naamkaran: Generative model for names
-------------------------------------
.. image:: https://github.com/appeler/naamkaran/workflows/test/badge.svg
:target: https://github.com/appeler/naamkaran/actions?query=workflow%3Atest
.. image:: https://img.shields.io/pypi/v/naamkaran.svg
:target: https://pypi.python.org/pypi/naamkaran
.. image:: https://pepy.tech/badge/naamkaran
:target: https://pepy.tech/project/naamkaran
Naamkaran is a generative model for names. It is a simple character-level RNN that predicts
the next character given the previous characters. The model is trained on a list of names from
FL Voter Registration Data and can be used to generate new names.
Installation
------------
Naamkaran can be installed from PyPI using pip:
.. code-block:: bash
pip install naamkaran
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
# it takes a start_letter and a number of names to generate
# generate 10 names starting with 'A'
generate_names('A', how_many=10)
positional arguments:
start_letter The letter to start the name with
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' and ending with 'n'
generate_names('A', end_letter='n', how_many=10)
# generate 10 names starting with 'A' and ending with 'n' with a maximum length of 4
generate_names('A', end_letter='n', how_many=10, max_length=4)
# generate 10 names starting with 'A' and ending with 'n' with a maximum length of 6
# and a temperature of 0.5
generate_names('A', end_letter='n', how_many=5, max_length=6, temperature=0.5)
# generate 10 female names starting with 'A' and ending with 'n' 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)
Data
----
The data used to train the model is from the Florida Voter Registration Data from early 2022.
The data is available here - `Florida voter registration database <http://dx.doi.org/10.7910/DVN/UBIG3F>`__
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 <https://opensource.org/licenses/MIT>`_.
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"description": "naamkaran: Generative model for names\n-------------------------------------\n\n.. image:: https://github.com/appeler/naamkaran/workflows/test/badge.svg\n :target: https://github.com/appeler/naamkaran/actions?query=workflow%3Atest\n.. image:: https://img.shields.io/pypi/v/naamkaran.svg\n :target: https://pypi.python.org/pypi/naamkaran\n.. image:: https://pepy.tech/badge/naamkaran\n :target: https://pepy.tech/project/naamkaran\n\nNaamkaran is a generative model for names. It is a simple character-level RNN that predicts \nthe next character given the previous characters. The model is trained on a list of names from \nFL Voter Registration Data and can be used to generate new names.\n\nInstallation\n------------\n\nNaamkaran can be installed from PyPI using pip:\n\n.. code-block:: bash\n\n pip install naamkaran\n\nGeneral API\n-----------\n\nThe general API for naamkaran is as follows:\n\n:: \n\n # naamkaran is the package name\n from naamkaran.generate import generate_names\n\n # generate_names is the function that generates names\n # it takes a start_letter and a number of names to generate\n\n # generate 10 names starting with 'A'\n generate_names('A', how_many=10)\n\n positional arguments:\n start_letter The letter to start the name with\n\n optional arguments:\n end_letter The letter to end the name with (default: None)\n how_many The number of names to generate (default: 1)\n max_length The maximum length of the name (default: 5)\n gender The gender of the name (default: \"M\")\n temperature The temperature of the model (default: 0.5)\n\n # generate 10 names starting with 'A' and ending with 'n'\n generate_names('A', end_letter='n', how_many=10)\n\n # generate 10 names starting with 'A' and ending with 'n' with a maximum length of 4\n generate_names('A', end_letter='n', how_many=10, max_length=4)\n\n # generate 10 names starting with 'A' and ending with 'n' with a maximum length of 6\n # and a temperature of 0.5\n generate_names('A', end_letter='n', how_many=5, max_length=6, temperature=0.5)\n\n # generate 10 female names starting with 'A' and ending with 'n' with a maximum length of 5\n # and a temperature of 0.5\n generate_names('A', end_letter='e', how_many=10, max_length=5, gender=\"F\", temperature=0.5)\n\n\nData\n----\n\nThe data used to train the model is from the Florida Voter Registration Data from early 2022.\nThe data is available here - `Florida voter registration database <http://dx.doi.org/10.7910/DVN/UBIG3F>`__\n\n\nAuthors\n-------\n\nRajashekar Chintalapati and Gaurav Sood\n\nContributing\n------------\n\nContributions are welcome. Please open an issue if you find a bug or have a feature request.\n\nLicense\n-------\n\nThe package is released under the `MIT License <https://opensource.org/licenses/MIT>`_.\n",
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