Metadata-Version: 2.1
Name: textacy
Version: 0.10.0
Summary: NLP, before and after spaCy
Home-page: https://github.com/chartbeat-labs/textacy
Maintainer: Burton DeWilde
Maintainer-email: burtdewilde@gmail.com
License: Apache
Project-URL: Documentation, https://chartbeat-labs.github.io/textacy
Project-URL: Source Code, https://github.com/chartbeat-labs/textacy
Project-URL: Bug Tracker, https://github.com/chartbeat-labs/textacy/issues
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 primarily on the tasks that come before and follow after.
        
        [![build status](https://img.shields.io/travis/chartbeat-labs/textacy/master.svg?style=flat-square)](https://travis-ci.org/chartbeat-labs/textacy)
        [![current release version](https://img.shields.io/github/release/chartbeat-labs/textacy.svg?style=flat-square)](https://github.com/chartbeat-labs/textacy/releases)
        [![pypi version](https://img.shields.io/pypi/v/textacy.svg?style=flat-square)](https://pypi.python.org/pypi/textacy)
        [![conda version](https://anaconda.org/conda-forge/textacy/badges/version.svg)](https://anaconda.org/conda-forge/textacy)
        
        ### Features
        
        - Convenient entry points to working with one or many documents processed by spaCy, with functionality added via custom extensions and automatic language identification for applying the right spaCy pipeline
        - Variety of downloadable datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
        - Easy file I/O for streaming data to and from disk
        - Cleaning, normalization, and exploration of raw text — before processing
        - Flexible extraction of words, ngrams, noun chunks, entities, acronyms, key terms, and other elements of interest
        - Tokenization and vectorization of documents, with functionality for training, interpreting, and visualizing topic models
        - String, set, and document similarity comparison by a variety of metrics
        - Calculations for common text statistics, including Flesch-Kincaid Grade Level and multilingual Flesch Reading Ease
        
        ... *and more!*
        
        
        ### Links
        
        - Download: https://pypi.org/project/textacy
        - Documentation: https://chartbeat-labs.github.io/textacy
        - Source code: https://github.com/chartbeat-labs/textacy
        - Bug Tracker: https://github.com/chartbeat-labs/textacy/issues
        
        
        ### 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: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Natural Language :: English
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: viz
