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.
⚠️ Gensim is in stable maintenance mode: we are not accepting new features, but bug and documentation fixes are still welcome! ⚠️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, a Python package for scientific computing. Please bear in mind that building NumPy from source (e.g. by installing gensim on a platform which lacks NumPy .whl distribution) is a non-trivial task involving linking NumPy to a BLAS library.
It is recommended to provide a fast one (such as MKL, ATLAS or OpenBLAS) which can improve performance by as much as an order of magnitude. On OSX, NumPy picks up its vecLib BLAS automatically, so you don’t need to do anything special.
Install the latest version of gensim:
pip install --upgrade gensim
Or, if you have instead downloaded and unzipped the source tar.gz package:
tar -xvzf gensim-X.X.X.tar.gz
cd gensim-X.X.X/
pip install .
For alternative modes of installation, see the documentation.
Gensim is being continuously tested under all supported Python versions. Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7.
How come gensim is so fast and memory efficient? Isn’t it pure Python, and isn’t Python slow and greedy?Many scientific algorithms can be expressed in terms of large matrix operations (see the BLAS note above). Gensim taps into these low-level BLAS libraries, by means of its dependency on NumPy. So while gensim-the-top-level-code is pure Python, it actually executes highly optimized Fortran/C under the hood, including multithreading (if your BLAS is so configured).
Memory-wise, gensim makes heavy use of Python’s built-in generators and iterators for streamed data processing. Memory efficiency was one of gensim’s design goals, and is a central feature of gensim, rather than something bolted on as an afterthought.
For commercial support, please see Gensim sponsorship.
Ask open-ended questions on the public Gensim Mailing List.
Raise bugs on Github but please make sure you follow the issue template. Issues that are not bugs or fail to provide the requested details will be closed without inspection.
When citing gensim in academic papers and theses, please use this BibTeX entry:
@inproceedings{rehurek_lrec,
title = {{Software Framework for Topic Modelling with Large Corpora}},
author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka},
booktitle = {{Proceedings of the LREC 2010 Workshop on New
Challenges for NLP Frameworks}},
pages = {45--50},
year = 2010,
month = May,
day = 22,
publisher = {ELRA},
address = {Valletta, Malta},
note={\url{http://is.muni.cz/publication/884893/en}},
language={English}
}
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