Field                                                                                                  |  Response
:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
Intended Task/Domain:                                                                                  |  PII/PHI Detection: To detect and classify Personally Identifiable Information (PII) and Protected Health Information (PHI) in structured and unstructured text across domains like healthcare, finance, and legal.
Model Type:                                                                                            |  Transformer (GLiNER architecture).
Intended Users:                                                                                        |  Developers and data professionals implementing data governance, privacy compliance (GDPR, HIPAA), and content moderation workflows.
Output:                                                                                                |  A list of dictionaries, where each dictionary contains the detected text, its label (e.g., SSN), start and end positions, and a confidence score.
Describe how the model works:                                                                          |  The model takes a text string as input and uses a non-generative transformer architecture to produce span-level entity annotations. It identifies and labels sensitive information across 55+ categories without generating new text.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:  |  Not Applicable
Technical Limitations & Mitigation:                                                                    |  Limitation: Performance varies by domain, text format, and the confidence threshold chosen. Mitigation: NVIDIA recommends use-case-specific validation and human review for high-stakes deployments to ensure accuracy and safety.   
Verified to have met prescribed NVIDIA quality standards:  |  Yes
Performance Metrics:                                                                                   |  Strict F1 Score is the primary evaluation metric. The model also provides per-entity confidence scores in its output.
Potential Known Risks:                                                                                 |  If the model does not work as intended, it could lead to false negatives (failing to detect PII) or false positives (incorrectly flagging non-sensitive data, causing unnecessary redaction).
Licensing:                                                                                             |  Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)