Metadata-Version: 2.1
Name: snorkel
Version: 0.9.3
Summary: A system for quickly generating training data with weak supervision
Home-page: https://github.com/snorkel-team/snorkel
License: Apache License 2.0
Project-URL: Homepage, https://snorkel.org
Project-URL: Source, https://github.com/snorkel-team/snorkel/
Project-URL: Bug Reports, https://github.com/snorkel-team/snorkel/issues
Project-URL: Citation, https://doi.org/10.14778/3157794.3157797
Description: <img src="figs/logo_01.png" width="150"/>
        
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/snorkel)
        ![PyPI](https://img.shields.io/pypi/v/snorkel)
        ![Conda](https://img.shields.io/conda/v/conda-forge/snorkel)
        [![build](https://travis-ci.com/snorkel-team/snorkel.svg?branch=master)](https://travis-ci.com/snorkel-team/snorkel?branch=master)
        [![docs](https://readthedocs.org/projects/snorkel/badge/?version=master)](https://snorkel.readthedocs.io/en/master)
        [![coverage](https://codecov.io/gh/snorkel-team/snorkel/branch/master/graph/badge.svg)](https://codecov.io/gh/snorkel-team/snorkel/branch/master)
        [![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        
        ***Programmatically Build and Manage Training Data***
        
        # Quick Links
        * [Snorkel website](https://snorkel.org)
        * [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials)
        * [Snorkel documentation](https://snorkel.readthedocs.io/)
        * [Snorkel community forum](https://spectrum.chat/snorkel)
        * [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml)
        * [Snorkel Twitter](https://twitter.com/SnorkelML)
        
        # Getting Started
        The quickest way to familiarize yourself with the Snorkel library is to walk through the [Get Started](https://snorkel.org/get-started/) page on the Snorkel website, followed by the full-length tutorials in the [Snorkel tutorials](https://github.com/snorkel-team/snorkel-tutorials) repository.
        These tutorials demonstrate a variety of tasks, domains, labeling techniques, and integrations that can serve as templates as you apply Snorkel to your own applications.
        
        
        # Installation
        
        To install Snorkel, we recommend using `pip`:
        
        ```bash
        pip install snorkel
        ```
        
        or `conda`:
        
        ```bash
        conda install snorkel -c conda-forge
        ```
        
        For information on installing from source and contributing to Snorkel, see our
        [contributing guidelines](./CONTRIBUTING.md).
        
        <details><summary><b>Details on installing with <tt>conda</tt></b></summary>
        <p>
        
        The following example commands give some more color on installing with `conda`.
        These commands assume that your `conda` installation is Python 3.6,
        and that you want to use a virtual environment called `snorkel-env`.
        
        ```bash
        # [OPTIONAL] Activate a virtual environment called "snorkel"
        conda create --yes -n snorkel-env python=3.6
        conda activate snorkel-env
        
        # We specify PyTorch here to ensure compatibility, but it may not be necessary.
        conda install pytorch==1.1.0 -c pytorch
        conda install snorkel==0.9.0 -c conda-forge
        ```
        
        </p>
        </details>
        
        <details><summary><b>A quick note for Windows users</b></summary>
        <p>
        
        If you're using Windows, we highly recommend using Docker
        (you can find an example in our
        [tutorials repo](https://github.com/snorkel-team/snorkel-tutorials/blob/master/Dockerfile))
        or the [Linux subsystem](https://docs.microsoft.com/en-us/windows/wsl/faq).
        We've done limited testing on Windows, so if you want to contribute instructions
        or improvements, feel free to open a PR!
        
        </p>
        </details>
        
        # Discussion
        
        ## Issues
        We use [GitHub Issues](https://github.com/snorkel-team/snorkel/issues) for posting bugs and feature requests — anything code-related.
        Just make sure you search for related issues first and use our Issues templates.
        We may ask for contributions if a prompt fix doesn't fit into the immediate roadmap of the core development team.
        
        ## Contributions
        We welcome contributions from the Snorkel community! 
        This is likely the fastest way to get a change you'd like to see into the library.
        
        Small contributions can be made directly in a pull request (PR).
        If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. 
        For ideas about what to work on, we've labeled specific issues as [`help wanted`](https://github.com/snorkel-team/snorkel/issues?utf8=%E2%9C%93&q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22+).
        
        To set up a development environment for contributing back to Snorkel, see our [contributing guidelines](./CONTRIBUTING.md).
        All PRs must pass the continuous integration tests and receive approval from a member of the Snorkel development team before they will be merged.
        
        ## Community Forum
        For broader Q&A, discussions about using Snorkel, tutorial requests, etc., use the [Snorkel community forum](https://spectrum.chat/snorkel) hosted on Spectrum.
        We hope this will be a venue for you to interact with other Snorkel users — please don't be shy about posting!
        
        ## Announcements
        To stay up-to-date on Snorkel-related announcements (e.g. version releases, upcoming workshops), subscribe to the [Snorkel mailing list](https://groups.google.com/forum/#!forum/snorkel-ml). We promise to respect your inboxes — communication will be sparse!
        
        ## Twitter
        Follow us on Twitter [@SnorkelML](https://twitter.com/SnorkelML).
        
Keywords: machine-learning ai weak-supervision
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
