Metadata-Version: 2.1
Name: annif
Version: 0.54.0
Summary: Automated subject indexing and classification tool
Home-page: https://github.com/NatLibFi/Annif
Author: Osma Suominen
Author-email: osma.suominen@helsinki.fi
License: UNKNOWN
Description: <img src="http://annif.org/new/static/img/annif-RGB.svg" width="150">
        
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        Annif is an automated subject indexing toolkit. It was originally created as
        a statistical automated indexing tool that used metadata from the
        [Finna.fi](https://finna.fi) discovery interface as a training corpus.
        
        This repo contains a rewritten production version of Annif based on the
        [prototype](https://github.com/osma/annif). It is a work in progress, but
        already functional for many common tasks.
        
        # Basic install
        
        You will need Python 3.6+ to install Annif.
        
        The recommended way is to install Annif from
        [PyPI](https://pypi.org/project/annif/) into a virtual environment.
        
            python3 -m venv annif-venv
            source annif-venv/bin/activate
            pip install annif
        
        You will also need NLTK data files:
        
            python -m nltk.downloader punkt
        
        Start up the application:
        
            annif
        
        See [Getting Started](https://github.com/NatLibFi/Annif/wiki/Getting-started)
        in the wiki for more details.
        
        # Docker install
        
        You can use Annif as a pre-built Docker container. Please see the 
        [wiki documentation](https://github.com/NatLibFi/Annif/wiki/Usage-with-Docker)
        for details.
        
        # Development install
        
        A development version of Annif can be installed by cloning the [GitHub
        repository](https://github.com/NatLibFi/Annif).
        
        ## Installation and setup
        
        Clone the repository.
        
        Switch into the repository directory.
        
        Create and activate a virtual environment (optional, but highly recommended):
        
            python3 -m venv venv
            . venv/bin/activate
        
        Install dependencies (including development) and make the installation editable:
        
            pip install .[dev]
            pip install -e .
        
        You will also need NLTK data files:
        
            python -m nltk.downloader punkt
        
        Start up the application:
        
            annif
        
        ## Unit tests
        
        Run `. venv/bin/activate` to enter the virtual environment and then run `pytest`.
        To have the test suite watch for changes in code and run automatically, use
        pytest-watch by running `ptw`.
        
        # Getting help
        
        Many resources are available:
        
         * [Usage documentation in the wiki](https://github.com/NatLibFi/Annif/wiki)
         * [Annif tutorial](https://github.com/NatLibFi/Annif-tutorial) for learning to use Annif
         * [annif-users](https://groups.google.com/forum/#!forum/annif-users) discussion forum
         * [Internal API documentation](https://readthedocs.org/projects/annif/) on ReadTheDocs
         * [annif.org](http://annif.org) project web site
        
        # Publications / How to cite
        
        An article about Annif has been published in the peer-reviewed Open Access
        journal LIBER Quarterly. The software itself is also archived on Zenodo and
        has a citable DOI.
        
        ## Annif articles
        
        Suominen, O.; Inkinen, J.; Lehtinen, M., 2021. 
        Annif and Finto AI: Developing and Implementing Automated Subject Indexing.
        Manuscript accepted on 23 June 2021 for publication in JLIS.it. URN:
        [https://urn.fi/URN:NBN:fi-fe2021080942632](https://urn.fi/URN:NBN:fi-fe2021080942632)
        
            @article{suominen2021annif,
              title={Annif and Finto AI: Developing and Implementing Automated Subject Indexing},
              author={Suominen, Osma and Inkinen, Juho and Lehtinen, Mona},
              journal={JLIS.it},
              note = {(in press)},
              url = {https://urn.fi/URN:NBN:fi-fe2021080942632}
            }
        
        Suominen, O., 2019. Annif: DIY automated subject indexing using multiple
        algorithms. LIBER Quarterly, 29(1), pp.1–25. DOI:
        [https://doi.org/10.18352/lq.10285](https://doi.org/10.18352/lq.10285)
        
            @article{suominen2019annif,
              title={Annif: DIY automated subject indexing using multiple algorithms},
              author={Suominen, Osma},
              journal={{LIBER} Quarterly},
              volume={29},
              number={1},
              pages={1--25},
              year={2019},
              doi = {10.18352/lq.10285},
              url = {https://doi.org/10.18352/lq.10285}
            }
        
        ## Citing the software itself
        
        Zenodo DOI:
        [https://doi.org/10.5281/zenodo.2578948](https://doi.org/10.5281/zenodo.2578948)
        
            @misc{https://doi.org/10.5281/zenodo.2578948,
              doi = {10.5281/ZENODO.2578948},
              url = {https://doi.org/10.5281/zenodo.2578948},
              title = {NatLibFi/Annif},
              year = {2019}
            }
        
        # License
        
        The code in this repository is licensed under Apache License 2.0, except for the
        dependencies included under `annif/static/css` and `annif/static/js`,
        which have their own licenses, see the file headers for details.
        Please note that the [YAKE](https://github.com/LIAAD/yake) library is licended
        under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.txt), while Annif is
        licensed under the Apache License 2.0. The licenses are compatible, but
        depending on legal interpretation, the terms of the GPLv3 (for example the
        requirement to publish corresponding source code when publishing an executable
        application) may be considered to apply to the whole of Annif+Yake if you
        decide to install the optional Yake dependency.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: fasttext
Provides-Extra: voikko
Provides-Extra: vw
Provides-Extra: nn
Provides-Extra: omikuji
Provides-Extra: yake
Provides-Extra: dev
