Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
For a Python3 port of gensim by Parikshit Samant, visit this fork.
- All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM),
- Intuitive interfaces
- easy to plug in your own input corpus/datastream (trivial streaming API)
- easy to extend with other Vector Space algorithms (trivial transformation API)
- Efficient implementations of popular algorithms, such as online Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections
- Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers.
- Extensive HTML documentation and tutorials.
If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.
This software depends on NumPy and Scipy, two Python packages for scientific computing. You must have them installed prior to installing gensim.
It is also recommended you install a fast BLAS library prior to installing NumPy. This is optional, but using an optimized BLAS such as ATLAS or OpenBLAS is known to improve performance by as much as an order of magnitude.
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 setup.py test sudo python setup.py install
For alternative modes of installation (without root privileges, development installation, optional install features), see the documentation.
This version has been tested under Python 2.5, 2.6 and 2.7, and should run on any 2.5 <= Python < 3.0.
Manual for the gensim package is available in HTML. It contains a walk-through of all its features and a complete reference section. It is also included in the source distribution package.
Gensim is open source software, and has been released under the GNU LGPL license. Copyright (c) 2009-2013 Radim Rehurek