Gensim
Vector space modeling and topic modeling toolkit
From Wikipedia, the free encyclopedia
Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities, using modern statistical machine learning.
| Gensim | |
|---|---|
 | |
| Original author | Radim Řehůřek |
| Developer | Radim Řehůřek |
| Release | 2009 |
| Stable release | 4.4.0[1]
/ 16 October 2025 |
| Written in | Python |
| Operating system | Linux, Windows, macOS |
| Type | Information retrieval |
| License | LGPL |
| Website | radimrehurek |
| Repository | github |
Gensim is implemented in Python and Cython for performance. 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,[2] word2vec and doc2vec algorithms,[3] as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections.[4]
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.[5]
