gensim – Topic Modelling in Python

Travis Wheel

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.

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 (simple streaming API)

    • easy to extend with other Vector Space algorithms (simple 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 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 ATLAS or OpenBLAS is known to improve performance by as much as an order of magnitude. On OS X, NumPy picks up the BLAS that comes with it 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 Python 3.5, 3.6, 3.7 and 3.8. 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

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},
      language={English}
}

Gensim is open source software released under the GNU LGPLv2.1 license. Copyright (c) 2009-now Radim Rehurek

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  • Released:
Newer release available (4.4.0)

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Credits

Author: Radim Rehurek

License

GNU Lesser General Public License v2 or later (LGPLv2+) (LGPLv2.1)

Tags

Singular Value Decomposition SVD Latent Semantic Indexing LSA LSI Latent Dirichlet Allocation LDA Hierarchical Dirichlet Process HDP Random Projections TFIDF word2vec

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Metadata

Project links

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  • Released:
Newer release available (4.4.0)

2 maintainers

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Avatar for mpenkov from gravatar.com mpenkov Avatar for piskvorky from gravatar.com piskvorky

Credits

Author: Radim Rehurek

License

GNU Lesser General Public License v2 or later (LGPLv2+) (LGPLv2.1)

Tags

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Release files for gensim 3.8.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for gensim 3.8.3
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gensim-3.8.3.tar.gz 23.4 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for gensim 3.8.3
File
gensim-3.8.3-cp38-cp38-win_amd64.whl 24.2 MB CPython 3.8 CPython 3.8 Windows x86-64 Details
gensim-3.8.3-cp38-cp38-win32.whl 24.1 MB CPython 3.8 CPython 3.8 Windows x86-32 Details
gensim-3.8.3-cp38-cp38-manylinux1_x86_64.whl 24.2 MB CPython 3.8 CPython 3.8 Linux glibc 2.5+ x86-64 Details
gensim-3.8.3-cp38-cp38-manylinux1_i686.whl 24.2 MB CPython 3.8 CPython 3.8 Linux glibc 2.5+ x86-32 Details
gensim-3.8.3-cp38-cp38-macosx_10_9_x86_64.whl 24.2 MB CPython 3.8 CPython 3.8 macOS 10.9+ x86-64 Details
gensim-3.8.3-cp37-cp37m-win_amd64.whl 24.2 MB CPython 3.7 CPython 3.7 pymalloc Windows x86-64 Details
gensim-3.8.3-cp37-cp37m-win32.whl 24.1 MB CPython 3.7 CPython 3.7 pymalloc Windows x86-32 Details
gensim-3.8.3-cp37-cp37m-manylinux1_x86_64.whl 24.2 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.5+ x86-64 Details
gensim-3.8.3-cp37-cp37m-manylinux1_i686.whl 24.2 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.5+ x86-32 Details
gensim-3.8.3-cp37-cp37m-macosx_10_9_x86_64.whl 24.2 MB CPython 3.7 CPython 3.7 pymalloc macOS 10.9+ x86-64 Details
gensim-3.8.3-cp36-cp36m-win_amd64.whl 24.2 MB CPython 3.6 CPython 3.6 pymalloc Windows x86-64 Details
gensim-3.8.3-cp36-cp36m-win32.whl 24.1 MB CPython 3.6 CPython 3.6 pymalloc Windows x86-32 Details
gensim-3.8.3-cp36-cp36m-manylinux1_x86_64.whl 24.2 MB CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-64 Details
gensim-3.8.3-cp36-cp36m-manylinux1_i686.whl 24.2 MB CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-32 Details
gensim-3.8.3-cp36-cp36m-macosx_10_9_x86_64.whl 24.2 MB CPython 3.6 CPython 3.6 pymalloc macOS 10.9+ x86-64 Details
gensim-3.8.3-cp35-cp35m-win_amd64.whl 24.2 MB CPython 3.5 CPython 3.5 pymalloc Windows x86-64 Details
gensim-3.8.3-cp35-cp35m-win32.whl 24.1 MB CPython 3.5 CPython 3.5 pymalloc Windows x86-32 Details
gensim-3.8.3-cp35-cp35m-manylinux1_x86_64.whl 24.2 MB CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-64 Details
gensim-3.8.3-cp35-cp35m-manylinux1_i686.whl 24.2 MB CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-32 Details
gensim-3.8.3-cp35-cp35m-macosx_10_9_intel.whl 24.6 MB CPython 3.5 CPython 3.5 pymalloc macOS 10.9+ Intel (x86-64, i386) Details
gensim-3.8.3-cp27-cp27mu-manylinux1_x86_64.whl 24.2 MB CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-64 Details
gensim-3.8.3-cp27-cp27mu-manylinux1_i686.whl 24.2 MB CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-32 Details
gensim-3.8.3-cp27-cp27m-win_amd64.whl 23.8 MB CPython 2.7 CPython 2.7 pymalloc Windows x86-64 Details
gensim-3.8.3-cp27-cp27m-win32.whl 23.8 MB CPython 2.7 CPython 2.7 pymalloc Windows x86-32 Details
gensim-3.8.3-cp27-cp27m-manylinux1_x86_64.whl 24.2 MB CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-64 Details
gensim-3.8.3-cp27-cp27m-manylinux1_i686.whl 24.2 MB CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-32 Details
gensim-3.8.3-cp27-cp27m-macosx_10_9_x86_64.whl 24.2 MB CPython 2.7 CPython 2.7 pymalloc macOS 10.9+ x86-64 Details

Total release size: 675.8 MB

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4.4.0

26 release files

4.3.3

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4.3.2

21 release files

4.3.1

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4.3.0

17 release files

4.2.0

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4.1.2

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4.1.1

13 release files

4.1.0

17 release files

4.0.1

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4.0.0

10 release files

This release

3.8.3 This release

28 release files

3.8.2

22 release files

3.8.1

31 release files

3.8.0

15 release files

3.7.3

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3.7.2

15 release files

3.7.1

31 release files

3.7.0

25 release files

3.6.0

24 release files

3.5.0

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3.4.0

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3.3.0

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3.2.0

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3.1.0

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3.0.0

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2.3.0

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2.2.0

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2.1.0

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2.0.0

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1.0.1

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1.0.0

9 release files

0.13.4

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0.13.0

1 release file

0.12.4

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0.12.3

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0.12.0

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0.9.1

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0.8.6

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0.8.1

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0.7.8

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0.7.6

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0.7.5

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0.7.3

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0.7.1

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0.7.0

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0.6.0

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0.5.0

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0.4.7

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0.4.1

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0.3.0

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