Naamkaran

Naamkaran is a character-level LSTM that generates candidate names conditioned on a starting character and a binary gender input. The Python package exposes generation controls for the ending character, maximum length, sample count, and softmax temperature.

Files

• naamkaran.pt: PyTorch state dictionary for the generator.
• vocabulary.parquet: ordered character vocabulary with one non-null string column named token.

The vocabulary is Parquet rather than a serialized scikit-learn vectorizer. This makes the token order explicit and removes version-sensitive pickle loading from inference.

Training data

The model was trained on names from the early 2022 Florida voter registration data. The package documentation cites the source dataset at https://doi.org/10.7910/DVN/UBIG3F.

Intended use

Use this model to generate synthetic name-like strings for demonstrations, testing, and exploratory applications. It is not a source of verified personal names or demographic labels.

Limitations

The training population and collection period constrain what the model learns. Generated strings can reproduce spelling patterns, imbalance, errors, and social biases present in the voter data. The binary gender conditioning does not represent the full range of gender identities. Do not use generated names to infer identity, ethnicity, citizenship, eligibility, or other sensitive attributes, and do not treat them as representative population samples.

Reproducible loading

The naamkaran package pins this repository to a full commit SHA. Downstream code should also pass a full commit SHA as revision when downloading files.