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        "author": "Alexander Engelhardt",
        "author_email": "alexander.w.engelhardt@gmail.com",
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            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Topic :: Text Processing :: Linguistic",
            "Topic :: Utilities"
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        "description": "startup-name-generator\n======================\n\n- `This package on PyPI <https://pypi.org/project/sng/>`_\n- `This package on GitHub <https://github.com/AlexEngelhardt/startup-name-generator>`_\n\nSummary\n-------\n\nThis package can train a model that learns the \"structure\" of the words in a\nsupplied text corpus. It then generates new words with a similar structure,\nwhich can be used as suggestions for naming things like companies or software.\n\nQuickstart\n----------\n\nCheck out the `Jupyter Notebook(s) in doc/notebooks/ <https://startup-name-generator.readthedocs.io/en/latest/notebooks/01_quickstart.html>`_.\n\nDocumentation\n-------------\n\n- The full documentation is `available online <https://startup-name-generator.readthedocs.io/en/latest/>`_\n- I also gave a lightning talk presenting the basic idea, it's available `on Youtube <https://www.youtube.com/watch?v=1w3Q3CEldG0>`_.\n\nExtended summary\n----------------\n\nNaming a startup is `hard <https://mashable.com/2012/10/04/startup-naming/>`_.\n\nI therefore wrote a Python package to randomly generate company name ideas.\n\nIt takes an arbitrary text as input, and then trains a recurrent neural network\n(RNN) on each its words, learning the structure of the text. The input text can\nbe a simple word list (e.g. Greek or Gallic), or a chapter from a book, or just\na random list of words (e.g. all Pokemon). The script then generates new random\nnames that sound simliar to the provided list.\n\nLiterature/References\n---------------------\n\n- `Andrew Ng's Deep Learning MOOC <https://www.deeplearning.ai/>`_\n- http://karpathy.github.io/2015/05/21/rnn-effectiveness/\n- https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py\n\n\n\n",
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        "license": "MIT",
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        "name": "sng",
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        "summary": "Generate name proposals for companies, software, etc.",
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