model_word2vec.Rd
The word2vec algorithms include skip-gram and CBOW models, using either hierarchical softmax or negative sampling.
model_word2vec(...) load_word2vec(file)
... | Any other options, from the official documentation. |
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file | Path to a saved model. |
#> → Preprocessing 9 documents #> ← 9 documents after perprocessing# initialise word2vec <- model_word2vec(size = 100L, window = 5L, min_count = 1L) word2vec$build_vocab(docs)#> Noneword2vec$train(docs, total_examples = word2vec$corpus_count, epochs = 20L)#> (76, 580)word2vec$init_sims(replace = TRUE)#> None#> [('user', 0.10656532645225525), ('minors', 0.08684408664703369), ('human', 0.08643960952758789), ('eps', 0.054633546620607376), ('trees', 0.024800226092338562), ('graph', 0.01699760928750038), ('response', 0.015698809176683426), ('computer', -0.0017682183533906937), ('survey', -0.031362384557724), ('time', -0.13173900544643402)]#> trees#> 0.016136127