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license: apache-2.0
base_model: urchade/gliner_small-v2.1
library_name: gliner
pipeline_tag: token-classification
tags:
- gliner
- token-classification
- pii
- ner
---
# pii-proxy
A GLiNER model fine-tuned for PII detection. GLiNER does zero-shot NER over
arbitrary labels — you pass the entity types you care about at inference time,
so there is no fixed label schema to work around.
Fine-tuned from `urchade/gliner_small-v2.1` on the full Nemotron-PII dataset (~100k).
## Results
Held-out test set (100 examples), 24 fine-grained PII labels:
| Metric | F1 |
|---|---|
| Fine-grained | 92.8% |
| Coarse-grained | 94.4% |
NVIDIA's Nemotron-PII reference: fine 96.2%, coarse 96.7%.
## Usage
```python
from gliner import GLiNER
model = GLiNER.from_pretrained("daslabhq/pii-proxy")
labels = ["first_name", "last_name", "email", "phone_number", "ssn", "street_address"]
entities = model.predict_entities("Patient Marcus Weber, marcus.weber@gmail.com", labels)
for e in entities:
print(e["text"], "->", e["label"])
```
## Training
- Base model: `urchade/gliner_small-v2.1`
- Epochs: 5, batch size 16
- Train examples: 98215
- Focal loss (alpha 0.75, gamma 2)
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