Datasets:
StrataSynth Personas Spain: 1,250 synthetic people
Browse files- README.md +185 -0
- img/es-age.png +3 -0
- img/es-employment.png +3 -0
- img/es-household.png +3 -0
- personas_es.jsonl +0 -0
README.md
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| 1 |
+
---
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license: cc-by-4.0
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language:
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- es
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tags:
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- synthetic
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- synthetic-data
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- personas
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- synthetic-personas
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- persona
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- synthetic-users
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- user-simulation
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- agent-evaluation
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- spain
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- stratasynth
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pretty_name: StrataSynth Personas Spain
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size_categories:
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- 1K<n<10K
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task_categories:
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- text-generation
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configs:
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- config_name: default
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data_files:
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- split: train
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path: personas_es.jsonl
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---
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# StrataSynth Personas Spain
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**1,250 synthetic people from Spain, written in Spanish, each with a biography, a way of speaking, hobbies, skills and goals, and a measured personality.**
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1,250 people · 27 fields per person · 17 autonomous communities
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Part of the [StrataSynth Personas collection](https://huggingface.co/collections/StrataSynth/stratasynth-personas-6ab96c1be53faa6c4c596031): eight countries, 10,000 people. All of them together: [StrataSynth/stratasynth-personas-8-countries](https://huggingface.co/datasets/StrataSynth/stratasynth-personas-8-countries).
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## Why this dataset
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- **A real population, not a stereotype.** Age, region, living arrangements and family structure follow the country's real adult population. Children's ages fit their parents' ages, and names follow real name frequencies by generation.
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- **Written in Spanish.** Biographies, daily routines, speaking styles, hobbies, skills and goals are written natively, not translated.
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- **Personality you can filter on.** Big Five (0-1) and adult attachment for every person. More extraverted people have more social hobbies; more open people have more cultural and learning-oriented ones.
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- **Ready to become a user.** A biography plus a speaking style is what a user simulator needs: drop one into a prompt and you have a customer, a patient or a respondent from Spain.
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## The population
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- **Ages** 18-85, median 50.
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- **Employment:** 56 % employed, 18 % retired, 8 % at home, 8 % unemployed, 8 % studying.
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- **Home:** 50 % own their home, 31 % rent, 18 % live in the family home.
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- **Living with parents:** 19 % of adults.
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## What people use it for
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- **Testing chatbots and agents** built for Spain with realistic local users before going live.
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- **Fairness and bias checks** across age, gender and region.
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- **Seeding synthetic data** in Spanish: "write the question this person would ask".
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- **Synthetic respondents** to pre-test surveys, products and messaging.
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## Example
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```json
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{
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"id": "es-00002",
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"name": "María Dolores Pascual Martínez",
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"country": "ES",
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"language": "es",
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"age": 50,
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"gender": "female",
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"region": "Principado de Asturias",
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"city_size": "large",
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"education": "secondary",
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"occupation": "cajera de supermercado",
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"last_occupation": null,
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"income_level": "low",
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"household_type": "empty_nester",
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"marital_status": "partnered",
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"children": 2,
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"children_ages": [
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24,
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26
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],
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"housing": "rented",
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"employment_status": "employed",
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"lives_with_parents": false,
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"daily_routine": "María Dolores se levanta a las seis y media, antes que nadie, para tener la casa en orden antes de entrar a turno en el supermercado. Después de fichar, pasa casi siempre por casa de su madre a preparar la comida y…",
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"persona": "María Dolores Pascual Martínez tiene cincuenta años y lleva media vida doblando toallas que nadie le agradece. Es cajera de supermercado en una gran ciudad asturiana, cuida de su madre enferma y de una casa que sostiene…",
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"speaking_style": "Habla con frases medianas, cálidas y con mucha carga emocional contenida; usa muletillas como «mujer», «ya te digo» o «no sé yo» cuando no quiere comprometerse. Su tono es cercano y familiar, pero se vuelve cortante…",
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"hobbies_and_interests": [
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"Cocinar para la familia",
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"Ver series con su pareja",
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"Quedar con amigas del barrio",
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"Pasear por la ciudad",
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"Visitar a sus hijos"
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],
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"skills": [
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"Manejo de caja y cobros",
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"Atención al cliente",
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"Organización del hogar",
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"Cuidado de su madre",
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"Cocina casera"
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],
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"goals": [
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"Cuidar de su madre sin descuidar su propia salud.",
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"Mantener el contacto con sus hijos y verlos más a menudo.",
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"Encontrar tiempo para sí misma y descansar mejor."
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],
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"consumer": {
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"shopping_style": "habitual",
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"price_sensitivity": "high",
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"brand_loyalty": "medium",
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"sustainability_concern": "low",
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"tech_adoption": "mainstream"
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},
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"personality": {
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"big_five": {
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"O": 0.4,
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"C": 0.67,
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"E": 0.46,
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"A": 0.7,
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"N": 0.84
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},
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"attachment": {
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"anxiety": 0.62,
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"avoidance": 0.23
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}
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}
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}
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```
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## Fields
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| Field | Type | Description |
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|---|---|---|
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| `id` | string | Country code + number, e.g. `es-00001` |
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| `name` | string | Full name. Two surnames in Spain, Mexico and Brazil, as is customary there |
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| `country` | string | `ES`, `MX`, `US`, `GB`, `DE`, `FR`, `IT`, `BR` |
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| `language` | string | Language of all text fields: `es`, `en`, `de`, `fr`, `it`, `pt` |
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| `age` | int | 18 to 85 |
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| `gender` | string | `male`, `female`, `non_binary` |
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| `region` | string | Region within the country (Brazil: macro-region) |
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| `city_size` | string | `rural`, `small`, `medium`, `large`, `metro` |
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| `education` | string | `secondary`, `vocational`, `university`, `postgrad` |
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| `occupation` | string \| null | Current occupation, in the person's language. `null` if not working |
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| `last_occupation` | string \| null | Last occupation of people who are retired, unemployed, studying or at home |
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| `income_level` | string | `low`, `medium`, `high`, relative to the country |
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| `household_type` | string | `single`, `couple`, `family_young`, `family_teen`, `empty_nester` |
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| `marital_status` | string | `single`, `partnered`, `married`, `divorced`, `widowed` |
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| `children` | int | Number of children |
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| `children_ages` | list[int] | Ages of the children |
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| `housing` | string | `owned`, `rented`, `family_home` |
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| `employment_status` | string | `employed`, `unemployed`, `student`, `retired`, `homemaker`, `other` |
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| `lives_with_parents` | bool \| null | Whether the person lives with their parents (not provided for Brazil) |
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| `daily_routine` | string | A typical day, in the person's language |
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| `persona` | string | Biography, in the person's language |
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| `speaking_style` | string | How the person talks: tone, length, vocabulary, habits, in the person's language |
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| `hobbies_and_interests` | list[string] | Five hobbies and interests |
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| `skills` | list[string] | Three to six skills |
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| `goals` | list[string] | One to three goals for the coming years |
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| `consumer` | object | `shopping_style`, `price_sensitivity`, `brand_loyalty`, `sustainability_concern`, `tech_adoption` |
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| `personality.big_five` | object | `O` openness, `C` conscientiousness, `E` extraversion, `A` agreeableness, `N` neuroticism, 0 to 1 |
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| `personality.attachment` | object | Adult attachment `anxiety` and `avoidance`, 0 to 1 |
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## Load it
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```python
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from datasets import load_dataset
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people = load_dataset("StrataSynth/stratasynth-personas-spain", split="train")
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```
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## Want more?
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These people come from StrataSynth's Humans Engine. If you need people for a specific segment, or want to interview them, write to **hello@stratasynth.com**.
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## Notes
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- Every person is fully synthetic. They are not records of real people or of any institution, and any resemblance to a real person is coincidental.
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- Names are drawn from real name frequencies, so a synthetic person may share a name with real people.
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## License
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CC BY 4.0. Please credit **StrataSynth** when you use it.
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img/es-age.png
ADDED
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Git LFS Details
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img/es-employment.png
ADDED
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Git LFS Details
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img/es-household.png
ADDED
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Git LFS Details
|
personas_es.jsonl
ADDED
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The diff for this file is too large to render.
See raw diff
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