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gensim -- Python Framework for Topic Modelling

Gensim is a Python library for *Vector Space Modelling* with very large corpora.
Target audience is the *Natural Language Processing* (NLP) community.


* 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`.

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, optional install features), see the `documentation <>`_.

This version has been tested under Python 2.5 and 2.6, but 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 LPGL license <>`_.
Copyright (c) 2010 Radim Rehurek


Python framework for efficient vector space modelling




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