text: from setuptools import setup, find_packages
  import os
  # Utility function to read the README file.
  # Used for the long_description.  It's nice, because now 1) we have a top level
  # README file and 2) it's easier to type in the README file than to put a raw
  # string in below ...
  def read(fname):
      return open(os.path.join(os.path.dirname(__file__), fname)).read()
  
  setup(
    name = 'indicLP',         # How you named your package folder (MyLib)
    packages = find_packages(),   # Chose the same as "name"
    version = '0.1.1',      # Start with a small number and increase it with every change you make
    license='MIT',        # Chose a license from here: https://help.github.com/articles/licensing-a-repository
    description = 'indicLP is python package developed specifically to perform NLP tasks on Indic Languages like Hindi and Tamil, to make the world of AI a more inclusive space.',   # Give a short description about your library
    author = 'Aakash Ezhilan & Adityan Sunil Kumar',                   # Type in your name
    author_email = 'aakash.ezhilan@gmail.com',      # Type in your E-Mail
    url = 'https://github.com/Aakash-Ez/indicLP',   # Provide either the link to your github or to your website
    download_url = 'https://github.com/Aakash-Ez/indicLP/archive/refs/tags/v0.1.1.tar.gz',    # I explain this later on
    #long_description=read('README.rst'),
    include_package_data=True,
    package_data={
      "assets":["assets/embed_models/word2vec.hi.model","assets/embed_models/word2vec.ta.model",
      "assets/stopwords/hi/stopwords.pkl",
      "assets/stopwords/ta/stopwords.pkl",
      "assets/tokenizer_models/hi/token.model",
      "assets/tokenizer_models/ta/token.model"],
    },
    keywords = ['Indic Natural Language Processing', 'Natural Language Processing', 'NLP', 'Hindi', 'Tamil', 'PyTorch'],   # Keywords that define your package best
    install_requires=[            # I get to this in a second
            'torch>=1.9.1',
            'torchtext>=0.10.1',
            'gensim>=4.0.0',
            'numpy>=1.19.1',
            'indic_transliteration',
            'snowballstemmer'
        ],
    classifiers=[
      'Development Status :: 3 - Alpha',      # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package
      'Intended Audience :: Developers',      # Define that your audience are developers
      'Topic :: Scientific/Engineering :: Artificial Intelligence',
      'Intended Audience :: Science/Research',
      'Natural Language :: Tamil',
      'Natural Language :: Hindi',
      'License :: OSI Approved :: MIT License',   # Again, pick a license
      'Programming Language :: Python :: 3.6',
      'Programming Language :: Python :: 3.7',
      'Programming Language :: Python :: 3.8',
      'Programming Language :: Python :: 3.9'
    ],
  )