Alias mmBERT-small: personal data detection, French and English

Token classification model of Alias: it finds the personal data in a French or English text so it can be masked before the text goes to an LLM. Fine-tuned from jhu-clsp/mmBERT-small; in Alias it runs together with rules (checksums and keywords).

Labels

BIO tags over eight types: PERSON, ADDRESS, EMAIL, PHONE, IBAN, NATIONAL_ID (French NIR, US SSN, UK NINO), BIRTH_DATE, IDENTIFIER (a person's file, contract or customer number). Company names, company addresses, switchboards and invoice numbers are not labelled. Long texts are read in windows of 512 tokens overlapping by 128.

Training data

Synthetic and public only, no real personal data:

documents generated from a grid of types, domains and formats with fake identities (Faker, MIT), every span known by construction;
WikiNER French (CC BY 4.0; Nothman et al., 2013), names converted to PERSON;
gretel synthetic_pii_finance_multilingual (Apache-2.0), French and English.

3 epochs, learning rate 3e-5, batch 16, windows of 512 tokens (alias_pii.train). Chosen among three candidates (mmBERT-small, XLM-RoBERTa base, mDeBERTa-v3 base) by a rule written before training (docs/TRAINING.md in the repository).

Results

Set	Precision	Recall	Leak rate
Synthetic test split (4,200 documents, read once)	0.970	0.980	1.1 %
200 documents written by an LLM absent from training	0.94	0.93	3.5 %

Strict precision and recall (label and both boundaries); leak rate: share of personal values still readable in full after masking. Not measured on real documents yet. CPU, unquantised: about one second per 3,000-character page on 4 cores. Dynamic int8 quantisation destroys its precision.

Limits

Measured on synthetic and LLM-written text only: real documents may hold forms it never saw.
Out of distribution its weak spots are birth dates and national IDs. The Alias rules add national IDs (checksum), birth dates announced by a keyword and file numbers announced by a person's-file keyword ("dossier n°", "customer ID"); a birth date with no keyword can still be missed (11 % leaked on LLM-written validation text, docs/DETECTOR.md).
No detector has perfect recall: Alias is a component, not an anonymity guarantee.

Use

# pip install "alias-pii[model] @ git+https://github.com/gabrielkpo/alias"
from huggingface_hub import snapshot_download

from alias_pii.detector import RulesAndModel
from alias_pii.masking import mask

detector = RulesAndModel.from_checkpoint(snapshot_download("Gabrielkph/alias-mmbert-small"))
print(mask("Écrire à Jean Dupont, 14 rue des Lilas, 69003 Lyon.", detector).text)

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Labels

Training data

Synthetic and public only, no real personal data:

documents generated from a grid of types, domains and formats with fake identities (Faker, MIT), every span known by construction;

WikiNER French (CC BY 4.0; Nothman et al., 2013), names converted to PERSON;

gretel synthetic_pii_finance_multilingual (Apache-2.0), French and English.

Results

Set	Precision	Recall	Leak rate
Synthetic test split (4,200 documents, read once)	0.970	0.980	1.1 %
200 documents written by an LLM absent from training	0.94	0.93	3.5 %

Limits

Measured on synthetic and LLM-written text only: real documents may hold forms it never saw.

Out of distribution its weak spots are birth dates and national IDs. The Alias rules add national IDs (checksum), birth dates announced by a keyword and file numbers announced by a person's-file keyword ("dossier n°", "customer ID"); a birth date with no keyword can still be missed (11 % leaked on LLM-written validation text, docs/DETECTOR.md).

No detector has perfect recall: Alias is a component, not an anonymity guarantee.

Use

# pip install "alias-pii[model] @ git+https://github.com/gabrielkpo/alias" from huggingface_hub import snapshot_download from alias_pii.detector import RulesAndModel from alias_pii.masking import mask detector = RulesAndModel.from_checkpoint(snapshot_download("Gabrielkph/alias-mmbert-small")) print(mask("Écrire à Jean Dupont, 14 rue des Lilas, 69003 Lyon.", detector).text)