from nameprediction import NamePredictor

predictor = NamePredictor.from_pretrained(
    repo_id="romor/nameprediction",
    filename="name_gender_country_model_v14.pth",
)

print(predictor.predict_name("Ada Lovelace").to_dict())

The package also works with the built-in defaults:

python

from nameprediction import NamePredictor

predictor = NamePredictor.from_pretrained()
print(predictor.predict_name("Ada Lovelace").to_dict())

Model Summary

Architecture: byte-level transformer over UTF-8 encoded names
Tasks: multi-task prediction of gender, country, and region
Checkpoint file: name_gender_country_model_v14.pth
Package default model source: romor/nameprediction

Intended Use

This model is intended for large-scale name-based inference workflows where approximate predictions are acceptable, for example exploratory analysis or enrichment pipelines.

Limitations

Predictions are probabilistic and can be wrong, especially for rare, multicultural, transliterated, or ambiguous names.
Name-based gender inference is inherently imperfect and may not reflect a person's self-identified gender.
Country and region predictions are weak proxies inferred from names only, not verified demographic facts.
The model should not be used as the sole basis for decisions that affect people.

Safety And Bias

This model may reflect biases in the training data and in the simplifying assumption that names carry reliable signals about gender, country, or region. Use caution when applying it to sensitive populations or downstream decision-making systems.

Package Repository

PyPI: nameprediction
GitHub: https://github.com/tomthe/nameprediction