MODEL PROFILE

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

Text analysislicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/1 12:01:44
OVERVIEW

Model overview

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

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

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。