Russian PII NER Model
Token-classification model that finds personal data (names, addresses,
contacts) and Russian identity-document numbers (passport, SNILS, INN, OMS,
driver licence, military ID, birth certificate, bank card) in Russian text.
It is the NER component of the pii-guard anonymization pipeline.
Fine-tuned from ai-forever/ruBert-base on 17,137 annotated Russian sentences,
with a 21-type / 43-label BIO head.
Model Sources
- Training data: redmadrobot-rnd/pii_train — 17,137 sentences, 39,687 entity spans
- Evaluation data: redmadrobot-rnd/pii_benchmark — 2,841 held-out sentences, disjoint from the training data
- Code: github.com/redmadrobot-rnd/pii-guard
Entity Types
1. Person (names)
FIRST_NAME, LAST_NAME, MIDDLE_NAME — given name, surname, and patronymic,
including mixed-case, latin-script, and out-of-order spellings.
2. Location / Address
COUNTRY, REGION, DISTRICT, CITY, STREET, HOUSE — the full Russian
address hierarchy down to the house/building number.
3. Contacts
EMAIL, PHONE, URL, IP_ADDRESS — structured contact and network
identifiers in many real-world formats.
4. Russian identity-document numbers
PASSPORT, INN (taxpayer number), SNILS (insurance account), OMS
(medical insurance policy), CREDIT_CARD, DRIVER_LICENSE,
MILITARY_ID, BIRTH_CERTIFICATE — document numbers in their canonical and
noisy/typo'd forms.
Labels follow the BIO scheme: B-<TYPE> / I-<TYPE> plus O — 43 labels total.
Usage
from transformers import pipeline
ner = pipeline(
"token-classification",
model="redmadrobot-rnd/rubert-base-pii-ner",
aggregation_strategy="simple",
)
text = "Иванов Пётр Сергеевич, паспорт 45 11 123456, тел. +7 999 123-45-67"
for ent in ner(text):
print(f"{ent['entity_group']:16s} {ent['word']!r} {ent['score']:.2f}")
Windowing over long inputs, confidence filtering and merging adjacent
fragments of the same entity are the caller's responsibility. The pii-guard
pipeline uses max_length=512, a sliding window with stride=128, and drops
predictions below min_confidence=0.3.
Merging is not optional: aggregation_strategy="simple" leaves elena@pochta.ru
as el + ena@pochta.ru when the model re-emits B- mid-entity. All scores
below assume merged fragments.
Evaluation
redmadrobot-rnd/pii_benchmark, 2,841 held-out sentences. Span-level micro-F1, one-to-one greedy matching, gold spans not normalised.
Table 1. 14 categories both systems express — exact matching. Folded: the
pipeline emits PERSON where the model emits FIRST_NAME. The pipeline also
covers DATE_TIME, BANK_ACCOUNT, BIK and TELEGRAM, which this model has no
head for.
| System | P | R | F1 |
|---|---|---|---|
| This model alone | 81.9 | 85.5 | 83.6 |
pii-guard pipeline (rules + this model) | 90.4 | 87.5 | 88.9 |
On the benchmark's own leaderboard protocol — PERSON + LOCATION, overlap
matching — this model scores 94.7 F1, the pipeline 95.0.
Table 2. Per-entity F1, this model alone — exact matching.
| Entity | gold | F1 | Entity | gold | F1 |
|---|---|---|---|---|---|
| FIRST_NAME | 499 | 89.8 | IP_ADDRESS | 155 | 36.0 |
| LAST_NAME | 457 | 84.5 | PASSPORT | 494 | 81.3 |
| MIDDLE_NAME | 308 | 91.5 | INN | 263 | 68.3 |
| COUNTRY | 242 | 86.1 | SNILS | 223 | 78.5 |
| REGION | 176 | 80.3 | OMS | 170 | 90.1 |
| CITY | 339 | 82.5 | CREDIT_CARD | 203 | 87.9 |
| DISTRICT | 161 | 78.3 | DRIVER_LICENSE | 371 | 81.8 |
| STREET | 171 | 87.3 | MILITARY_ID | 278 | 78.3 |
| HOUSE | 163 | 92.7 | BIRTH_CERTIFICATE | 341 | 83.7 |
| 221 | 97.6 | ||||
| PHONE | 173 | 86.9 | |||
| URL | 206 | 89.7 |
IP_ADDRESS is the one weak spot — the model splits on the dots; in pii-guard
this type is a regex rule.
Training Data
redmadrobot-rnd/pii_train
— 17,137 Russian sentences with 39,687 BIO-annotated entity
spans: manually verified production logs (pseudonymized), synthetic
document-style texts per Russian document type, and hard negatives (numeric
strings that resemble document identifiers but are not). Rows that also occur in
pii_benchmark were removed from training, so evaluation stays clean.
Fine-tuned for 10 epochs, lr 3e-5 (linear decay, no warmup), batch size 16, AdamW (weight decay 0.01), fp32, max sequence length 512, seed 42.
Citation
If you use this model, please cite this repository.
BibTeX:
@misc{redmadrobot_rubert_base_pii_ner,
title = {Russian PII NER Model},
author = {Red Mad Robot R\&D},
year = {2026},
howpublished = {\url{https://huggingface.co/redmadrobot-rnd/rubert-base-pii-ner}},
note = {Fine-tuned from ai-forever/ruBert-base}
}