Metadata-Version: 2.1
Name: textacy
Version: 0.6.2
Summary: NLP, before and after spaCy
Home-page: https://github.com/chartbeat-labs/textacy
Maintainer: Burton DeWilde
Maintainer-email: burtdewilde@gmail.com
License: Apache
Download-URL: https://pypi.org/project/textacy
Description: textacy: NLP, before and after spaCy
        ====================================
        
        ``textacy`` is a Python library for performing a variety of natural language
        processing (NLP) tasks, built on the high-performance ``spacy`` library. With the
        fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. ---
        delegated to another library, ``textacy`` focuses on the tasks that come before
        and follow after.
        
        .. image:: https://img.shields.io/travis/chartbeat-labs/textacy/master.svg?style=flat-square
            :target: https://travis-ci.org/chartbeat-labs/textacy
            :alt: build status
        
        .. image:: https://img.shields.io/github/release/chartbeat-labs/textacy.svg?style=flat-square
            :target: https://github.com/chartbeat-labs/textacy/releases
            :alt: current release version
        
        .. image:: https://img.shields.io/pypi/v/textacy.svg?style=flat-square
            :target: https://pypi.python.org/pypi/textacy
            :alt: pypi version
        
        .. image:: https://anaconda.org/conda-forge/textacy/badges/version.svg
            :target: https://anaconda.org/conda-forge/textacy
            :alt: conda version
        
        Features
        --------
        
        - Provide a convenient entry point and interface to one or many documents, with
          the core processing delegated to spaCy
        - Stream text, json, csv, spaCy binary, and other data to and from disk
        - Download and explore a variety of included datasets with both text content and
          metadata, from Congressional speeches to historical literature to Reddit comments
        - Clean and normalize raw text, before analyzing it
        - Access and filter basic linguistic elements, such as words, ngrams, and noun
          chunks; extract named entities, acronyms and their definitions, and key terms
        - Flexibly tokenize and vectorize documents and corpora, then train, interpret,
          and visualize topic models using LSA, LDA, or NMF methods
        - Compare strings, sets, and documents by a variety of similarity metrics
        - Calculate common text statistics, including Flesch-Kincaid Grade Level,
          SMOG Index, and multilingual Flesch Reading Ease
        
        ... *and more!*
        
        
        Links
        -----
        
        - PyPi project: https://pypi.org/project/textacy
        - Source code: https://github.com/chartbeat-labs/textacy
        - Documentation: https://chartbeat-labs.github.io/textacy
        
        **Note:** Docs used to be hosted on ReadTheDocs, but the builds stopped working
        many months ago, and now those docs are out-of-date. This is unfortunate, especially
        since ReadTheDocs allows for multiple versions while GitHub Pages does not.
        I'll keep trying on ReadTheDocs; if the build issues ever get resolved, I'll
        switch the docs back.
        
        
        Maintainer
        ----------
        
        Howdy, y'all. 👋
        
        - Burton DeWilde (<burton@chartbeat.com>)
        
Keywords: textacy,spacy,nlp,text processing,linguistics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Natural Language :: English
Classifier: Topic :: Text Processing :: Linguistic
Provides-Extra: viz
Provides-Extra: lang
Provides-Extra: all
