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            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
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        "description": "============\nSklearn Plus\n============\n\n.. image:: https://img.shields.io/pypi/v/sklearn_plus.svg\n    :target: https://pypi.python.org/pypi/sklearn_plus\n\n.. image:: https://api.travis-ci.org/ybbaigo/sklearn-plus.svg\n    :target: https://travis-ci.org/ybbaigo/sklearn_plus\n\n.. image:: https://readthedocs.org/projects/sklearn-plus/badge/?version=latest\n    :target: https://sklearn-plus.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\n    A set of python modules for Natural Language Processing\n\n\n    * Free software: MIT license\n    * Documentation: https://sklearn-plus.readthedocs.io.\n\n\nSklearn plus\n----------------\n\nSklearn plus is an end-to-end neural network API, written in Python. Currently we focus on Natural Language Processing(NLP) tasks. It is based on Tensoflow_ and scikit-learn_. It assembles many preprocessing function, utils and deep learning models about NLP tasks.\n\nIt was developed with a focus on expanding sklearn and making using deep learning model to handle NLP tasks easier.\n\nFeatures\n----------------\n\n**End-To-End**. Sklearn plus is based on Tensorflow and implements many classical models. Also it offers easy and consistent API with sklearn style for specific NLP task.\n\n**Easy extensibility**. Sklearn plus inherits sklearn base classes and follows sklearn API design principles. It is easy to assemble new models, preprocessing functions and utils in it.\n\n.. _Tensoflow: https://www.tensorflow.org/\n.. _scikit-learn: http://scikit-learn.org/stable/\n\n\nQuick Start\n----------------\n\n* TODO\n\nInstallation\n----------------\n\nThere are two ways to install Sklearn plus:\n\n1. **Install sklearn plus from PyPI(recommended)**:\n\n::\n\n  sudo pip install sklearn-plus\n\nIf you are using a virtualenv, you may want to avoid using sudo:\n\n::\n\n  pip install sklearn-plus\n\n2. **Install sklearn plus from the GitHub source:**\n\n::\n\n  git clone https://github.com/ybbaigo/sklearn-plus.git\n  cd sklearn-plus\n  sudo python setup.py install\n\n\nContributing pull requests\n--------------------------------\n\nHere's a quick guide to submitting your improvements:\n\n1. **Write the code.** There are three base modules in sklearn plus: preprocess, utils and nerual_network. Write your code in the three modules and reference to the samples in them. We use PEP8 syntax conventions.\n2. **Write the docstrings.** Make sure any code you touch still has up-to-date docstrings and documentation. Please follow the numpydoc_style_.\n\n3. **Write tests.** Your code should have full unit test coverage. If you want to see your PR merged promptly, this is crucial.\n\n4. **Make sure all tests are passing.** Make sure your code tests can pass on Python 2.7 and Python 3.6 with Tensorflow 1.1.0.\n\n5. **Make sure that your PR does not add PEP8 violations.** Make sure that your PR does not add PEP8 violations you can check it by flake8_:\n\n  * install flake8: ```pip install flake8```\n  * check: ```flake8 path/to/code/```\n\n6. **Commit, use appropriate, descriptive commit messages.**\n\n7. **Submit your PR.** If you have complete (and passing) unit tests as well as proper docstrings/documentation, your PR is likely to be merged promptly.\n\n.. _numpydoc_style: https://numpydoc.readthedocs.io/en/latest/format.html#overview\n.. _flake8: http://flake8.pycqa.org/en/latest/index.html#quickstart)\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n",
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