Hello, My understanding is that word2vec can be ran in two modes: continuous bag-of-words (CBOW) (order of words does not matter) continuous skip-gram (order of words matters) I would like to run the *CBOW* implementation from Spark's MLlib, but it is not clear to me from the documentation and their example how to do it. This is the example listed on their page.From: https://spark.apache.org/docs/2.1.0/mllib-feature-extraction.html#example import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}val input = sc.textFile("data/mllib/sample_lda_data.txt").map(line => line.split(" ").toSeq)val word2vec = new Word2Vec()val model = word2vec.fit(input)val synonyms = model.findSynonyms("1", 5)for((synonym, cosineSimilarity) <- synonyms) { println(s"$synonym $cosineSimilarity")} *My questions:* Which of the two modes does this example use? Do you know how I can run the model in the CBOW mode? Thanks in advance!
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