| --- |
| 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. |
|
|
