naamkaran 0.2.0

Generative model for names.

naamkaran: generative model for names

Naamkaran is a generative model for names. It is based on a character-level RNN trained on names from the Florida Voter Registration Data.

Gradio App.

Naamkaran on HF

Installation

Naamkaran can be installed from PyPI using pip:

pip install naamkaran

For development with all tools:

pip install -e ".[dev,test]"

For web applications (Gradio/Flask):

pip install "naamkaran[web]"

General API

The general API for naamkaran is as follows:

# naamkaran is the package name
from naamkaran.generate import generate_names

# generate_names is the function that generates names

positional arguments:
  start_letter  The letter to start the name with (default: "a")

optional arguments:
    end_letter  The letter to end the name with (default: None)
    how_many    The number of names to generate (default: 1)
    max_length  The maximum length of the name (default: 5)
    gender      The gender of the name (default: "M")
    temperature The temperature of the model (default: 0.5)

# generate 10 names starting with 'A'
generate_names('A', how_many=10)
['Allis', 'Alber', 'Aderi', 'Albri', 'Alawa',
'Arver', 'Agnee', 'Anous', 'Areyd', 'Adria']


# generate 10 names starting with 'B' and ending with 'n'
generate_names('B', end_letter='n', how_many=10)
['Brian', 'Beran', 'Burin', 'Bahan', 'Balin',
'Bounn', 'Baran', 'Balan', 'Belin', 'Brion']

# generate 5 names starting with 'B' and ending with 'n' with a maximum length of 4
generate_names('B', end_letter='n', how_many=5, max_length=4)
['Bern', 'Bren', 'Bran', 'Bonn', 'Brun']

# generate 10 names starting with 'D' and ending with 'd' with a maximum length of 6
# and a temperature of 0.5
generate_names('D', end_letter='d', how_many=5, max_length=6, temperature=0.5)
['Derayd', 'Davind', 'Deland', 'Denild', 'David']

# generate 10 female names starting with 'A' and ending with 'e' with a maximum length of 5
# and a temperature of 0.5
generate_names('A', end_letter='e', how_many=10, max_length=5, gender="F", temperature=0.5)
['Annhe', 'Annie', 'Altre', 'Anne', 'Ashle',
'Arine', 'Anice', 'Andre', 'Anale', 'Allie']

Data

The model is trained on names from the Florida Voter Registration Data from early 2022. The data are available on the Harvard Dataverse

Authors

Rajashekar Chintalapati and Gaurav Sood

Contributing

Contributions are welcome. Please open an issue if you find a bug or have a feature request.

License

The package is released under the MIT License.