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.

• All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core),
• 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 multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning.
• Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers.
• Extensive documentation and Jupyter Notebook 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 before installing NumPy. This is optional, but using an optimized BLAS such as MKL, ATLAS or OpenBLAS is known to 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:

    python setup.py 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.

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.

• QuickStart
• Tutorials
• Official API Documentation

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.

Company	Logo	Industry	Use of Gensim
RARE Technologies		ML & NLP consulting	Creators of Gensim – this is us!
Amazon		Retail	Document similarity.
National Institutes of Health		Health	Processing grants and publications with word2vec.
Cisco Security		Security	Large-scale fraud detection.
Mindseye		Legal	Similarities in legal documents.
Channel 4		Media	Recommendation engine.
Talentpair		HR	Candidate matching in high-touch recruiting.
Juju		HR	Provide non-obvious related job suggestions.
Tailwind		Media	Post interesting and relevant content to Pinterest.
Issuu		Media	Gensim's LDA module lies at the very core of the analysis we perform on each uploaded publication to figure out what it's all about.
Search Metrics		Content Marketing	Gensim word2vec used for entity disambiguation in Search Engine Optimisation.
12K Research		Media	Document similarity analysis on media articles.
Stillwater Supercomputing		Hardware	Document comprehension and association with word2vec.
SiteGround		Web hosting	An ensemble search engine which uses different embeddings models and similarities, including word2vec, WMD, and LDA.
Capital One		Finance	Topic modeling for customer complaints exploration.

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{https://is.muni.cz/publication/884893/en}},
      language={English}
}