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Python Framework for Topic Modeling

Project description

Gensim is a Python framework designed to help make the conversion of natural language texts to the Vector Space Model as simple and natural as possible.

Gensim contains algorithms for unsupervised learning from raw, unstructured digital texts, such as Latent Semantic Analysis and Latent Dirichlet Allocation. These algorithms discover hidden (latent) corpus structure. Once found, documents can be succinctly expressed in terms of this structure, queried for topical similarity and so on.

If the previous paragraphs left you confused, you can first read more about the Vector Space Model and unsupervised document analysis at Wikipedia.


Gensim’s target audience is the NLP research community and interested general public; gensim is not meant to be a production tool for commercial environments.


gensim depends on NumPy and Scipy, two Python packages for scientific computing. You need to have them installed prior to using gensim; if you don’t have them yet, you can get them from <>.

The simple way to install gensim is:

sudo easy_install gensim

Or, if you have instead downloaded and unzipped the source tar.gz package, you’ll need to run:

python test
sudo python install

For alternative modes of installation (without root priviledges, development installation), see the documentation.

This version has been tested under Python 2.5, but should run on any 2.5 <= Python < 3.0.


Manual for the gensim package is available as HTML and as PDF. It contains a walk-through of all the features and a complete reference section. It is also included in the source package.

Gensim is open source software, and has been released under the GNU LPGL license. Copyright (c) 2010 Radim Rehurek

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Files for gensim, version 0.4.2
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