---
license: mit
language:
- tr
library_name: onnx
pipeline_tag: token-classification
base_model: akdeniz27/bert-base-turkish-cased-ner
tags:
- pii
- ner
- token-classification
- bert
- turkish
- onnx
- int8
- onnxruntime
---

# pii-ner-model

Dynamic-INT8 **ONNX** export of [`akdeniz27/bert-base-turkish-cased-ner`](https://huggingface.co/akdeniz27/bert-base-turkish-cased-ner)
(BERTurk, MIT). It detects free-text PII — **names and addresses** — that a deterministic
regex masker can't catch, and runs **in-process via `onnxruntime` (no torch)**.

Freya's voice agent loads it for freeform-PII redaction (`src/privacy/ner.py`,
`LocalPiiDetector`); the agent image fetches this repo at build into `PII_NER_MODEL_DIR`.
NER is optional + fail-open and gated per-agent by `privacy_config.mask_pii`.

## Files

| file | what |
|------|------|
| `model.int8.onnx` | dynamic-INT8-quantized BERTurk token-classification model (~106 MB) |
| `tokenizer.json` | Rust-tokenizer config for the `onnxruntime` path |
| `config.json` | `id2label` map for decode |
| `export_model.py` | the offline recipe that produced the artifacts (not used at runtime) |

## Labels

7-class BIO: `O`, `B-PER`/`I-PER`, `B-ORG`/`I-ORG`, `B-LOC`/`I-LOC`. Downstream mapping:
`PER -> NAME`, `LOC -> ADDRESS`; `ORG` is dropped.

## Quality

Validated on Turkish: names F1 ~1.00 (cased) / ~0.93–0.95 (ASR-style lowercase). INT8 is
effectively lossless vs fp32 on cased text. Addresses (`LOC`) are weaker on lowercase ASR text.

## Regenerating

Needs torch + `optimum[onnxruntime]` (not runtime deps):

```bash
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install "optimum[onnxruntime]" transformers
python export_model.py --model akdeniz27/bert-base-turkish-cased-ner --out /tmp/pii-ner
# then copy model_quantized.onnx -> model.int8.onnx, plus tokenizer.json + config.json
```

## License

MIT — same as the base model. See `LICENSE`. Base model: `akdeniz27/bert-base-turkish-cased-ner`.