persona-data 0.5.0

Shared dataset loading and prompt formatting for implicit-personalization projects

persona-data

Shared dataset loading, prompt formatting, and environment utilities for the implicit-personalization projects.

Overview

persona-data provides the common dataset and prompt helpers used across the persona projects:

• SynthPersonaDataset for persona profiles plus QA pairs
• PersonaGuessDataset for turn-based persona games
• NemotronPersonasFranceDataset for French persona profiles from NVIDIA
• NemotronPersonasUSADataset for US persona profiles from NVIDIA
• prompt helpers for roleplay and multiple-choice evaluation
• environment helpers for seeds, devices, and artifact paths

Installation

Add as a uv git source in your project's pyproject.toml:

[project]
dependencies = ["persona-data"]

[tool.uv.sources]
persona-data = { git = "ssh://[e-mail]/implicit-personalization/persona-data.git" }

Then run uv sync.

For local development alongside other repos, use an editable path source:

[tool.uv.sources]
persona-data = { path = "../persona-data", editable = true }

Testing

uv run --with pytest pytest tests/test_datasets.py

The release workflow also runs tests/smoke_test.py against the built wheel and source distribution.

Package layout

src/persona_data/
├── __init__.py
├── synth_persona.py       # SynthPersonaDataset, PersonaDataset, PersonaData, QAPair, Statement
├── persona_guess.py       # PersonaGuessDataset, GameRecord, Turn
├── nemotron_personas.py   # NemotronPersonasFranceDataset, NemotronPersonasUSADataset
├── prompts.py             # format_prompt, format_mc_question, format_messages
└── environment.py         # set_seed, get_device, get_artifacts_dir

Datasets

Each dataset is a module with its own types and a loader that downloads from Hugging Face, cached via HF_HOME.

SynthPersona

from persona_data.synth_persona import SynthPersonaDataset

dataset = SynthPersonaDataset()

persona = dataset[0]
persona.name              # "Ethan Robinson"

qa_pairs = dataset.get_qa(persona.id, type="implicit", item_type="mcq")

# Leakage-aware split: train on individual FRQs, test on shared MCQs.
train_qa, test_qa = dataset.train_test_split(persona.id)

# Optional cap if you want a smaller train slice:
# train_qa, test_qa = dataset.train_test_split(persona.id, n_train=50)

PersonaGuess

from persona_data.persona_guess import PersonaGuessDataset

games = PersonaGuessDataset()
game = games[0]
turns = games.get_qa(game.game_id, player="A")

Prompt formatting

from persona_data.prompts import format_messages, format_prompt

system_prompt = format_prompt(persona, "biography")

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Where did you grow up?"},
    {"role": "assistant", "content": "I grew up in Little Rock, Arkansas."},
]
full_prompt, response_start_idx = format_messages(messages, tokenizer)

format_prompt accepts a PersonaData plus one of the standard variants ("templated" or "biography"), or raw profile text. It also accepts mode="roleplay" (default) and mode="conversational".

The persona-less Assistant baseline is just another persona in the dataset under BASELINE_PERSONA_ID ("baseline_assistant"). It appears in normal iteration when loaded, and dataset.baseline retrieves it directly:

dataset = SynthPersonaDataset()
baseline = dataset.baseline  # PersonaData | None
system_prompt = format_prompt(baseline, "templated")

Use BASELINE_PERSONA_ID and BASELINE_PERSONA_NAME from persona_data.synth_persona for artifact naming and UI labels.

For multiple-choice prompts, use format_mc_question(qa) to render the question, choices, and trailing answer-only instruction. Use mc_answer_only_instruction(n_choices) if you need just the instruction text, and mc_correct_letter(qa) to get the gold label.

format_messages handles tokenizers that do not support the "system" role (for example Gemma 2) by merging system content into the first user message. Pass add_generation_prompt=True to render an inference-ready prompt (messages ending in a user turn); the returned response_start_idx then equals the prompt length, ready to slice model.generate output.

Environment helpers

from persona_data.environment import set_seed, get_device, get_artifacts_dir

set_seed(1337)        # sets random, numpy, and torch seeds
device = get_device() # cuda > mps > cpu

Used by

• persona-vectors — activation extraction and steering
• cues_attribution — section-level ablation attribution
• persona-2-lora — LoRA-based persona internalization