redmadrobot-rnd/pii_train

Russian PII NER Training Dataset

Dataset Description

This is the training corpus for PII (Personally Identifiable Information) detection and Named Entity Recognition (NER) on Russian-language text. It targets guardrail and anonymization pipelines that must reliably find personal data (names, addresses, contacts) and Russian identity-document numbers (passport, SNILS, INN, OMS, etc.) in text.

The corpus combines real, manually annotated examples from production logs — where all real personal data has been replaced with synthetic equivalents — with synthetic document-style texts and hand-filtered hard negatives. It is token-level annotated in the standard BIO scheme across 21 entity types.

Dataset Sources

• —Evaluation set: redmadrobot-rnd/pii_benchmark — the held-out test split for this corpus (2,841 sentences, identical schema, disjoint rows)
• —Model trained on this corpus: redmadrobot-rnd/rubert-base-pii-ner — the NER component of the pii-guard anonymization pipeline
• —Code: github.com/redmadrobot-rnd/pii-guard

Relation to the benchmark

This dataset is the train counterpart of **redmadrobot-rnd/pii_benchmark** (2,841 evaluation sentences): identical label set, annotation scheme, and three-column schema, disjoint rows.

Content Categories

The corpus is annotated across 21 entity types, grouped into four families.

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.

Training Sample Composition

The dataset contains 17,137 sentences with 39,687 annotated entity spans.

Table 1. Entity spans per type

Table 2. Spans grouped into coarse categories (the schema used for cross-model comparison on pii_benchmark)

Data Structure

The 21 entity types appear in the BIO scheme as B-<TYPE> / I-<TYPE> (plus O), e.g. B-FIRST_NAME, I-STREET, B-SNILS — 43 labels in total.

Curation Rationale

General-purpose Russian NER corpora cover person and location names but not the Russian identity-document taxonomy (passport, SNILS, INN, OMS, driver licence, military ID, birth certificate) that an anonymization guardrail has to redact, and they contain no negative examples for numeric strings that merely look like document identifiers. This corpus was built to fill both gaps, with a fine-grained address hierarchy so that redaction can be selective rather than blanket.

Source Data

Table 3. Source families

Table 4. Synthetic document templates, rows per type

Provenance is documented here but is not shipped as a column — the rows form a single flat table, exactly like the benchmark.

Personal and Sensitive Information

The corpus is about personal data but is not intended to contain any. All real personal data in the log-sourced rows was replaced with synthetic equivalents prior to annotation; document numbers, cards, names, addresses, emails, phones, and IPs are synthetic and do not identify real people. Document identifiers are format-valid (including checksums where applicable) so that detectors can be trained on realistic inputs — do not treat them as issued documents.

Citation

If you use this dataset, please cite this repository.

BibTeX:

@misc{redmadrobot_pii_train,
  title        = {Russian PII NER Training Dataset},
  author       = {Red Mad Robot R\&D},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/redmadrobot-rnd/pii_train}},
  note         = {Companion evaluation set: \url{https://huggingface.co/datasets/redmadrobot-rnd/pii_benchmark}}
}