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It is also recommended you install a fast BLAS library before installing NumPy. You must have them installed prior to installing gensim. This software depends on NumPy and Scipy, two Python packages for scientific computing. If this feature list left you scratching your head, you can first read more about the Vectorĭocument analysis on Wikipedia. Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning.ĭistributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers.Įxtensive documentation and Jupyter Notebook tutorials. the corpus size (can process input larger than RAM, streamed, out-of-core)Įasy to plug in your own input corpus/datastream (simple streaming API)Įasy to extend with other Vector Space algorithms (simple transformation API)Įfficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), FeaturesĪll algorithms are memory-independent w.r.t. Target audience is the natural language processing (NLP) and information retrieval (IR) community. If there’s a specific image you’d like us to see, you can send it us at this dropbox link.Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. We’ll be updating TPAI regularly to address those pieces of feedback and issue reports. Please give us any feedback or report issues with this release. We’ve also fixed a large number of issues, and included some nice quality of life improvements. This week we’re introducing a new feature to help generate before/after images to share with friends or social media.
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