection with zero-shot entity recognition capabilities. This model was developed in collaboration between Wordcab and Knowledgator. For enterprise-ready, specialized PII/PHI/PCI models, contact us at info@wordcab.com. GLiNER (Generalist and Lightweight Named Entity Recognition) is a bidirectional transformer model that can identify **any entity type** without predefined categories. Unlike traditional NER models that are limited to specific entity classes, GLiNER allows you to specify exactly what entities you want to extract at runtime. - **Zero-shot recognition**: Extract any entity type without retraining - **Privacy-first**: Process sensitive data locally without API calls - **Lightweight**: Much faster than large language models for NER tasks - **Production-ready**: Quantization-aware training with FP16 and UINT8 ONNX models - **Comprehensive**: 60+ predefined PII categories with custom entity support

译文排队中,当前展示来源原文。

文本分析license:apache-2.0
上下文窗口来源未披露
输入 → 输出文本 → 文本
模型权重待核验
档案更新时间2026/11/2 19:15:42
OVERVIEW

模型介绍

A production-grade open-source model for privacy-focused PII, PHI, and PCI detection with zero-shot entity recognition capabilities. This model was developed in collaboration between Wordcab and Knowledgator. For enterprise-ready, specialized PII/PHI/PCI models, contact us at info@wordcab.com. GLiNER (Generalist and Lightweight Named Entity Recognition) is a bidirectional transformer model that can identify **any entity type** without predefined categories. Unlike traditional NER models that are limited to specific entity classes, GLiNER allows you to specify exactly what entities you want to extract at runtime. - **Zero-shot recognition**: Extract any entity type without retraining - **Privacy-first**: Process sensitive data locally without API calls - **Lightweight**: Much faster than large language models for NER tasks - **Production-ready**: Quantization-aware training with FP16 and UINT8 ONNX models - **Comprehensive**: 60+ predefined PII categories with custom entity support

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。