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.
Key Features
- 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.
Dependencies:
- 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.
Website: radimrehurek.com/gensim
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.
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