The TWINNY.AI Personas Dataset is a synthetic collection of 400 richly structured professional personas, engineered to power behavioral AI twins, persona-driven language model fine-tuning, and professional simulation systems.

Each persona is built from 14 attributes spanning demographics, professional context, behavioral psychology, and communication style sampled with realistic non-uniform distributions that mirror actual workforce demographics rather than uniform random sampling.

This dataset is the core training resource for the TWINNY.AI project: a system designed to create AI behavioral twins that authentically reflect human professional archetypes across industries, seniority levels, and cultural contexts.

Dataset at a Glance

Property	Value
Total personas	400
Attributes per persona	14
Industries covered	13
Cultural contexts	7
Role levels	6
Age ranges	6
Behavioral scores	7 (scale 1–5)
Generation seed	`42` (fully reproducible)
License	MIT
Formats	CSV, JSONL, JSON

Dataset Structure

TwinnyAI-Personas-Dataset/
├── TwinnyAI_final_dataset.csv     ← Complete validated dataset (primary file)
├── TwinnyAI_train.jsonl           ← Training split, JSONL format (LLM-ready)
├── personas.csv                   ← Raw persona profiles
└── personas.json                  ← Full JSON objects with nested structure

Schema — Field Reference

Field	Type	Description	Example
`persona_id`	int	Unique persona identifier	`1`
`age_range`	string	Age bracket	`"29–35"`
`industry`	string	Professional industry	`"Technology"`
`role_level`	string	Seniority level	`"Senior"`
`cultural_context`	string	Cultural background	`"North American"`
`communication_style`	string	Primary communication mode	`"detailed and analytical"`
`risk_tolerance_score`	int (1–5)	Appetite for uncertainty and bold decisions	`4`
`approval_threshold_score`	int (1–5)	Need for external validation before acting	`2`
`formality_score`	int (1–5)	Preference for formal vs. informal interaction	`4`
`conciseness_score`	int (1–5)	Tendency toward brief vs. elaborate responses	`3`
`confidence_score`	int (1–5)	Self-assurance in decisions and communication	`4`
`decision_speed_score`	int (1–5)	Speed of reaching conclusions	`3`
`ai_trust_score`	int (1–5)	Openness to AI-assisted decision-making	`2`
`delegation_preference`	bool	Comfort with delegating tasks to others	`FALSE`
`created_at`	ISO 8601	Generation timestamp	`"2026-02-27T13:06:40"`

Sample Record

{
  "persona_id": 1,
  "age_range": "29–35",
  "industry": "Technology",
  "role_level": "Senior",
  "cultural_context": "North American",
  "communication_style": "detailed and analytical",
  "risk_tolerance_score": 4,
  "approval_threshold_score": 2,
  "formality_score": 4,
  "conciseness_score": 3,
  "confidence_score": 4,
  "decision_speed_score": 3,
  "ai_trust_score": 2,
  "delegation_preference": false,
  "created_at": "2026-02-27T13:06:40.273465"
}

Distribution Statistics

All statistics below are computed directly from the 400-persona dataset.

🏭 Industry Distribution

Industry	Count	%
Technology	72	18.0%	`████████████████████`
Finance	57	14.2%	`████████████████`
Healthcare	40	10.0%	`███████████`
Consulting	37	9.2%	`██████████`
Legal	28	7.0%	`████████`
Operations	28	7.0%	`████████`
Marketing	27	6.8%	`███████`
Education	24	6.0%	`███████`
Media	22	5.5%	`██████`
Government	19	4.8%	`█████`
Retail	17	4.2%	`█████`
Manufacturing	16	4.0%	`████`
Real Estate	13	3.2%	`████`

🎯 Role Level Distribution

Role Level	Count	%
Mid-level	119	29.8%	`████████████████████████████████`
Senior	101	25.2%	`████████████████████████████`
Junior	80	20.0%	`██████████████████████`
Manager	55	13.8%	`███████████████`
Director	28	7.0%	`████████`
Executive	17	4.2%	`█████`

🎂 Age Range Distribution

Age Range	Count	%
29–35	101	25.2%	`████████████████████████████`
36–42	89	22.2%	`█████████████████████████`
43–50	81	20.2%	`██████████████████████`
22–28	59	14.8%	`████████████████`
51–58	46	11.5%	`█████████████`
59–65	24	6.0%	`███████`

🌍 Cultural Context Distribution

Cultural Context	Count	%
North American	121	30.2%	`████████████████████████████████`
Western European	80	20.0%	`██████████████████████`
East Asian	60	15.0%	`████████████████`
South Asian	47	11.8%	`█████████████`
Middle Eastern	32	8.0%	`█████████`
Latin American	32	8.0%	`█████████`
African	28	7.0%	`████████`

💬 Communication Style Distribution

Style	Count	%
empathetic and consultative	50	12.5%
detailed and analytical	46	11.5%
data-driven	46	11.5%
diplomatic and cautious	45	11.2%
formal and structured	44	11.0%
direct and concise	42	10.5%
warm and collaborative	42	10.5%
technical and precise	38	9.5%
narrative-focused	30	7.5%
assertive and decisive	30	7.5%

📊 Behavioral Score Summary (scale 1–5)

Score Field	Mean	Std	Min	Max	Interpretation
`risk_tolerance_score`	3.24	~1.1	1	5	Slight risk appetite
`approval_threshold_score`	3.18	~1.1	1	5	Moderate validation need
`ai_trust_score`	3.14	~1.1	1	5	Mild AI openness
`formality_score`	3.05	~1.2	1	5	Balanced formality
`conciseness_score`	3.02	~1.0	1	5	Balanced length
`decision_speed_score`	2.99	~1.0	1	5	Moderate deliberation
`confidence_score`	2.91	~1.1	1	5	Slightly below-neutral (realistic)

Scores follow Gaussian distributions centered at 3.0 with σ ≈ 1.0–1.2, with role-level biases applied via the pipeline's ROLE_RISK_BIAS and ROLE_APPROVAL_BIAS lookup tables.

🤝 Delegation Preference

Preference	Count	%
Comfortable delegating (`TRUE`)	~218	54.5%
Prefers direct control (`FALSE`)	~182	45.5%

Generated via random.random() > 0.45 - approximately 55% of personas prefer delegation.

Behavioral Trait Correlations

The dataset encodes realistic role-level behavioral biases, not random sampling:

Role	Risk Tolerance Bias	Approval Need Bias	Confidence Bias
Junior	−1 to 0 (cautious)	+1 to +2 (high)	−1 to 0
Mid-level	0 (neutral)	0 to +1	−1 to 0
Senior	0 to +1 (bold)	−1 to 0 (low)	0 to +1
Manager	−1 to +1 (spread)	−1 to +1	0 to +1
Director	0 to +1	−2 to 0	0 to +1
Executive	+1 to +2 (high)	−2 to −1 (very low)	0 to +1

This means an Executive persona statistically shows high risk tolerance + low approval seeking, matching real-world behavioral research, while a Junior persona trends toward caution and external validation.

Usage Examples

Load with 🤗 Datasets

from datasets import load_dataset

ds = load_dataset("Mostafa190/TwinnyAI-Personas-Dataset")
print(ds)
# DatasetDict({
#     train: Dataset({
#         features: ['persona_id', 'age_range', 'industry', ...],
#         num_rows: 400
#     })
# })
print(ds["train"][0])

Load and explore with pandas

import pandas as pd

df = pd.read_csv(
    "hf://datasets/Mostafa190/TwinnyAI-Personas-Dataset/TwinnyAI_final_dataset.csv"
)

print(df.shape)        # (400, 15)
print(df.describe())   # Score statistics

# Filter: Senior Technology personas who trust AI
tech_seniors = df[
    (df["industry"] == "Technology") &
    (df["role_level"] == "Senior") &
    (df["ai_trust_score"] >= 4)
]
print(f"Matched: {len(tech_seniors)} personas")

Load JSONL for fine-tuning

import json

with open("TwinnyAI_train.jsonl", "r") as f:
    personas = [json.loads(line) for line in f]

p = personas[0]
print(f"#{p['persona_id']}: {p['role_level']} in {p['industry']}")
print(f"Style: {p['communication_style']}")
print(f"Risk: {p['risk_tolerance_score']}/5 | AI Trust: {p['ai_trust_score']}/5")

Generate LLM system prompts

import json, random

with open("personas.json") as f:
    personas = json.load(f)

def to_system_prompt(p):
    return (
        f"You are a {p['age_range']}-year-old {p['role_level']} "
        f"in the {p['industry']} industry ({p['cultural_context']} background). "
        f"Your communication style is {p['communication_style']}. "
        f"Risk tolerance: {p['risk_tolerance_score']}/5. "
        f"Confidence: {p['confidence_score']}/5. "
        f"You {'delegate when possible' if p['delegation_preference'] else 'prefer direct control'}. "
        f"Respond consistently in character."
    )

persona = random.choice(personas)
print(to_system_prompt(persona))

Generation Pipeline

Script	Role
`01_generate_personas.py`	Samples traits with weighted distributions, applies role-level biases, generates 400 personas with `seed=42`
`03_validate_dataset.py`	Schema validation, duplicate removal, score range checks
`04_export_jsonl.py`	Exports to JSONL training format
`groq_generator.py`	Optional: enriches personas with LLM-generated narrative descriptions via Groq API

Fully reproducible: Running generate_personas(n=400, seed=42) produces the exact same 400 personas every time.

Intended Use

LLM fine-tuning : Train models to generate or simulate professional personas
Behavioral AI twins : Power digital twin systems with realistic archetypes
AI agent seeding : Consistent behavioral profiles for role-play agents
Prompt engineering : System prompt templates for persona-conditioned generation
UX research & simulation : Diverse synthetic user profiles for product testing
Academic research : Study trait distributions in synthetic professional populations

Out-of-Scope Use

Impersonating or deceiving real individuals
Building surveillance or profiling systems targeting real people
Generating discriminatory content based on demographic attributes

Ethics & Limitations

Bias transparency : Cultural distributions reflect configurable pipeline priors (e.g., 30% North American). These are adjustable weights, not value judgments
Score design : Gaussian-sampled scores with σ ≈ 1.0–1.2; extremes (1 or 5) exist but are intentionally less frequent
No PII : Zero personally identifiable information in any field

License

Released under the MIT License. Free to use, modify, and distribute with attribution.

Citation

@dataset{twinnyai_personas_2026,
  author       = {Mostafa190},
  title        = {TWINNY.AI Personas Dataset},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Mostafa190/TwinnyAI-Personas-Dataset}},
  license      = {MIT},
  note         = {400 synthetic professional personas, Twinnify pipeline, seed=42}
}

_{Built with ❤️ as part of the TWINNY.AI project · Powered by the 20AI Pipeline}

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