![]() Iterate over sentences from the Brown corpus load ( 'glove-twitter-25' ) > # Use the downloaded vectors as usual: > glove_vectors. keys ())) > # Download the "glove-twitter-25" embeddings > glove_vectors = gensim. > import gensim.downloader > # Show all available models in gensim-data > print ( list ( gensim. parsing.preprocessing – Functions to preprocess raw text.parsing.porter – Porter Stemming Algorithm.gment_wiki – Convert wikipedia dump to json-line format.scripts.word2vec2tensor – Convert the word2vec format to Tensorflow 2D tensor.scripts.make_wiki_online_nodebug – Convert articles from a Wikipedia dump.scripts.make_wiki_online – Convert articles from a Wikipedia dump.scripts.word2vec_standalone – Train word2vec on text file CORPUS.scripts.make_wikicorpus – Convert articles from a Wikipedia dump to vectors.scripts.glove2word2vec – Convert glove format to word2vec.scripts.package_info – Information about gensim package.topic_coherence.text_analysis – Analyzing the texts of a corpus to accumulate statistical information about word occurrences.topic_gmentation – Segmentation module.topic_coherence.probability_estimation – Probability estimation module.topic_coherence.indirect_confirmation_measure – Indirect confirmation measure module.topic_coherence.direct_confirmation_measure – Direct confirmation measure module.topic_coherence.aggregation – Aggregation module.test.utils – Internal testing functions.similarities.fastss – Fast Levenshtein edit distance.similarities.levenshtein – Fast soft-cosine semantic similarity search.similarities.nmslib – Approximate Vector Search using NMSLIB.similarities.annoy – Approximate Vector Search using Annoy.similarities.termsim – Term similarity queries.similarities.docsim – Document similarity queries.models.fasttext_inner – Cython routines for training FastText models.models.doc2vec_inner – Cython routines for training Doc2Vec models.models.word2vec_inner – Cython routines for training Word2Vec models.models.callbacks – Callbacks for track and viz LDA train process.herencemodel – Topic coherence pipeline.models.poincare – Train and use Poincare embeddings.models.phrases – Phrase (collocation) detection.models._fasttext_bin – Facebook’s fastText I/O.models.doc2vec – Doc2vec paragraph embeddings. ![]() models.keyedvectors – Store and query word vectors.models.lda_worker – Worker for distributed LDA.models.lda_dispatcher – Dispatcher for distributed LDA.models.lsi_worker – Worker for distributed LSI.models.lsi_dispatcher – Dispatcher for distributed LSI.anslation_matrix – Translation Matrix model.models.logentropy_model – LogEntropy model.models.hdpmodel – Hierarchical Dirichlet Process.models.ldaseqmodel – Dynamic Topic Modeling in Python.models.lsimodel – Latent Semantic Indexing.models.nmf – Non-Negative Matrix factorization.models.ensembelda – Ensemble Latent Dirichlet Allocation.models.ldamulticore – parallelized Latent Dirichlet Allocation.models.ldamodel – Latent Dirichlet Allocation.corpora.wikicorpus – Corpus from a Wikipedia dump.corpora.ucicorpus – Corpus in UCI format.corpora.textcorpus – Tools for building corpora with dictionaries.corpora.svmlightcorpus – Corpus in SVMlight format.corpora.sharded_corpus – Corpus stored in separate files.corpora.opinosiscorpus – Topic related review sentences.corpora.mmcorpus – Corpus in Matrix Market format.corpora.malletcorpus – Corpus in Mallet format.corpora.lowcorpus – Corpus in GibbsLda++ format. ![]() ![]()
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