Gensim is an open-source library for unsupervised topic modeling and natural language processing, using modern statistical machine learning.

Original author(s)Radim Řehůřek
Developer(s)RARE Technologies Ltd.
Initial release2009
Stable release
3.8.0 / 8 July 2019 (2019-07-08)
Written inPython
Operating systemLinux, Windows, macOS
TypeInformation retrieval

Gensim is implemented in Python and Cython. Gensim is designed to handle large text collections using data streaming and incremental online algorithms, which differentiates it from most other machine learning software packages that target only in-memory processing.

Main features

Gensim includes streamed parallelized implementations of fastText,[1] word2vec and doc2vec algorithms,[2] as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections.[3]

Some of the novel online algorithms in Gensim were also published in the 2011 PhD dissertation Scalability of Semantic Analysis in Natural Language Processing of Radim Řehůřek, the creator of Gensim.[4]

Uses of Gensim

Gensim has been used and cited in over 1400 commercial and academic applications as of 2018[5], in a diverse array of disciplines from medicine to insurance claim analysis to patent search[6]. The software has been covered in several new articles, podcasts and interviews.[7][8][9]

Free and commercial support

The open source code is developed and hosted on GitHub[10] and a public support forum is maintained on Google Groups[11] and Gitter.[12]

Gensim is commercially supported by the company, who also provide student mentorships and academic thesis projects for Gensim via their Student Incubator programme.[13]


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