nameprediction 0.1.1

Predict gender, country, and region from names using a byte-level transformer.

nameprediction

nameprediction is a Python package for predicting gender, country, and region from personal names using a byte-level transformer.

The repository contains both:

• the reusable inference package under src/nameprediction
• the original training and local inference scripts used to produce checkpoints

Install

For local development:

pip install -e .

For users after publication:

pip install nameprediction

Quick Start

1. Download a model from Hugging Face

from nameprediction import download_model

model_path = download_model()

The same flow is available from the CLI:

nameprediction-download-model

By default this downloads from romor/nameprediction and fetches name_gender_country_model_v14.pth.

You can override the source explicitly if needed:

model_path = download_model(repo_id="romor/nameprediction")

Direct model URLs also work:

model_path = download_model("https://example.com/name_gender_country_model_v14.pth")

2. Load the predictor

from nameprediction import NamePredictor

predictor = NamePredictor.from_pretrained()

If you already downloaded the model and want to point at a specific local path, use:

predictor = NamePredictor.from_pretrained(
   model_path=model_path,
)

Predict Single Names

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

predict_name returns a NamePrediction dataclass with these fields:

• name
• predicted_gender
• f_prob
• predicted_country
• predicted_country_confidence
• predicted_region
• predicted_region_confidence

Predict Lists Of Names

results = predictor.predict_names([
   "Ada Lovelace",
   "Alan Turing",
   "Grace Hopper",
])

You can also use the convenience function:

from nameprediction import predict_names

results = predict_names(["Ada Lovelace", "Alan Turing"])

Predict DataFrames

import pandas as pd
from nameprediction import predict_dataframe

df = pd.DataFrame({"name": ["Ada Lovelace", "Alan Turing"]})

predicted = predict_dataframe(
   df,
   name_col="name",
   column_suffix="_v15",
)

This appends:

• predicted_gender_v15
• f_prob_v15
• predicted_country_v15
• predicted_country_confidence_v15
• predicted_region_v15
• predicted_region_confidence_v15

Package Notes

• The package derives model architecture settings such as embed_dim, num_heads, num_layers, and max_len from the checkpoint when available.
• Label encoders are bundled with the package and validated against the checkpoint class counts at load time.
• The default published model source is romor/nameprediction with filename name_gender_country_model_v14.pth.
• You can override the default model source with repo_id=..., filename=..., or the environment variables NAMEPREDICTION_HF_REPO_ID, NAMEPREDICTION_MODEL_FILENAME, and NAMEPREDICTION_MODEL_REVISION.

Repository Layout

• src/nameprediction: package code intended for PyPI users
• train.py: original training script
• inference.py: legacy local inference helper script
• config.yaml: local training and inference configuration

Publishing

Maintainer instructions for PyPI and Hugging Face are in PUBLISHING.md.