code: function (jvmObject) {
  
          this.logger = Logger.getLogger("mllib_feature_Word2Vec_js");
          if (!jvmObject) {
              jvmObject = new org.apache.spark.mllib.feature.Word2Vec();
          }
          JavaWrapper.call(this, jvmObject);
  
      }
docs: Word2Vec creates vector representation of words in a text corpus.
  The algorithm first constructs a vocabulary from the corpus
  and then learns vector representation of words in the vocabulary.
  The vector representation can be used as features in
  natural language processing and machine learning algorithms.
  
  We used skip-gram model in our implementation and hierarchical softmax
  method to train the model. The variable names in the implementation
  matches the original C implementation.
  
  For original C implementation, see https://code.google.com/p/word2vec/
  For research papers, see
  Efficient Estimation of Word Representations in Vector Space
  and
  Distributed Representations of Words and Phrases and their Compositionality.
  @memberof module:eclairjs/mllib/feature
  @classdesc
  @class