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        "description": "# sklearn2gem\n\n[![Build Status](https://travis-ci.org/stewartpark/sklearn2gem.svg?branch=master)](https://travis-ci.org/stewartpark/sklearn2gem)\n[![Requirements Status](https://requires.io/github/stewartpark/sklearn2gem/requirements.svg?branch=master)](https://requires.io/github/stewartpark/sklearn2gem/requirements/?branch=master)\n[![PyPI version](https://badge.fury.io/py/sklearn2gem.svg)](https://badge.fury.io/py/sklearn2gem)\n\n\u26a1 sklearn2gem ports your scikit-learn model into a fast ruby C binding!\n\n# Getting started\n\nInstall sklearn2gem using `pip`:\n\n```\npip install sklearn2gem\n```\n\nor via `easy_install`:\n\n```\neasy_install sklearn2gem\n```\n\nAfter that, dump your scikit-learn model with `sklearn.externals.joblib`, and run `sklearn2gem model_name@version your_model.pkl foo/bar/model_name`. You should be able to see a newly created folder named `model_name` under `foo/bar/`.\n\nSee [`examples/iris.py`](https://github.com/stewartpark/sklearn2gem/blob/master/examples/iris.py) to try it out.\n\nTo produce a pre-compiled binary gem, use [gem-compiler](https://github.com/luislavena/gem-compiler).\n\n# What machine learning algorithms are supported?\n\nSince sklearn2gem uses `nok/sklearn-porter` to convert a model into a C file, you can refer to [this page](https://github.com/nok/sklearn-porter#machine-learning-algorithms).",
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