---
license: other
license_name: desert-ant-labs-source-available-1.0
license_link: https://license.desertant.ai/1.0
language:
- bg
- hr
- cs
- da
- nl
- en
- et
- fi
- fr
- de
- el
- hu
- ga
- it
- lv
- lt
- mt
- pl
- pt
- ro
- sk
- sl
- es
- sv
tags:
- pii
- redaction
- token-classification
- on-device
- onnx
- multilingual
pipeline_tag: token-classification
---

# redact: on-device multilingual PII redaction

Detects and redacts personal data (names, addresses, emails, phone numbers,
cards, IBANs, national IDs and more) in text across **all 24 official EU
languages** (Latin, Greek and Cyrillic scripts). A BIOES token classifier plus a
portable, dependency-free deterministic layer for structured IDs. The deployable
model is **~13.7 MB** (int4 ONNX); with the tokenizer the total on-device
footprint is **~16 MB**.

> `"Call Anna Kovács at anna@example.hu, IBAN GB29NWBK60161331926819"` →
> `"Call [GIVEN_NAME] [SURNAME] at [EMAIL], IBAN [BANK_ACCOUNT]"`

## Try it

- **Live demo:** [desert-ant-labs/redact-demo](https://huggingface.co/spaces/desert-ant-labs/redact-demo): paste text and watch PII get highlighted or masked, fully in your browser.
- **iOS / macOS / tvOS / visionOS:** [`redact-swift`](https://github.com/Desert-Ant-Labs/redact-swift): the Swift SDK (Swift Package Manager) with a built-in demo app. It bundles the compiled Core ML model below.
- **Node / browser (JavaScript / TypeScript):** [`redact-js`](https://github.com/Desert-Ant-Labs/redact-js) (`npm i @desert-ant-labs/redact`): fetches the model from this repo (transformers.js) and caches it.
- **JVM / Android (Kotlin):** [`redact-kotlin`](https://github.com/Desert-Ant-Labs/redact-kotlin) (JitPack): ONNX Runtime with the bundled int4 model.

```swift
import Redact

let redact = Redact()
let r = try await redact.redaction(of: "Email Anna Kovács at anna@example.hu.")
r.redactedText   // "Email [GIVEN_NAME_1] [SURNAME_1] at [EMAIL_1]."
```

## Files

| File | Format | Size | Contents |
|---|---|---:|---|
| `redact.onnx` | ONNX (int4, opset 21) | ~13.7 MB | 4-bit-quantized model, batch=1, ready for on-device runtimes |
| `redact.mlmodelc` | Compiled Core ML (4-bit) | ~11.6 MB | Palettized model, ready to load on Apple platforms (used by `redact-swift`) |
| `redact.pt` | PyTorch checkpoint | ~90 MB | Full-precision weights + config (for retraining / other runtimes) |
| `config.json` | JSON | tiny | Transformer + label config |
| `tokenizer.json`, `tokenizer_config.json` | JSON | ~2.3 MB | EU-trimmed (31,475-piece) SentencePiece tokenizer (XLM-R lineage) |
| `labels.json` | JSON | tiny | BIOES `id2label` / `label2id` |
| `redact_meta.json` | JSON | tiny | Public labels, deterministic-owner labels, recommended thresholds, base-model info |

## Taxonomy (20 public labels)

`GIVEN_NAME`, `SURNAME`, `STREET_NAME`, `BUILDING_NUMBER`, `SECONDARY_ADDRESS`,
`CITY`, `STATE`, `ZIP_CODE`, `EMAIL`, `PHONE`, `CREDIT_CARD`, `BANK_ACCOUNT`,
`ROUTING_NUMBER`, `IP_ADDRESS`, `URL`, `GOVERNMENT_ID`, `PASSPORT`,
`DRIVERS_LICENSE`, `TAX_ID`, `SSN`.

The deterministic layer additionally emits `IMEI` (device identifier), a
deterministic-only label outside the 20-label neural head.

## Architecture

- **Encoder:** Multilingual-MiniLM (XLM-R lineage) truncated to 6 layers with an
  EU-script-trimmed vocab (~23 M params), fine-tuned for BIOES tagging.
- **Deterministic layer:** a pure-stdlib post-processor owns high-confidence
  structured labels (email, URL, IP/MAC, card, IBAN/BIC, VIN, SSN, routing,
  tax id, government id, passport, driving licence, IMEI) with real validation
  (Luhn, ISO-13616 IBAN, ISO-7064, per-country checksums) and reconciles them
  with the model's contextual predictions. EU structured coverage includes
  **checksum-validated national IDs for all 24 EU countries, all 27 EU VAT
  numbers, IMEI, and per-country driving-licence numbers**. The same layer is
  ported byte-for-byte to the JS and Swift runtimes (span-for-span parity).
- Recommended runtime: `min_score = 0.6`, `max_length = 256`, `stride = 64`.

## Benchmark

On a fair, all-label, 24-language evaluation (external WikiANN + MultiNERD for
names/places, plus a neutral format-valid structured-PII set): fair composite
**leak-safe recall 88.8, typed F1 85.4, strict F1 71.0**, best among on-device
models by a wide margin, with better labeling accuracy and precision than models
100×+ its size.

## License

[Desert Ant Labs Source-Available License](https://license.desertant.ai/1.0). Free for
most apps; a commercial license is required at scale. Full terms are at the link.
Licensing: <licensing@desertant.ai>.