---
license: apache-2.0
language:
- en
tags:
- gliner
- pii
- named-entity-recognition
- privacy
- sosa
- singapore
- nric
- fin
base_model: urchade/gliner_large-v2.1
---

# sosa-pii-ner-sg-v1.0.0

Regional PII named entity recognition model for Singapore documents.
Part of the **SOSA DevOps Privacy Filter** — a local-first, privacy-preserving
AI runtime for developers. Weights are Apache 2.0. No cloud required.

🔗 **Source code:** [SOSA DevOps on GitHub](https://github.com/SovereignSystems-cc/sosa-devops)
🌐 **Product:** [sovereignsystems.cc](https://sovereignsystems.cc)

---

## Model summary

Fine-tuned from `urchade/gliner_large-v2.1` on synthetic Singapore PII data.
Detects NRIC/FIN numbers, Unique Entity Numbers (UEN), Singapore local phone
numbers, and healthcare reference numbers (HRN) in English documents.

**Intended use:** Local PII detection within the SOSA DevOps Privacy Filter
sidecar. Text never leaves the user's machine.

---

## Labels

| Label | Description | Format | Validator |
|---|---|---|---|
| `sg_nric_fin` | Singapore NRIC / FIN | Letter + 7 digits + check letter (S/T/F/G/M) | Prefix + check character |
| `sg_uen` | Unique Entity Number | 9–10 chars (numeric + alpha suffix, or T-prefix) | Format validation |
| `sg_phone_local` | Singapore local phone | 8 digits, starting 3/6/8/9 | Prefix validation |
| `sg_health_hrn` | Healthcare reference number | Alphanumeric 10–21 chars | Format validation |

Global labels also carried (defence-in-depth):
`email`, `phone_e164`, `credit_card`, `passport_generic`, `ipv4_public`

---

## Evaluation — v1.0.0 gate results

| Label | F1 | Gate |
|---|---|---|
| `sg_nric_fin` | **0.9524** | ≥ 0.85 ✅ |
| `sg_uen` | **0.7143** | ≥ 0.70 ✅ |

First-run gate pass (T1). Training: D-SG-1 dataset (1,157 positive examples),
10,000 steps, A40 GPU, 2026-05-28/29.

---

## Limitations

- **NRIC/UEN alphanumeric collision:** Both labels use alphanumeric formats.
  Context (NRIC/FIN identity keywords vs UEN/company registration keywords)
  disambiguates.
- **UEN precision:** F1=0.7143 reflects limited UEN training examples. Adequate
  for production defence-in-depth; higher recall on NRIC is the primary
  detection surface.
- **Context-gated:** Bare values without surrounding context are unreliable.
- **Language:** English. Singapore-specific context templates (healthcare,
  HR, business registration).

---

## Training data

Synthetic Singapore PII examples only. No real resident or patient data used.

---

## Integrity

`pytorch_model.bin` SHA-256:
```
070db3ae5b22c7f40cf8037a604262f58a404d17bbf79dd69a26fe9f98edd91f
```

---

## License

Apache 2.0 — inherited from `urchade/gliner_large-v2.1`.
Fine-tuned by Sovereign Systems.
See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0).