kvkk-pii-ner
Turkish PII (Personally Identifiable Information) detection model for KVKK compliance.
Fine-tuned from dbmdz/bert-base-turkish-cased for token classification.
Model Description
This model detects unstructured PII entities in Turkish text that cannot be caught by deterministic regex patterns. It is designed as Tier 2 of the kvkk-pii hybrid pipeline.
- Base model:
dbmdz/bert-base-turkish-cased(110M parameters) - Task: Token classification (BIO tagging)
- Labels: PERSON, ADDRESS, HEALTH_DATA, KVKK_SPECIAL
- Training data: 14,913 synthetic samples
- Name generation reference: Nisanyan Adlar Sozlugu
Labels
| Label | Description | KVKK Category |
|---|---|---|
| PERSON | Person names | Kimlik (Md. 3) |
| ADDRESS | Physical addresses | Iletisim (Md. 3) |
| HEALTH_DATA | Diagnoses, medications, lab results | Ozel Nitelikli (Md. 6) |
| KVKK_SPECIAL | Religion, ethnicity, political views | Ozel Nitelikli (Md. 6) |
Usage
With kvkk-pii (recommended)
from kvkk_pii import PIIAnalyzer
analyzer = PIIAnalyzer(ner_model_path="ersanbil/kvkk-pii-ner")
results = analyzer.analyze("Hasta: Ahmet Yilmaz, Tani: E11.9 - Tip 2 diabetes mellitus")
Standalone with transformers
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("ersanbil/kvkk-pii-ner")
model = AutoModelForTokenClassification.from_pretrained("ersanbil/kvkk-pii-ner")
model.eval()
text = "Hasta Adi: LEVENT DUMAN"
encoding = tokenizer(text, return_tensors="pt", return_offsets_mapping=True)
with torch.no_grad():
outputs = model(**{k: v for k, v in encoding.items() if k != "offset_mapping"})
predictions = outputs.logits.argmax(dim=-1)[0]
for token_id, pred in zip(encoding["input_ids"][0], predictions):
label = model.config.id2label[pred.item()]
if label != "O":
print(f"{tokenizer.decode([token_id])}: {label}")
Performance
| Entity | Precision | Recall | F1 |
|---|---|---|---|
| PERSON | 1.00 | 1.00 | 1.00 |
| ADDRESS | 1.00 | 1.00 | 1.00 |
| HEALTH_DATA | 0.99 | 1.00 | 0.99 |
| Overall | 99.86% |
5-Fold Cross-Validation: Mean F1 = 98.86% +/- 0.19%
Limitations
- KVKK_SPECIAL category has limited training coverage
- City names may be tagged as PERSON (acceptable from a PII perspective)
- Free-text health data recall is lower than structured forms
- Inference runs on CPU for cross-device consistency (110M model is fast enough)
Training
- Fine-tuned with HuggingFace Transformers
- 10 epochs, batch size 64, learning rate 3e-5, cosine scheduler
- Early stopping with patience 3
License
Apache License 2.0
Author
Ersan Bilik — ersan.ai
Citation
@software{kvkk_pii_ner,
author = {Bilik, Ersan},
title = {kvkk-pii-ner: Turkish PII Detection Model for KVKK Compliance},
year = {2026},
url = {https://huggingface.co/ersanbil/kvkk-pii-ner}
}