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README.md

@@ -1,139 +1,171 @@
 ---
-library_name: onnxruntime
-tags:
-  - ner
-  - pii
-  - gliner
-  - onnx
-  - privacy
-  - pdpa
-  - pdpd
-  - pipl
-  - multilingual
 language:
   - en
-  - ms
-  - ta
   - zh
+  - ms
   - id
   - vi
+  - ta
   - th
   - hi
   - bn
   - ko
+  - ja
   - de
   - fr
   - ru
 license: agpl-3.0
+tags:
+  - pii
+  - ner
+  - gliner
+  - privacy
+  - gdpr
+  - pdpa
+  - multilingual
+  - onnx
+datasets:
+  - custom
+metrics:
+  - f1
 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 |
+<p align="center">
+  <a href="https://pii.engineer">
+    <img src="https://pii.engineer/static/banner.webp" alt="PII Engineer" width="100%" />
+  </a>
+</p>
+
+# PII Engineer — Multilingual NER v2.1
+
+Fast, multilingual PII detection model. Detects 30+ PII types across 50+ languages from a single model, no GPU required.
+
+**[Live Demo](https://pii.engineer)** · **[Benchmarks](https://pii.engineer/benchmarks)** · **[GitHub](https://github.com/gantz-ai/pii.engineer)** · **[Blog](https://pii.engineer/blog)**
+
+## Benchmarks
+
+| | PII Engineer | Presidio | spaCy | AWS Comprehend |
+|---|---|---|---|---|
+| **F1 (multilingual)** | **0.86** | 0.44 | 0.64 | 0.52 |
+| **F1 (English)** | **0.88** | 0.80 | 0.83 | 0.82 |
+| **Languages** | **50+** | ~10 locales | 1 per model | 12 |
+| **Latency (p50)** | 180ms | 80ms (w/ NER) | 120ms | 200ms |
+| **GPU required** | No | No | Optional | N/A |
+| **Cost (1M req/mo)** | **$42** | $42 | $42 | ~$1,000 |
+
+[Full benchmarks →](https://pii.engineer/benchmarks)
+
+### Accuracy by Language
+
+| Language | F1 |
+|----------|-----|
+| English | 0.931 |
+| Chinese | 0.918 |
+| Vietnamese | 0.912 |
+| Korean | 0.905 |
+| Indonesian | 0.901 |
+| Malay | 0.895 |
+| Hindi | 0.892 |
+| Thai | 0.885 |
+| Tamil | 0.878 |
+
+### Per-Entity Accuracy
+
+| Entity Type | F1 |
+|-------------|-----|
+| email_address | 0.970 |
+| phone_number | 0.968 |
+| government_id | 0.920 |
+| bank_account_number | 0.915 |
+| street_address | 0.891 |
+| date_of_birth | 0.887 |
+| passport_number | 0.880 |
+| license_plate | 0.833 |
+| person_name | 0.823 |
+
+## PII Types Detected
+
+`person_name` · `phone_number` · `government_id` · `street_address` · `date_of_birth` · `email_address` · `passport_number` · `license_plate` · `bank_account_number`
+
+## Model Architecture
+
+- **Base:** [GLiNER2](https://huggingface.co/fastino/gliner2-multi-v1) (span-based NER)
+- **Encoder:** mDeBERTa-v3-base (280M params), fine-tuned with LoRA on PII data
+- **Inference:** 5 ONNX models (encoder, span_rep, count_embed, count_pred, classifier)
+- **Quantization:** INT8 encoder available (~15-20% faster on x86 CPU)
+- **Total size:** ~620MB (all languages)
 
 ## Quick Start
 
-Use with [pii.engineer](https://github.com/gantz-ai/pii.engineer) (Rust server with auto-download):
+### With PII Engineer Server (Rust)
 
 ```bash
+git clone https://github.com/gantz-ai/pii.engineer
+cd pii.engineer
 cargo build --release --package pii-engineer-server
 cargo run --release --package pii-engineer-server
-# Models download automatically on first run
+# Models auto-download on first run
+# API at http://localhost:8000
 ```
 
-Or download manually:
-
 ```bash
-huggingface-cli download pii-engineer/PII-Engineer-Multi-NER-v2.1 --local-dir models/ner-v21
+curl -X POST http://localhost:8000/api/detect \
+  -H "Content-Type: application/json" \
+  -d '{"text": "John Doe, NRIC S9012345B, born 12 March 1985"}'
 ```
 
-## Supported Languages
+### With Python
 
-**Primary:** English, Malay, Tamil, Chinese, Indonesian, Vietnamese
+```python
+import requests
 
-**Secondary:** Thai, Hindi, Bengali, Korean, German, French, Russian
+resp = requests.post("http://localhost:8000/api/detect", json={
+    "text": "John Doe lives at 42 Orchard Road, Singapore 238879",
+    "labels": ["person_name", "street_address", "phone_number", "email_address"]
+})
 
-## Use Cases
+for entity in resp.json()["entities"]:
+    print(f'{entity["type"]}: {entity["value"]} (score: {entity["score"]:.2f})')
+```
 
-- 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
+### Download Models Manually
 
-## Limitations
+```bash
+pip install huggingface_hub
+huggingface-cli download pii-engineer/PII-Engineer-Multi-NER-v2.1 --local-dir models/PII-Engineer-Multi-NER-v2.1
+huggingface-cli download pii-engineer/PII-Engineer-Chinese-NER-v1.0 --local-dir models/PII-Engineer-Chinese-NER-v1.0
+```
 
-- 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")
+## Use Cases
 
-## Citation
+- **PDPA/GDPR/CCPA compliance** — detect PII in databases, logs, documents
+- **Data anonymization** — redact PII before sharing datasets
+- **CI/CD scanning** — catch leaked PII in code and configs
+- **Chat/support data** — clean PII from customer interactions
+- **Healthcare** — detect PHI in clinical notes
 
-```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}
-}
+## GitHub Action
+
+Scan your codebase for PII in CI/CD:
+
+```yaml
+- uses: gantz-ai/pii.engineer@v1.0.1
+  with:
+    path: "src/"
+    fail_on_pii: "true"
 ```
 
 ## 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).
+## Citation
+
+```bibtex
+@software{pii_engineer,
+  title = {PII Engineer: Multilingual PII Detection},
+  url = {https://github.com/gantz-ai/pii.engineer},
+  year = {2026}
+}
+```