textacy


Nametextacy JSON
Version 0.13.0 PyPI version JSON
download
home_page
SummaryNLP, before and after spaCy
upload_time2023-04-02 23:05:33
maintainer
docs_urlNone
author
requires_python>=3.9
licenseCopyright 2016 Chartbeat, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
keywords spacy nlp text processing linguistics
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bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ## 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>)

            

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