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        "author": "Stephen Hansen",
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        "description": "# topic-modelling-tools\nTopic Modelling with Latent Dirichlet Allocation using Gibbs sampling.\nThis version of the package uses the GNU Scientific Library for random number\ngeneration, providing faster performance than numpy.\n\nby Stephen Hansen, stephen.hansen@economics.ox.ac.uk\nAssociate Professor of Economics, University of Oxford\n\nPython/Cython code for cleaning text and estimating LDA via collapsed Gibbs sampling as in Griffiths and Steyvers (2004).\n\nTutorial scripts and notebooks making use of this library, along with some\nexample data, can be found in:\nhttps://github.com/sekhansen/text-mining-tutorial\n\n\n## Installation instructions\n\nThis version of the package requires the GNU Scientific Library (GSL) to be\ninstalled.  You can download GSL from ftp://ftp.gnu.org/gnu/gsl/ or \nfor Mac OSX using homebrew, you can do `brew install gsl`. If you have conda,\ndo `conda install gsl`.  \n\n(For a version that doesn't require GSL (but is somewhat slower), checkout the\n\"master\" branch of this repository, or `pip install topic-modelling-tools`.)\n\nIf you already have GSL, Python and pip installed, `pip install topic-modelling-tools_gsl`\nshould work.  The package depends on some other python libraries such as\nnumpy and nltk but this should be taken care of by pip.\n\nThe only other requirement is that a C++ compiler is needed to build the Cython\ncode.  For Mac OS X you can download Xcode command-line tools,\nwhile for Windows you can download the Visual Studio C++ compiler.\n\n\n\n",
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        "name": "topic-modelling-tools-fast",
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