Metadata-Version: 2.1
Name: textacy
Version: 0.12.0
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://textacy.readthedocs.io
Project-URL: Source Code, https://github.com/chartbeat-labs/textacy
Project-URL: Bug Tracker, https://github.com/chartbeat-labs/textacy/issues
Keywords: 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 :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Natural Language :: English
Classifier: Topic :: Text Processing :: Linguistic
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License-File: LICENSE.txt
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## 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

- Access and extend spaCy's core functionality for working with one or many documents through convenient methods and custom extensions
- Load prepared datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
- Clean, normalize, and explore raw text before processing it with spaCy
- Extract structured information from processed documents, including n-grams, entities, acronyms, keyterms, and SVO triples
- Compare strings and sequences using a variety of similarity metrics
- Tokenize and vectorize documents then train, interpret, and visualize topic models
- Compute text readability and lexical diversity statistics, including Flesch-Kincaid grade level, multilingual Flesch Reading Ease, and Type-Token Ratio

... *and much more!*

### links

- Download: https://pypi.org/project/textacy
- Documentation: https://textacy.readthedocs.io
- Source code: https://github.com/chartbeat-labs/textacy
- Bug Tracker: https://github.com/chartbeat-labs/textacy/issues

### maintainer

Howdy, y'all. 👋

- Burton DeWilde (<burtdewilde@gmail.com>)


