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GLiNER PII is a successor to the Gretel GLiNER PII/PHI models. Built on the GLiNER bi-large base, it detects and classifies a broad range of Personally Identifiable Information (PII) and Protected Health Information (PHI) in structured and unstructured text. It is non-generative and produces span-level entity annotations with confidence scores across 55+ categories. This model was developed by NVIDIA.
This model is ready for commercial/non-commercial use.
Governing Terms: This trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information: Apache License, Version 2.0.
Global
GLiNER PII supports detection and redaction of sensitive information across regulated and enterprise scenarios.
Note: performance varies by domain, format, and threshold, so validation and human review are recommended for high‑stakes deployments.
Build.NVIDIA.com 03/03/2026 via link
Hugging Face 10/28/2025 via link
Architecture Type: Transformer
Network Architecture: GLiNER
This model was developed based on knowledgator/gliner-bi-large-v1.0
Number of model parameters: 5.7 × 10^8
Input Type(s): Text
Input Format: UTF-8 string(s)
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: supports structured and unstructured text
Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: List of dictionaries with keys {text, label, start, end, score}
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
Preferred/Supported Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Link: nvidia/nemotron-pii
Data Modality: Text
Text Training Data Size: ~100k records (~10^5, <1B tokens)
Data Collection Method: Synthetic
Labeling Method: Synthetic
Properties: Synthetic persona-grounded dataset generated with NVIDIA NeMo Data Designer, spanning 50+ industries and 55+ entity types (U.S. and international formats). Includes both structured and unstructured records. Labels automatically injected during generation.
Data Collection Method: Hybrid: Automated, Human
Labeling Method: Hybrid: Automated, Human
Evaluation Results
From the combined evaluation across Argilla, AI4Privacy, and Gretel PII datasets:
| Benchmark | GLiNER V1 (Strict F1) | GLiNER V2 (Strict F1) |
|---|---|---|
| Argilla PII | 0.64 | 0.70 |
| AI4Privacy | 0.60 | 0.64 |
| nvidia dataset | 0.66 | 0.87 |
We evaluated the model using threshold=0.3.
Acceleration Engine: PyTorch (via Hugging Face Transformers)
Test Hardware: NVIDIA A100 (Ampere, PCIe/SXM)
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
For more detailed information on ethical considerations for this model, please see the the Bias, Explainability, Safety & Security, and Privacy Subcards.
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