ai4privacy/openpii-masking-mini-10k

OpenPII Masking Mini 10K

A compact, stratified subset of ai4privacy/pii-masking-openpii-1m, containing 10,000 samples for rapid experimentation, fine-tuning, and benchmarking of PII detection and masking models.

Sampling Methodology

Samples were selected using proportional stratified sampling by language:

1. Target count per language = round(lang_proportion × 10,000) — proportional representation.
2. Streaming + reservoir sampling collected 3× the target candidates per language.
3. Fixed random seed 42 applied at every step for full reproducibility.
4. Final samples shuffled and split 90/10 into train/validation.

Language Distribution

Language	Code	Samples
EN	en	1256
FR	fr	918
DE	de	841
ES	es	632
IT	it	625
BG	bg	328
PL	pl	326
CS	cs	325
ET	et	321
LT	lt	321
SV	sv	321
LV	lv	320
SK	sk	320
HU	hu	318
FI	fi	317
RO	ro	317
DA	da	316
EL	el	316
HR	hr	315
SL	sl	315
SR	sr	314
NL	nl	311
PT	pt	307

Schema

Field	Type	Description
source_text	string	Original text with real PII
masked_text	string	Text with PII replaced by [LABEL_N] tokens
privacy_mask	list[dict]	Annotations: label, start, end, value, label_index
uid	int	Unique identifier
language	string	ISO 639-1 language code
region	string	ISO 3166-1 region code
script	string	Script type (e.g., Latn, Cyrl)
mbert_tokens	list[str]	mBERT tokenization
mbert_token_classes	list[str]	BIO NER labels per token

PII Entity Types (19)

AGE, BUILDINGNUM, CITY, CREDITCARDNUMBER, DATE, DRIVERLICENSENUM, EMAIL, GENDER, GIVENNAME, IDCARDNUM, PASSPORTNUM, SEX, SOCIALNUM, STREET, SURNAME, TAXNUM, TELEPHONENUM, TITLE, ZIPCODE

Usage

from datasets import load_dataset

ds = load_dataset("ai4privacy/openpii-masking-mini-10k")
for sample in ds["train"]:
    print(sample["source_text"])
    print(sample["privacy_mask"])
    break

p5y Data Analytics

This dataset is built on the p5y framework - think of it as i18n but for privacy. Just as i18n (internationalization) translates content into different locales, p5y translates sensitive data into privacy-safe formats through a standardized 3-step approach:

1. Awareness - Scan and markup private entities in unstructured text, producing a structured privacy mask with entity types, distribution, density, and risk assessment.
2. Protection - Control identified personal data through masking, pseudonymization, or k-anonymization, tailored to the specific use case and regulatory requirements.
3. Quality Assurance - Measure remaining privacy risk after anonymization, evaluating de-anonymization risks through expert annotation and automated assessment.

Learn more at p5y.org

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License

CC BY 4.0 — same as source dataset.

Citation

If you use this dataset, please cite the original:

@dataset{ai4privacy_openpii_1m,
  author = {AI4Privacy},
  title = {OpenPII 1M — Multilingual PII Masking Dataset},
  year = {2024},
  publisher = {Hugging Face},
  doi = {10.57967/hf/8202},
  url = {https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1m}
}