finallywe build the lda model as beforemodel gensim models ldamodel ldamodelcorpus=mmid word=id wordnum_topics= update_every= chunksize= passes= this will again take couple of hours (you will see the progress on your consolewhich can give you an indication of how long you still have to waitonce it is doneyou can save it to file so you don' have to redo it all the timemodel save('wiki_lda pkl'if you exit your session and come back lateryou can load the model again withmodel gensim models ldamodel ldamodel load('wiki_lda pkl'let us explore some topicstopics [for doc in mmtopics append(model[doc]we can see that this is still sparse model even if we have many more documents than before (over million as we are writing this)import numpy as np lens np array([len(tfor in print np mean(lens print np mean(lens < topics]sothe average document mentions topics and percent of them mention or fewer if you have not seen the idiom beforeit may be odd to take the mean of comparisonbut it is direct way to compute fraction np mean(lens < is taking the mean of an array of booleans the booleans get interpreted as and in numeric context thereforethe result is number between and which is the fraction of ones in this caseit is the fraction of elements of lenswhich are less than or equal to