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This package contains the pure Python implementation of gensim. If you don't need the highly optimized version of word2vec, it is sufficient to install this package. Otherwise installing the "python-gensim-addons"-package is strongly recommended. Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. Features: * All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM). * 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 implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI), 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, and word2vec on multiple cores. * Extensive HTML documentation and tutorials.
Package | Summary | Distribution | Download |
python-gensim-core-0.10.0-1.el7.noarch.html | Python framework for fast Vector Space Modelling | EPEL 7 for ppc64le | python-gensim-core-0.10.0-1.el7.noarch.rpm |
python-gensim-core-0.10.0-1.el7.noarch.html | Python framework for fast Vector Space Modelling | EPEL 7 for aarch64 | python-gensim-core-0.10.0-1.el7.noarch.rpm |
python-gensim-core-0.10.0-1.el7.noarch.html | Python framework for fast Vector Space Modelling | EPEL 7 for ppc64 | python-gensim-core-0.10.0-1.el7.noarch.rpm |
python-gensim-core-0.10.0-1.el7.noarch.html | Python framework for fast Vector Space Modelling | EPEL 7 for x86_64 | python-gensim-core-0.10.0-1.el7.noarch.rpm |
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