| --- |
| 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) |
|
|
