Metadata-Version: 2.1
Name: sadedegel
Version: 0.2.1
Summary: Extraction-based Turkish news summarizer.
Home-page: https://github.com/GlobalMaksimum/sadedegel
Author: Global Maksimum AI
Author-email: info@globalmaksimum.com
License: MIT
Description: <a href="http://sadedegel.ai"><img src="https://avatars0.githubusercontent.com/u/2204565?s=280&v=4" width="125" height="125" align="right" /></a>
        
        # SadedeGel: An extraction based Turkish news summarizer
        
        SadedeGel is a library for extraction-based news summarizer using pretrained BERT model.
        Development of the library takes place as a part of [Açık Kaynak Hackathon Programı 2020](https://www.acikhack.com/)
        
        💫 **Version 0.2.1 (Maintanence Release) out now!**
        [Check out the release notes here.](https://github.com/GlobalMaksimum/sadedegel/releases)
        
        
        ## 📖 Documentation
        
        | Documentation   |                                                                |
        | --------------- | -------------------------------------------------------------- |
        | [Contribute]    | How to contribute to the sadedeGel project and code base.          |
        
        [contribute]: https://github.com/GlobalMaksimum/sadedegel/blob/master/CONTRIBUTING.md
        
        ## 💬 Where to ask questions
        
        The SadedeGel project is maintained by [@globalmaksmum](https://github.com/GlobalMaksimum) AI team members
        [@dafajon](https://github.com/dafajon),
        [@askarbozcan](https://github.com/askarbozcan),
        [@mccakir](https://github.com/mccakir) and 
        [@husnusensoy](https://github.com/husnusensoy). 
        
        | Type                     | Platforms                                              |
        | ------------------------ | ------------------------------------------------------ |
        | 🚨 **Bug Reports**       | [GitHub Issue Tracker]                                 |
        | 🎁 **Feature Requests**  | [GitHub Issue Tracker]                                 |
        
        [github issue tracker]: https://github.com/GlobalMaksimum/sadedegel/issues
        
        ## Features
        
        Coming soon...
        
        📖 **For more details, see the
        
        Coming soon...
        
        ## Install sadedeGel
        
        - **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
          Studio)
        - **Python version**: 3.5+ (only 64 bit)
        - **Package managers**: [pip] 
        
        [pip]: https://pypi.org/project/sadedegel/
        
        ### pip
        
        Using pip, sadedeGel releases are available as source packages and binary wheels.
        
        ```bash
        pip install sadedegel
        ```
        
        When using pip it is generally recommended to install packages in a virtual
        environment to avoid modifying system state:
        
        ```bash
        python -m venv .env
        source .env/bin/activate
        pip install sadedegel
        ```
        
        ### conda
        
        Coming soon...
        
        
        ### Quickstart with SadedeGel
        
        To load SadedeGel, use `sadedegel.load()`
        
        ```python
        import sadedegel
        from sadedegel.dataset import load_sentence_corpus, load_raw_corpus
        
        nlp = sadedegel.load()
        tokenized = load_sentence_corpus()
        raw = load_raw_corpus()
        
        summary = nlp(raw[0])
        summary = nlp(tokenized[0], sentence_tokenizer=False)
        ```
        
        ## PyLint, Flake8 and Bandit
        sadedeGel utilized [pylint](https://www.pylint.org/) for static code analysis, 
        [flake8](https://flake8.pycqa.org/en/latest) for code styling and [bandit](https://pypi.org/project/bandit) 
        for code security check.
        
        To run all tests
        
        ```bash
        make lint
        ```
        
        ## Run tests
        
        sadedeGel comes with an [extensive test suite](sadedegel/tests). In order to run the
        tests, you'll usually want to clone the repository and build sadedeGel from source.
        This will also install the required development dependencies and test utilities
        defined in the `requirements.txt`.
        
        Alternatively, you can find out where sadedeGel is installed and run `pytest` on
        that directory. Don't forget to also install the test utilities via sadedeGel's
        `requirements.txt`:
        
        ```bash
        make test
        ```
        
        ## References
        ### Software Engineering
        * Special thanks to [spaCy](https://github.com/explosion/spaCy) project for their work in showing us the way to implement a proper python module rather than merely explaining it.
            * We have borrowed many document and style related stuff from their code base :smile:
            
        ### Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP)
        * Resources on Extractive Text Summarization:
        
            * [Leveraging BERT for Extractive Text Summarization on Lectures](https://arxiv.org/abs/1906.04165)  by Derek Miller
            * [Fine-tune BERT for Extractive Summarization](https://arxiv.org/pdf/1903.10318.pdf) by Yang Liu
            * We have borrowed many document and style related stuff from their code base :smile:  
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.5
Description-Content-Type: text/markdown
