---
license: mit
library_name: pytorch
tags:
  - name-generation
  - character-level
  - lstm
---

# 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.