Metadata-Version: 2.1
Name: sadedegel
Version: 0.6
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://sadedegel.ai/dist/img/logo-2.png?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 0.6 out now!**
        [Check out the release notes here.](https://github.com/GlobalMaksimum/sadedegel/releases)
        
        
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        ## 📖 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
        
        * Several news datasets
          * Basic corpus
              * Raw corpus (`sadedegel.dataset.load_raw_corpus`)
              * Sentences tokenized corpus (`sadedegel.dataset.load_sentences_corpus`)  
              * Human annotated summary corpus (`sadedegel.dataset.load_summary_corpus`)   
          * [Extended corpus](sadedegel/dataset/README.md)
              * Raw corpus (`sadedegel.dataset.extended.load_extended_raw_corpus`)
              * Sentences tokenized corpus (`sadedegel.dataset.extended.load_extended_sents_corpus`)
        * ML based sentence boundary detector (**SBD**) trained for Turkish language (`sadedegel.dataset`)
        * Various baseline summarizers
          * Position Summarizer
            * First Important Summarizer
            * Last Important Summarizer
          * Length Summarizer
          * Band Summarizer
          * Random Summarizer
          
        * Various unsupervised/supervised summarizers
          * ROUGE1 Summarizer
          * Cluster Summarizer
          * Supervised Summarizer
         
        
        📖 **For more details, refere to [sadedegel.ai](http://sadedegel.ai)**
        
        ## Install sadedeGel
        
        - **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
          Studio)
        - **Python version**: 3.6+ (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)
        ```
        
        To use our ML based sentence boundary detector
        
        ```python
        from sadedegel.tokenize import Doc
        
        doc = ("Bilişim sektörü, günlük devrimlerin yaşandığı ve hızına yetişilemeyen dev bir alan haline geleli uzun bir zaman olmadı. Günümüz bilgisayarlarının tarihi, yarım asırı yeni tamamlarken; yaşanan gelişmeler çok "
        "daha büyük ölçekte. Türkiye de bu gelişmelere 1960 yılında Karayolları Umum Müdürlüğü (şimdiki Karayolları Genel Müdürlüğü) için IBM’den satın aldığı ilk bilgisayarıyla dahil oldu. IBM 650 Model I adını taşıyan bilgisayarın "
        "satın alınma amacı ise yol yapımında gereken hesaplamaların daha hızlı yapılmasıydı. Türkiye’nin ilk bilgisayar destekli karayolu olan 63 km uzunluğundaki Polatlı - Sivrihisar yolu için yapılan hesaplamalar IBM 650 ile 1 saatte yapıldı. "
        "Daha öncesinde 3 - 4 ayı bulan hesaplamaların 1 saate inmesi; teknolojinin, ekonomik ve toplumsal dönüşüme büyük etkide bulunacağının habercisiydi.")
        
        Doc(doc).sents
        ```
        ```python
        ['Bilişim sektörü, günlük devrimlerin yaşandığı ve hızına yetişilemeyen dev bir alan haline geleli uzun bir zaman olmadı.',
         'Günümüz bilgisayarlarının tarihi, yarım asırı yeni tamamlarken; yaşanan gelişmeler çok daha büyük ölçekte.',
         'Türkiye de bu gelişmelere 1960 yılında Karayolları Umum Müdürlüğü (şimdiki Karayolları Genel Müdürlüğü) için IBM’den satın aldığı ilk bilgisayarıyla dahil oldu.',
         'IBM 650 Model I adını taşıyan bilgisayarın satın alınma amacı ise yol yapımında gereken hesaplamaların daha hızlı yapılmasıydı.',
         'Türkiye’nin ilk bilgisayar destekli karayolu olan 63 km uzunluğundaki Polatlı - Sivrihisar yolu için yapılan hesaplamalar IBM 650 ile 1 saatte yapıldı.',
         'Daha öncesinde 3 - 4 ayı bulan hesaplamaların 1 saate inmesi; teknolojinin, ekonomik ve toplumsal dönüşüme büyük etkide bulunacağının habercisiydi.']
        ```
        
        #### SadedeGel Server
        In order to integrate with your applications we provide a quick summarizer server with sadedeGel.
        
        ```bash
        python3 -m sadedegel.server 
        ```
        
        Refer to self documenting APIs for details (http://localhost:8000/docs or http://localhost:8000/redoc by default)
        
        ## 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
        
        * Other NLP related references
        
            * [ROUGE: A Package for Automatic Evaluation of Summaries](https://www.aclweb.org/anthology/W04-1013.pdf)
        
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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
