Token Classification
GLiNER
PyTorch
English
nvidia
PII
PHI
GLiNER
information extraction
entity recognition
privacy
Instructions to use nvidia/gliner-PII with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use nvidia/gliner-PII with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("nvidia/gliner-PII") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
|
Download EXPLAINABILITY.md from nvidia/gliner-PII: direct link, hf CLI and curl.
- Browser
- Download file 2.9 kB
-
https://huggingface.co/nvidia/gliner-PII/resolve/refs%2Fpr%2F2/EXPLAINABILITY.md
- Command line
-
hf download hf://nvidia/gliner-PII@refs/pr/2/EXPLAINABILITY.md
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curl -L -o EXPLAINABILITY.md https://huggingface.co/nvidia/gliner-PII/resolve/refs%2Fpr%2F2/EXPLAINABILITY.md
2.9 kB
| 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/) | |