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README.md

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+---
+library_name: onnxruntime
+tags:
+  - ner
+  - pii
+  - gliner
+  - onnx
+  - privacy
+  - pdpa
+  - pdpd
+  - pipl
+  - multilingual
+language:
+  - en
+  - ms
+  - ta
+  - zh
+  - id
+  - vi
+  - th
+  - hi
+  - bn
+  - ko
+  - de
+  - fr
+  - ru
+license: agpl-3.0
+pipeline_tag: token-classification
+---
+
+# PII-Engineer-Multi-NER-v2.1
+
+A high-accuracy, multilingual PII detection model for privacy compliance (PDPA, PDPD, PDP Law, PIPL).
+
+Fine-tuned with LoRA on GLiNER2 architecture (mDeBERTa-v3-base encoder, 280M params). Optimized for real-world PII detection across 13+ languages.
+
+**Built by [PII Engineer](https://pii.engineer)**
+
+## Labels (9 PII types)
+
+| Label | Description |
+|-------|-------------|
+| `person_name` | Full names, partial names |
+| `phone_number` | Phone/mobile numbers (international) |
+| `government_id` | NRIC, SSN, Aadhaar, NIK, CCCD, etc. |
+| `street_address` | Physical addresses |
+| `date_of_birth` | Birth dates in any format |
+| `email_address` | Email addresses |
+| `passport_number` | Passport numbers (multi-country) |
+| `license_plate` | Vehicle license plates |
+| `bank_account_number` | Bank account/routing numbers |
+
+## Performance
+
+| Label | Precision | Recall | F1 |
+|-------|-----------|--------|----|
+| person_name | 0.808 | 0.838 | 0.823 |
+| phone_number | 0.962 | 0.975 | 0.968 |
+| government_id | 0.902 | 0.938 | 0.920 |
+| street_address | 0.903 | 0.891 | 0.897 |
+| date_of_birth | 0.901 | 0.901 | 0.901 |
+| email_address | 0.974 | 0.966 | 0.970 |
+| passport_number | 0.808 | 0.812 | 0.810 |
+| license_plate | 0.837 | 0.847 | 0.842 |
+| bank_account_number | 0.879 | 0.906 | 0.892 |
+| **Mean** | | | **0.902** |
+
+## Architecture
+
+- **Encoder:** mDeBERTa-v3-base (768 hidden, 12 layers, 12 heads)
+- **Framework:** GLiNER2 span-based NER (5 ONNX models)
+- **Parameters:** ~280M
+- **Inference:** ONNX Runtime (CPU or GPU)
+
+### ONNX Models
+
+| File | Size | Description |
+|------|------|-------------|
+| encoder.onnx | 1.1GB | Token encoder (FP32) |
+| encoder_int8.onnx | 511MB | Token encoder (INT8 quantized) |
+| span_rep.onnx | 63MB | Span representation |
+| count_embed.onnx | 41MB | Count embedding |
+| count_pred.onnx | 4.6MB | Count prediction |
+| classifier.onnx | 4.5MB | Classification head |
+
+## Quick Start
+
+Use with [pii.engineer](https://github.com/gantz-ai/pii.engineer) (Rust server with auto-download):
+
+```bash
+cargo build --release --package pii-engineer-server
+cargo run --release --package pii-engineer-server
+# Models download automatically on first run
+```
+
+Or download manually:
+
+```bash
+huggingface-cli download pii-engineer/PII-Engineer-Multi-NER-v2.1 --local-dir models/ner-v21
+```
+
+## Supported Languages
+
+**Primary:** English, Malay, Tamil, Chinese, Indonesian, Vietnamese
+
+**Secondary:** Thai, Hindi, Bengali, Korean, German, French, Russian
+
+## Use Cases
+
+- PDPA (Singapore) compliance scanning
+- PDPD (Vietnam) compliance scanning
+- PDP Law (Indonesia) compliance scanning
+- PIPL (China) compliance scanning
+- PII detection in documents, chat logs, databases
+- Pre-processing for data anonymization pipelines
+
+## Limitations
+
+- Optimized for structured/semi-structured text (forms, emails, documents)
+- May underperform on highly informal social media text
+- Date-of-birth detection requires contextual birth cues (e.g., "born", "DOB", "lahir")
+
+## Citation
+
+```bibtex
+@misc{pii-engineer-multi-ner-v2.1,
+  title={PII-Engineer-Multi-NER-v2.1: Multilingual PII Detection Model},
+  author={PII Engineer},
+  year={2026},
+  url={https://pii.engineer},
+  note={Fine-tuned on GLiNER2 architecture with LoRA}
+}
+```
+
+## License
+
+AGPL-3.0 — free for open-source use. Commercial license available at [pii.engineer](https://pii.engineer).
+
+Built on [gliner2-multi-v1](https://huggingface.co/fastino/gliner2-multi-v1) (Apache 2.0) and [mDeBERTa-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) (MIT).