---
pretty_name: Nobody PII Synthetic
license: apache-2.0
language:
  - de
  - en
  - nl
task_categories:
  - token-classification
tags:
  - pii
  - synthetic
  - named-entity-recognition
  - privacy
  - german
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: validation
        path: data/validation.parquet
      - split: test
        path: data/test.parquet
---

# Nobody PII Synthetic

A procedurally generated, multilingual corpus for training and regression
testing PII detection and redaction systems, with German as the primary
language and English/Dutch coverage.

Companion model:
[`naeyn/nobody-pii-de`](https://huggingface.co/naeyn/nobody-pii-de)

## Release identity

This Hub revision contains the canonical Parquet snapshot. The Parquet files
were verified at Hub revision
`23eba3fadef50ef4c01738bea0b0ee1c392426e6`; card and attribution metadata may
receive later commits without changing these data files. Pin the data files by
revision and verify their hashes when exact bytes matter.

| file | SHA-256 |
|---|---|
| `data/train.parquet` | `19ac658494c9e7d1e835afe257d4424e6aea775f32322c1b68d9852dc59fe0d8` |
| `data/validation.parquet` | `447da873078dae51b6685c23c6d8a2a1d8375ffc923267108efed216eaaa6b1a` |
| `data/test.parquet` | `578ce3d52b621dec093be0e685d85183ffa7f238756f3d59b4aa18a865ab0919` |


## Data statement

This release was generated from templates and Faker locale providers. No
source records containing real people's personal data were used to construct
these splits. Names, addresses, dates, contact details, identifiers, and other
values are generated or randomly constructed for the corpus. Any resemblance
to a real person, organization, or address is coincidental and unintended.

The dataset does **not** contain records from
[`ai4privacy/pii-masking-400k`](https://huggingface.co/datasets/ai4privacy/pii-masking-400k)
or
[`ai4privacy/pii-masking-openpii-1m`](https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1m).
Those are separate sources with separate licensing and provenance. OpenPII is
used only as a documented training source for the companion model, not as data
in this repository.

## Generation provenance

- **Generator snapshot:** `gen_v4.1` document-composer revision. It includes
  business-document templates, multilingual name forms, structured-identifier
  traps, ordinary-date hard negatives, pronoun/role-noun negatives, and Swiss
  address forms.
- **Requested corpus size:** 10,000 generated documents plus a 5% supplement
  of 500 pure-negative controls, for 10,500 rows total.
- **Split construction:** one shuffled corpus split into 9,450 train, 525
  validation, and 525 test documents (90% / 5% / 5%). The source JSON names
  the middle split `eval`; the Hub export names it `validation`.
- **Generation seed:** not retained in the canonical JSON snapshot. The
  training mix used seed `42` for concatenation/shuffling, but that is a
  separate model-training seed and must not be presented as this corpus's
generation seed.
- **Generation date:** not embedded in the canonical data artifact. The
  `gen_v4.1` label identifies the generator revision, not a reproducible
  timestamp. The published Parquet files and hashes above are the authoritative
  snapshot.

A fresh run of the public generator family may reproduce the distribution and
template family but is not claimed to reproduce these exact rows. This
explicitly avoids implying byte-for-byte regeneration from metadata that was
not recorded.

## Splits and languages

| split | documents | German | English | Dutch |
|---|---:|---:|---:|---:|
| train | 9,450 | 5,349 | 2,265 | 1,836 |
| validation | 525 | 300 | 130 | 95 |
| test | 525 | 296 | 127 | 102 |
| **total** | **10,500** | **5,945** | **2,522** | **2,033** |

The held-out test split contains 3,426 labeled entity spans and is intended as
regression evidence for models trained on this corpus. It is not an
independent real-world benchmark.

## Identity, template, and split policy

A generated document intentionally reuses one document-local identity in its
header, body, signature, and related fields when the template calls for it.
Documents are otherwise generated independently. No global identity registry,
identity-level grouping, or cross-split uniqueness guarantee was implemented;
Faker can therefore produce coincidental value collisions across documents.
Template families and negative controls are allowed to recur in every split.

This is a transparent synthetic-regression design, not a privacy-safe claim
that the held-out split is independent in the statistical sense.

## Deduplication and split-contamination audit

An exact-text audit of all 10,500 canonical JSON rows found:

- 112 duplicate-text groups in the complete corpus;
- 36 groups spanning more than one split;
- 0 duplicate groups containing any labeled entity span.

The cross-split duplicate groups are therefore pure-negative boilerplate, not
repeated labeled PII. This check is exact-text only; because templates recur,
near-duplicate and semantic overlap are expected and are not represented by a
claim of independence. The companion model's external German benchmark audit
used a separate exact-hash and 5-gram-near-duplicate procedure, reported in
that model card.

## Features

Each Parquet row has these fields:

| field | type | description |
|---|---|---|
| `text` | string | original generated document |
| `tokenized_text` | list[string] | whitespace/punctuation token sequence |
| `ner` | list[struct] | labeled token spans |
| `lang` | string | `de`, `en`, or `nl` |
| `doctype` | string | generated document template family |

Each `ner` item is a struct with:

```json
{"start": 5, "end": 5, "label": "person"}
```

`start` and `end` are **inclusive token indices** into `tokenized_text`, not
character offsets. The public Parquet release uses typed structs so it can be
loaded by the Hub Dataset Viewer and `datasets` without schema ambiguity.

## Label taxonomy

- `person` — person-name mention
- `address` — postal or street address
- `date of birth` — birth date mention
- `email` — email address
- `phone number` — phone number
- `iban` — IBAN-shaped identifier
- `organization` — company or employer name

Dates used as delivery, invoice, contract, or other non-birth events are
included as hard negatives when they are not labeled as `date of birth`.
Structured identifiers are synthetic and may be format-valid; they do not
represent accounts or identifiers belonging to real people.

## Generation characteristics

The corpus contains business-style emails, chats, support tickets, letters,
contracts, records, plain prose, and pure-negative controls. It deliberately
includes:

- multilingual names and surname particles;
- punctuation, spacing, Unicode, and OCR/export variants;
- dates that require context to distinguish birth dates from ordinary dates;
- order, ticket, and customer-number traps;
- checksum-shaped synthetic identifiers; and
- exact labels produced at generation time rather than by a separate annotator.

### Training split label counts

| label | instances |
|---|---:|
| `person` | 24,974 |
| `email` | 10,472 |
| `address` | 7,865 |
| `organization` | 5,283 |
| `phone number` | 6,473 |
| `date of birth` | 3,409 |
| `iban` | 2,415 |

## Intended use

Use this dataset to train or regression-test PII detection and redaction
systems, especially for multilingual business-document workflows. Always
measure on representative real or appropriately licensed evaluation data
before making production, privacy, or compliance claims.

## Limitations

- Template-based synthetic text cannot model the full diversity, noise, OCR,
  formatting, or ambiguity of real documents.
- Faker locale distributions are not a representation of all names, cultures,
  scripts, or geographies.
- Models can memorize templates or generator artifacts rather than learn
  transferable PII recognition.
- Checksum-shaped values are synthetic and must not be treated as live account
  or identity data.
- Exact byte-level regeneration is not promised because the original generation
  seed and timestamp were not preserved in the published snapshot.

## Loading

```python
from datasets import load_dataset

dataset = load_dataset(
    "naeyn/nobody-pii-synth-de",
    revision="23eba3fadef50ef4c01738bea0b0ee1c392426e6",
)
print(dataset)
```

## License and attribution

This dataset is released under Apache-2.0. The generated rows do not include
third-party source records. Faker locale providers were used as a generation
tool; [Faker](https://github.com/joke2k/faker) is MIT-licensed.

The dataset is separate from the companion model's additional training source,
[`ai4privacy/pii-masking-openpii-1m`](https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1m),
which is used under CC-BY-4.0 with attribution to **Ai4Privacy / Ai Suisse SA**.
No OpenPII rows are included here.

## Citation

```bibtex
@dataset{nobody_pii_synth_de,
  title = {Nobody PII Synthetic},
  author = {naeyn},
  year = {2026},
  url = {https://huggingface.co/datasets/naeyn/nobody-pii-synth-de},
  license = {Apache-2.0}
}
```