Natural Language Processing

Gensim – Python-based vector space modeling and topic modeling toolkit

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Its target audience is the natural language processing (NLP) and information retrieval (IR) community.

This is a tool for discovering the semantic structure of documents by examining the patterns of words (or higher-level structures such as entire sentences or documents). gensim accomplishes this by taking a corpus, a collection of text documents, and producing a vector representation of the text in the corpus. The vector representation can then be used to train a model, which is an algorithms to create different representations of the data, which are usually more semantic.

gensim makes heavy use of Python’s built-in generators and iterators for streamed data processing.

Features include:

  • Processes large, web-scale corpora using incremental online training algorithms.
  • All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core). They use highly optimized math routines.
  • Distributed versions of several algorithms to speed up processing and retrieval on machine clusters.
  • 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.
  • Converters and I/O formats: contains memory-efficient implementations to several popular data formats including Matrix Market, SVMlight, Blei’s LDA-C and more.
  • Fast indexing of documents in their semantic representation, and retrieval of topically similar documents.
  • Extensive documentation and Jupyter Notebook tutorials.


  • Python >= 2.7 (tested with versions 2.7, 3.5 and 3.6).
  • NumPy >= 1.11.3.
  • SciPy >= 0.18.1.
  • Six >= 1.5.0.
  • smart_open >= 1.2.1.

Support: QuickStart, GitHub Code Repository, Mailing List, Gitter
Developer: RaRe Technologies / Radim Řehůřek
License: GNU LGPLv2.1 license

Gensim is written in Python. Learn Python with our recommended free books and free tutorials.

Return to Natural Language Processing | Return to Python Natural Language Tools

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