mabahboh/sitr-privacy-filter-ar-v1

سِتر · Sitr — Arabic & Gulf PII Detection

A token classifier for personal data in Arabic, adapted to Saudi, Gulf and Levantine formats. Built on openai/privacy-filter and trained on mabahboh/sitr-arabic-pii.

What it detects

43 fine-grained classes, tagged BIO over 87 tags:

access_token, account_number, api_key, bahrain_cpr, boundary_number, building_number, card_cvv, card_pin, commercial_registration, contract_number, credit_card, db_connection, deed_number, emirates_id, employee_id, gulf_iban, gulf_phone, insurance_policy, iqama, kuwait_civil_id, medical_record_number, medicine_name, national_address, national_id, oman_civil_id, otp_code, passport_number, password, postal_code, private_address, private_date, private_email, private_phone, qatar_qid, short_address, spoken_iqama, spoken_national_id, spoken_otp, spoken_phone, syria_national_id, syria_phone, vehicle_plate, visitor_number

These fold onto 8 base classes - account_number, national_id, other, private_address, private_date, private_email, private_phone, secret - and label_scheme.json ships alongside the weights so the collapse can be reproduced exactly.

Results

Entity-level scores on the held-out test split: a span counts only when its boundaries and its class both match. Splits are partitioned by document family, so no near-duplicate of a test row appears in training.

Level	Precision	Recall	F1
Fine-grained (43 classes)	0.629	0.776	0.694
Base classes (8)	0.788	0.852	0.818

The base-class row is the number that reflects redaction: confusing two sibling identifier types still masks the span. The gap between the rows is the cost of sub-type confusion alone.

Per class

Class	Precision	Recall	F1	Support
access_token *	0.607	0.872	0.716	39
account_number	0.917	0.981	0.948	213
api_key *	0.519	0.818	0.635	33
bahrain_cpr *	0.059	0.091	0.071	11
boundary_number *	0.310	0.333	0.321	39
building_number *	0.435	0.612	0.508	49
card_cvv *	0.565	0.722	0.634	18
card_pin *	0.286	0.118	0.167	17
commercial_registration	0.526	0.714	0.606	84
contract_number	0.553	0.727	0.628	150
credit_card	0.820	0.934	0.874	122
db_connection *	0.667	0.846	0.746	26
deed_number	0.435	0.709	0.539	103
emirates_id *	1.000	1.000	1.000	18
employee_id	0.774	0.827	0.800	249
gulf_iban *	0.512	0.750	0.609	28
gulf_phone *	0.093	0.154	0.116	26
insurance_policy	0.847	0.936	0.890	172
iqama	0.448	0.681	0.540	188
kuwait_civil_id *	0.125	0.125	0.125	8
medical_record_number	0.490	0.708	0.580	144
medicine_name	0.000	0.000	0.000	57
national_address	0.916	0.974	0.944	156
national_id	0.751	0.890	0.815	390
oman_civil_id *	0.286	0.429	0.343	14
otp_code *	0.197	0.293	0.235	41
passport_number	0.716	0.817	0.763	71
password	0.653	0.790	0.715	62
postal_code	0.484	0.754	0.590	61
private_address	0.188	0.032	0.055	93
private_date	0.882	0.940	0.910	215
private_email	0.925	0.966	0.945	293
private_phone	0.744	0.893	0.812	488
qatar_qid *	0.324	0.579	0.415	19
short_address	0.901	0.924	0.912	79
spoken_iqama *	0.010	0.059	0.017	17
spoken_national_id *	0.076	0.250	0.117	32
spoken_otp *	0.133	0.222	0.167	18
spoken_phone *	0.310	0.481	0.377	27
syria_national_id *	0.154	0.222	0.182	9
syria_phone *	0.025	0.045	0.032	22
vehicle_plate	0.482	0.675	0.562	80
visitor_number *	0.091	0.231	0.130	39

* fewer than 50 test spans - indicative only, not a measurement.

Read recall first. A missed span is a personal identifier reaching an external model and cannot be undone; a false positive is an over-redacted word. The two errors are not equally costly.

Usage

from transformers import pipeline

nlp = pipeline("token-classification", model="mabahboh/sitr-privacy-filter-ar",
               aggregation_strategy="simple")

nlp("هذا رقم هويتي 1429096553 وجوالي 0551234567")

Training

Method	LoRA (r=16, alpha=32, dropout=0.05), merged into the base weights
Adapted modules	k_proj, o_proj, q_proj, v_proj
Learning rate	0.0002
Epochs	3
Effective batch	32
Max sequence length	512 (stride 128)
Auxiliary base-class loss	weight 0.3

Training used a single head over the fine classes, with the base-class signal marginalised out of the same logits rather than added as a second head - the probability of a base class is the sum of the probabilities of every fine class folding into it. One distribution, so the two levels cannot contradict each other.

The adapter was applied to attention projections only. On a Mixture-of-Experts backbone this matters: routing weights are left untouched, so expert assignment is unchanged by training or by the merge.

Limitations

• Not a compliance guarantee. This reduces exposure; it does not certify that a system is anonymous or PDPL-compliant.
• Person names are not covered - private_person is absent from the training data, and names are where Arabic PII models fail hardest.
• Many classes are not separable by span alone. Ten bare digits is simultaneously a national ID, an iqama, a phone, a commercial registration and more; only context separates them. Classes marked * above have too little test support to measure.
• Evaluated on the dataset's own test split. Performance on live traffic will be lower.

Licence

Apache-2.0.