README.md

---
license: other
license_name: offchain-studio-source-license-1.0
license_link: https://ai.basement.dev/license
library_name: transformers
pipeline_tag: token-classification
base_model: microsoft/mdeberta-v3-base
base_model_relation: finetune
datasets:
  - ai4privacy/pii-masking-openpii-1m
language:
  - bg
  - cs
  - da
  - de
  - el
  - en
  - es
  - et
  - fi
  - fr
  - hr
  - hu
  - it
  - lt
  - lv
  - nl
  - pl
  - pt
  - ro
  - sk
  - sl
  - sr
  - sv
tags:
  - token-classification
  - pii
  - pii-detection
  - pii-masking
  - redaction
  - privacy
  - deberta-v3
metrics:
  - f1
model-index:
  - name: masker
    results:
      - task:
          type: token-classification
          name: PII span detection
        dataset:
          name: pii-masking-openpii-1m (validation)
          type: ai4privacy/pii-masking-openpii-1m
        metrics:
          - type: f1
            name: Strict span F1
            value: 0.982
          - type: f1
            name: Typed F1
            value: 0.995
          - type: recall
            name: Leak-safe recall
            value: 0.9996
---

# Masker

**masker** is a multilingual token classifier for personally identifiable
information (PII), covering **23 European languages**. It is the neural half of a
two-part redaction system: this model tags _contextual_ PII: names, addresses,
ages, dates.

A separate, dependency-free rules layer handles _structured_
PII (IBANs, cards, national IDs) with real checksum validation.

On any span where both fire, the checksum-validated rule takes precedence; everywhere else the model
decides. This repository is the neural half, and the **full-accuracy reference
model** of the family.

📱 Need something small enough to run on a phone or in a web-browser? See
[**masker-mini**](https://huggingface.co/amsintelligence/masker-mini). A 6-layer
distillation shipped as ONNX and 4-bit Core ML, down to ~17 MB.

## Model type & training

Masker is a **DeBERTa-v3 token classifier** fine-tuned from
[`microsoft/mdeberta-v3-base`](https://huggingface.co/microsoft/mdeberta-v3-base)
on [`ai4privacy/pii-masking-openpii-1m`](https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1m).
It is trained with **boundary-first BIOES supervision**.

After fine-tuning, the model's **vocabulary was pruned** from mDeBERTa's 250K
SentencePiece pieces to the **136,671** tokens actually present in the training
corpus. Because every used token is retained (byte-fallback covers the rest), the
prune is lossless on in-distribution text; it shrinks the embedding table by ~45%
(278M → 190M parameters). Weights are stored in **bfloat16**.

The head predicts **12 entity types** (48 BIOES labels + `O`):

`GIVEN_NAME`, `SURNAME`, `CITY`, `STREET_NAME`, `BUILDING_NUMBER`, `ZIP_CODE`,
`PHONE`, `EMAIL`, `CREDIT_CARD`, `DATE`, `AGE`, `GOVERNMENT_ID`.

`GOVERNMENT_ID` collapses passport / national-ID / SSN / tax / driver-license
numbers, which the rules layer disambiguates by checksum and format. Other
structured types (IBAN, IP, URL, etc.) are owned entirely by the rules layer and
are not part of this head.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

tok = AutoTokenizer.from_pretrained("amsintelligence/masker")
model = AutoModelForTokenClassification.from_pretrained("amsintelligence/masker").eval()

text = "Kundin Beatrix Möller, geb. 04.07.1979, Rosenweg 8, 50667 Köln."
enc = tok(text, return_tensors="pt", return_offsets_mapping=True)
offsets = enc.pop("offset_mapping")[0]
with torch.no_grad():
    labels = model(**enc).logits.argmax(-1)[0]
for (a, b), lab in zip(offsets.tolist(), labels.tolist()):
    if b > a and model.config.id2label[lab] != "O":
        print(text[a:b], "->", model.config.id2label[lab])
```

The raw output is per-token BIOES labels; turning those into character spans and
merging them with the rules layer is handled by the companion `mask` package.

## Evaluation

Measured on the openpii-1m validation split (span-level, boundary-exact).

**Overall**

| metric           | value     |
| ---------------- | --------- |
| Strict span F1   | **0.982** |
| Typed F1         | 0.995     |
| Typed precision  | 0.994     |
| Typed recall     | 0.996     |
| Leak-safe recall | 0.9996    |

**Per-type strict F1**

| entity          | F1    |
| --------------- | ----- |
| EMAIL           | 1.000 |
| DATE            | 1.000 |
| PHONE           | 0.999 |
| GOVERNMENT_ID   | 0.999 |
| CREDIT_CARD     | 0.999 |
| ZIP_CODE        | 0.997 |
| CITY            | 0.996 |
| STREET_NAME     | 0.992 |
| BUILDING_NUMBER | 0.992 |
| AGE             | 0.985 |
| GIVEN_NAME      | 0.940 |
| SURNAME         | 0.935 |

Person names are the hardest category; every other type is ≥ 0.985.

## Limitations & biases

- **In-distribution evaluation.** openpii-1m is synthetic / templated. These
  numbers are an upper bound on a friendly distribution and may be lower on
  real-world, out-of-distribution text. Validate on your own data before relying
  on it.
- Not a substitute for review in high-stakes settings. No detector is perfect;
  design for residual leakage.

## Credits & attribution

This model is a derivative work and would not exist without:

- **Backbone:** [`microsoft/mdeberta-v3-base`](https://huggingface.co/microsoft/mdeberta-v3-base)
  by Microsoft — **MIT License**. DeBERTaV3: He, Gao & Chen, 2021
  ([arXiv:2111.09543](https://arxiv.org/abs/2111.09543)).
- **Training data:** [`ai4privacy/pii-masking-openpii-1m`](https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1m)
  by **Ai4Privacy** — **CC-BY-4.0**. Attribution required; please retain this
  credit in downstream use.

## License

Licensed under the **Offchain Studio Source License, Version 1.0**, a
source-available license. Full terms: <https://ai.basement.dev/license>.

Commercial-license requests: `licensing@basement.dev`.

See the accompanying **`NOTICE`** file; its Required
Notice line _MASKER © 2026 Offchain Studio_ must be retained in
redistribution.

The underlying components keep their own (attribution-only) terms, retained in
Credits above: the mDeBERTa lineage is **MIT** and the openpii training data is
**CC-BY-4.0**.

## Citation

```bibtex
@software{masker,
  title  = {masker: multilingual PII detection for 23 European languages},
  year   = {2026},
  note   = {DeBERTa-v3 token classifier, boundary-first BIOES supervision},
  url    = {https://huggingface.co/amsintelligence/masker}
}
```