gensim – Topic Modelling in Python

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! ⚠️

Features

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.

Installation

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.

Documentation

Support

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.

Adopters

Company	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.

Citing gensim

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}
}