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README file template for documenting Machine Learning and Data Science projects.

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README Template

A README template to jumpstart your projects!
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Roadmap
  5. Contributing
  6. License
  7. Authors
  8. Acknowledgements

About The Project

In this section you should describe your project, including any datasets you used and appropriate citations. You may refer to your project report or cite your paper for more detailed information.

Here goes the title with hyperlink

You can include tables or images to summarize your results when and if appropriate.

Getting Started

In this section you should provide instructions on how to use this repository to recreate your project locally.

Dependencies

Here, list all libraries, packages and other dependencies that need to be installed to run your project. Include library versions and how they should be installed if a special requirement is needed.

For example, this is how you would list them:

  • Transformers 4.8.0
    conda install -c conda-forge transformers
  • OpenCV 4.5.2
    conda install -c conda-forge opencv

Alternative: Export your Environment

Alternatively, you can export your Python working environment, push it to your project's repository and allow users to clone it locally. This way, anyone can install it and they will have all dependencies needed. Here is how you export a copy of your Python environment:

conda env export > requirements.yml

The user will be able to recreate it using:

conda env create -f requirements.yml

Installation

  1. Clone the repo
    git clone https://github.com/catiaspsilva/README-template.git
  2. Setup (and activate) your environment
conda env create -f requirements.yml

Usage

Use this space to show useful examples of how a project can be used. For course projects, include which file to execute and the format of any input variables.

Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Authors

Your Name - @your_twitter - [email protected]

Project Link: https://github.com/your_username/repo_name

Acknowledgements

You can acknowledge any individual, group, institution or service.

Thank you

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README file template for documenting Machine Learning and Data Science projects.

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