Home Projects gensim
gensim
Python

gensim

Topic Modelling for Humans

by piskvorky · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
data-miningdata-sciencedocument-similarityLGPL-2.1Python
View on GitHub
In plain words

Analyze large sets of documents to find topics and similarities using a Python library.

From the README

Install
pip install --upgrade gensim

Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
gensim — GitHub preview card
📈 Star history
16.46k16.45k
2026-06-282026-08-31
📈 Track gensim

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Topic Modelling for Humans

gensim has 16.5k stars on GitHub. It has been forked 4.4k times. gensim is written mainly in Python. It has been in active development since 2011. gensim is available under the LGPL-2.1 license. Its main topics are data-mining, data-science, document-similarity, fasttext.

Frequently asked questions

What is gensim?

Topic Modelling for Humans

Is gensim open source?

gensim is an open-source project. It is released under the LGPL-2.1 license.

Is gensim free?

Yes. gensim is free and open source — you can use, modify and self-host it. Its LGPL-2.1 license is copyleft: if you distribute a modified version, it must remain under the same license.

What license does gensim use?

gensim is available under the LGPL-2.1 license.

What language is gensim written in?

gensim is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — gensim

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=piskvorky-gensim)](https://olud.ai/project/piskvorky-gensim.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.