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.
Please raise potential bugs on github. See Contribution Guide prior to raising an issue.
If you have an open-ended or a research question:
- Mailing List is the best option
- Gitter chat room is also available
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.
The simple way to install gensim is:
pip install -U gensim
Or, if you have instead downloaded and unzipped the source tar.gz package, you’d run:
python setup.py test
python setup.py install
For alternative modes of installation (without root privileges, development installation, optional install features), see the documentation.
This version has been tested under Python 2.6, 2.7, 3.3, 3.4 and 3.5 (support for Python 2.5 was dropped in gensim 0.10.0; install gensim 0.9.1 if you must use Python 2.5). Gensim’s github repo is hooked against Travis CI for automated testing on every commit push and pull request.
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.
- QuickStart
- Tutorials
- Tutorial Videos
- [Official API Documentation]
| Name | Logo | URL | Description |
|---|---|---|---|
| RaRe Technologies |  |
rare-technologies.com | Machine learning & NLP consulting and training. Creators and maintainers of Gensim. |
| Talentpair |  |
talentpair.com | Data science driving high-touch recruiting |
| Tailwind |  |
Tailwindapp.com | Post interesting and relevant content to Pinterest |
| Issuu |  |
Issuu.com | 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. |
| Sports Authority |  |
sportsauthority.com | Text mining of customer surveys and social media sources |
| Search Metrics |  |
searchmetrics.com | Gensim word2vec used for entity disambiguation in Search Engine Optimisation |
| Cisco Security |  |
cisco.com | Large-scale fraud detection |
| 12K Research |  |
12k.co | Document similarity analysis on media articles |
| National Institutes of Health |  |
github/NIHOPA | Processing grants and publications with word2vec |
| Codeq LLC |  |
codeq.com | Document classification with word2vec |
| Mass Cognition |  |
masscognition.com | Topic analysis service for consumer text data and general text data |
| Stillwater Supercomputing |  |
stillwater-sc.com | Document comprehension and association with word2vec |
| Channel 4 |  |
channel4.com | Recommendation engine |
| Amazon |  |
amazon.com | Document similarity |
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}
}