Package: gensim-plural
Gensim is a Python library fortopic modelling,document indexingandsimilarity retrievalwith large corpora.
Target audience is thenatural language processing(NLP) andinformation retrieval(IR) community.FeaturesAll algorithms arememory-independentw.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core),Intuitive interfaceseasy 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 onlineLatent Semantic Analysis (LSA/LSI/SVD),Latent Dirichlet Allocation (LDA),Random Projections (RP),Hierarchical Dirichlet Process (HDP)orword2vec deep learning.Distributed computing: can runLatent Semantic AnalysisandLatent Dirichlet Allocationon a cluster of computers.Extensivedocumentation and Jupyter Notebook tutorials.If this feature list left you scratching your head, you can first read more about theVector
Space Modelandunsupervised
document analysison Wikipedia.InstallationThis software depends onNumPy and Scipy, two Python packages for scientific computing.
You must have them installed prior to installinggensim.It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such asATLASorOpenBLASis 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 installgensimis:pip install -U gensimOr, if you have instead downloaded and unzipped thesource tar.gzpackage,
you’d run:python setup.py test
python setup.py installFor alternative modes of installation (without root privileges, development
installation, optional install features), see theinstall documentation.This version has been tested under Python 2.7, 3.5 and 3.6. Support for Python 2.6, 3.3 and 3.4 was dropped in gensim 1.0.0. Install gensim 0.13.4 if youmustuse Python 2.6, 3.3 or 3.4. Support for Python 2.5 was dropped in gensim 0.10.0; install gensim 0.9.1 if youmustuse Python 2.5). Gensim’s github repo is hooked againstTravis CI for automated testingon 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’sdesign goals, and is a central feature of gensim, rather than something bolted on as an afterthought.DocumentationQuickStartTutorialsTutorial VideosOfficial Documentation and WalkthroughCiting gensimWhenciting 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},
      language={English}
}Gensim is open source software released under theGNU LGPLv2.1 license.
Copyright (c) 2009-now Radim Rehurek