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component_type
string
locale
string
value
string
weight
int64
state
null
postal_code
null
area_codes
null
first_name
en-US
Aaliyah
1
null
null
null
first_name
en-US
Adrian
1
null
null
null
first_name
en-US
Alex
1
null
null
null
first_name
en-US
Alexis
1
null
null
null
first_name
en-US
Amari
1
null
null
null
first_name
en-US
Amelia
1
null
null
null
first_name
en-US
Andre
1
null
null
null
first_name
en-US
Aria
1
null
null
null
first_name
en-US
Avery
1
null
null
null
first_name
en-US
Bailey
1
null
null
null
first_name
en-US
Benjamin
1
null
null
null
first_name
en-US
Brianna
1
null
null
null
first_name
en-US
Cameron
1
null
null
null
first_name
en-US
Camila
1
null
null
null
first_name
en-US
Casey
1
null
null
null
first_name
en-US
Charlotte
1
null
null
null
first_name
en-US
Chloe
1
null
null
null
first_name
en-US
Dakota
1
null
null
null
first_name
en-US
Daniel
1
null
null
null
first_name
en-US
Diego
1
null
null
null
first_name
en-US
Drew
1
null
null
null
first_name
en-US
Elena
1
null
null
null
first_name
en-US
Elias
1
null
null
null
first_name
en-US
Emery
1
null
null
null
first_name
en-US
Emma
1
null
null
null
first_name
en-US
Ethan
1
null
null
null
first_name
en-US
Evelyn
1
null
null
null
first_name
en-US
Fatima
1
null
null
null
first_name
en-US
Gabriel
1
null
null
null
first_name
en-US
Grace
1
null
null
null
first_name
en-US
Harper
1
null
null
null
first_name
en-US
Henry
1
null
null
null
first_name
en-US
Isabella
1
null
null
null
first_name
en-US
Isaiah
1
null
null
null
first_name
en-US
Jada
1
null
null
null
first_name
en-US
Jasmine
1
null
null
null
first_name
en-US
Jordan
1
null
null
null
first_name
en-US
Julian
1
null
null
null
first_name
en-US
Kai
1
null
null
null
first_name
en-US
Kayla
1
null
null
null
first_name
en-US
Leah
1
null
null
null
first_name
en-US
Leo
1
null
null
null
first_name
en-US
Liam
1
null
null
null
first_name
en-US
Lucia
1
null
null
null
first_name
en-US
Mateo
1
null
null
null
first_name
en-US
Maya
1
null
null
null
first_name
en-US
Mia
1
null
null
null
first_name
en-US
Morgan
1
null
null
null
first_name
en-US
Naomi
1
null
null
null
first_name
en-US
Nora
1
null
null
null
first_name
en-US
Olivia
1
null
null
null
first_name
en-US
Parker
1
null
null
null
first_name
en-US
Quinn
1
null
null
null
first_name
en-US
Rafael
1
null
null
null
first_name
en-US
Riley
1
null
null
null
first_name
en-US
Rowan
1
null
null
null
first_name
en-US
Santiago
1
null
null
null
first_name
en-US
Sofia
1
null
null
null
first_name
en-US
Taylor
1
null
null
null
first_name
en-US
Valentina
1
null
null
null
first_name
en-US
Xavier
1
null
null
null
first_name
en-US
Zoe
1
null
null
null
last_name
en-US
Adams
1
null
null
null
last_name
en-US
Allen
1
null
null
null
last_name
en-US
Anderson
1
null
null
null
last_name
en-US
Baker
1
null
null
null
last_name
en-US
Bennett
1
null
null
null
last_name
en-US
Brooks
1
null
null
null
last_name
en-US
Brown
1
null
null
null
last_name
en-US
Campbell
1
null
null
null
last_name
en-US
Carter
1
null
null
null
last_name
en-US
Castillo
1
null
null
null
last_name
en-US
Chen
1
null
null
null
last_name
en-US
Clark
1
null
null
null
last_name
en-US
Collins
1
null
null
null
last_name
en-US
Cooper
1
null
null
null
last_name
en-US
Cruz
1
null
null
null
last_name
en-US
Davis
1
null
null
null
last_name
en-US
Diaz
1
null
null
null
last_name
en-US
Edwards
1
null
null
null
last_name
en-US
Evans
1
null
null
null
last_name
en-US
Flores
1
null
null
null
last_name
en-US
Foster
1
null
null
null
last_name
en-US
Garcia
1
null
null
null
last_name
en-US
Gomez
1
null
null
null
last_name
en-US
Gray
1
null
null
null
last_name
en-US
Green
1
null
null
null
last_name
en-US
Hall
1
null
null
null
last_name
en-US
Harris
1
null
null
null
last_name
en-US
Hayes
1
null
null
null
last_name
en-US
Hernandez
1
null
null
null
last_name
en-US
Hill
1
null
null
null
last_name
en-US
Jackson
1
null
null
null
last_name
en-US
Johnson
1
null
null
null
last_name
en-US
Jones
1
null
null
null
last_name
en-US
Kim
1
null
null
null
last_name
en-US
King
1
null
null
null
last_name
en-US
Lee
1
null
null
null
last_name
en-US
Lewis
1
null
null
null
last_name
en-US
Lopez
1
null
null
null
End of preview.

Synthetic Form Profiles

Synthetic Form Profiles is a compact, product-neutral component corpus for generating coherent fictional people and mailing profiles. It is designed for form testing, document rendering, OCR evaluation, software demonstrations, and other workflows that need varied but reproducible placeholder data.

The dataset contains components, not identity records. Consumers are expected to combine independently sampled name tokens, synthetic street roots, street suffixes, and locality metadata with their own seeded generator. The packaged artifact at artifacts/en-US.json provides the same components in a validated application-friendly layout.

Dataset structure

The components configuration exposes one row per reusable component:

Field Type Description
component_type string Category: first_name, last_name, street_root, street_suffix, or locality.
locale string BCP 47 locale associated with the component.
value string Display value sampled or composed by consumers.
weight number Relative sampling weight. Weights are diversity controls, not demographic estimates.
state string Two-letter state abbreviation for locality rows.
postal_code string Representative postal code for locality rows.
area_codes string Pipe-delimited NANP area codes available to a locality.

The versioned artifact follows the machine-readable contract in schema/corpus.schema.json. manifest.json records its SHA-256 checksum and component counts.

Recommended generation method

Use a seeded pseudo-random stream and derive one isolated stream per generated profile. Select one first name, one last name, one locality, one street root, and one suffix by relative weight. Compose fields instead of copying a preassembled row:

  1. Generate a house number within the range appropriate for the target form.
  2. Combine the house number, street root, and suffix.
  3. Keep city, state, postal code, and area code from the same locality.
  4. Derive full name and email from the selected person components.
  5. Use reserved fictional ranges for phone numbers and government identifiers.

This approach provides substantial combinatorial variance while keeping related form fields internally consistent.

Intended uses

  • Automated form population and validation
  • PDF and document rendering tests
  • OCR and document-understanding evaluation
  • Demo, tutorial, and fixture generation
  • Reproducible synthetic-data pipelines

Out-of-scope uses

  • Identifying, contacting, or profiling real people
  • Demographic, fairness, electoral, or population analysis
  • Address validation, geocoding, or deliverability decisions
  • Authentication, identity verification, or fraud detection
  • Generating credentials or identifiers intended to appear genuine

Privacy and safety

This corpus contains no assembled person records, contact records, source documents, user submissions, or sensitive attributes. Names and geographic components can resemble real-world values because they are ordinary language and place names; any complete profile produced from them is an algorithmic composition and must be represented as fictional.

Consumers should use .test email domains, NANP 555-01xx fictional subscriber numbers, and structurally invalid or explicitly reserved government identifier ranges. Generated artifacts should not be presented as evidence of a real identity or address.

Biases and limitations

  • The initial release covers only en-US formatting conventions.
  • The corpus is intentionally compact and does not model population frequency.
  • Weights promote output variety and must not be interpreted as demographic prevalence.
  • Locality rows are representative formatting references, not a complete or current postal directory.
  • Name coverage is not exhaustive and may not represent every culture, community, or naming convention.
  • Algorithmic combinations can still coincidentally resemble real people or locations.

Applications requiring demographic fidelity should use an independently reviewed source with suitable provenance, licensing, and bias analysis.

Versioning and integrity

Releases follow calendar versioning (YYYY.MM.PATCH). Consumers should pin a full repository commit and verify the SHA-256 value in manifest.json before activating an artifact. A moving branch should only discover a new immutable revision, never identify an in-progress generation run.

Schema-breaking changes require a new schemaVersion. Additive component releases increment corpusVersion and retain earlier tagged revisions.

Loading

from datasets import load_dataset

components = load_dataset(
    "rrainn/synthetic-form-profiles",
    "components",
    revision="<full-commit-sha>",
)

Direct artifact consumers can download artifacts/en-US.json with hf_hub_download, pinning the same full commit SHA, then validate it against the included schema and checksum manifest.

Maintenance and contributions

Changes should preserve the product-neutral schema, avoid assembled identities, include provenance and license review for externally sourced material, regenerate the artifact and manifest, and pass schema validation before publication. See CONTRIBUTING.md and CHANGELOG.md.

License

Licensed under the Apache License 2.0. See LICENSE.

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