---
id: textacy
version: "0.13.0"
license: Copyright 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… (full text in the JSON record)
license_treatment: permissive
maintenance: dormant
---

# textacy — NLP, before and after spaCy

License: permissive · Maintenance: dormant · Downloads: 102.8K/mo

## What it is and what it does

textacy is a Python library that builds on spaCy to handle NLP tasks before and after core linguistic processing. It delegates tokenization, part-of-speech tagging, and dependency parsing to spaCy, then adds convenience methods for working with one or many documents, cleaning and normalizing raw text, extracting structured information (n-grams, entities, acronyms, keyterms, SVO triples), comparing strings using similarity metrics, training and visualizing topic models, and computing text readability and lexical diversity statistics.

The package depends on 14 runtime libraries including numpy, scipy, scikit-learn, networkx, and spacy. It is distributed as a pure-Python wheel with low install friction. The project is in Beta status and has been dormant since April 2023, though the repository remains public with occasional commits. It supports Python 3.9 and later.

Use it for:

- Extract n-grams, entities, and keyterms from documents after spaCy processing.
- Clean and normalize raw text before feeding it into spaCy for linguistic analysis.
- Compute readability metrics and lexical diversity statistics on text corpora.
- Train and visualize topic models from document collections.
- Compare and rank strings using similarity metrics for deduplication or matching tasks.
- Load prepared datasets with text and metadata for rapid prototyping of NLP workflows.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

textacy extends spaCy's NLP capabilities with pre- and post-processing tools: text cleaning, entity and n-gram extraction, string similarity metrics, topic modeling, and readability statistics.

Yes, if you are already using spaCy and need pre- or post-processing utilities. The package fills a real gap with low install friction and no known vulnerabilities. However, dormant maintenance (last release 1230 days ago) means you should expect no active support or updates; use it for stable, well-defined tasks rather than as a foundation for new feature development.

## Install

pip install textacy
uv add textacy
poetry add textacy

## Installing textacy

Before you install: Low install friction with a pure-Python wheel. Maintenance is dormant—last release was 1230 days ago and the last commit was 2023-09-22—but the repository remains active and supports Python 3.9, 3.10, 3.11.

License in practice: Licensed under Apache License 2.0 (permissive). You may use, modify, and distribute freely in commercial and private projects, provided you include the license notice.

Quickstart:

pip install textacy
import textacy
# Requires a spaCy model to be loaded separately
import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Your text here')
extracted = textacy.extract.ngrams(doc, n=2)

Requires a spaCy language model to be installed and loaded separately before textacy can process text.

Verify before relying:

- Specific spaCy version compatibility requirements beyond what the fact sheet states.
- Performance characteristics when processing large document collections.
- Whether all features remain functional given the dormant maintenance status.

## Package facts

- License: Copyright 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… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 102.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

spacy nlp text extraction, entity and n-gram extraction, topic modeling python, text readability statistics, nlp preprocessing cleaning, string similarity metrics, document vectorization, spacy-ecosystem, text-extraction, topic-modeling

[View on SkillFed](https://skillfed.io/packages/textacy) · [View on PyPI](https://pypi.org/project/textacy/)