Sample file showing that training of large degrees of freedom works, but trying to load it back breaks heap. This is a scaled down version of a larger corpus (Produces about approx 4.5GB file)

W2V file load break's heap

import org.canova.api.util.ClassPathResource
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable
import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer
import org.deeplearning4j.models.word2vec.{ VocabWord, Word2Vec }
import org.deeplearning4j.models.word2vec.wordstore.inmemory.InMemoryLookupCache
import org.deeplearning4j.text.sentenceiterator.{ BasicLineIterator, SentenceIterator }
import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor
import org.deeplearning4j.text.tokenization.tokenizerfactory.{ DefaultTokenizerFactory, TokenizerFactory }
import org.slf4j.LoggerFactory
import scala.io.Source

object ProductCluster {
  lazy val log = LoggerFactory.getLogger(ProductCluster.getClass)

  def main(args: Array[String]): Unit = {
    println("Building Model for noinit_unstemmed_mixed_adagrad_v1000_5f...")
    noinit_unstemmed_adagrad_v1000_5f()    
  }
  
  def noinit_unstemmed_adagrad_v1000_5f(): Unit = {
    println("Building Model for noinit_unstemmed_adagrad_v1000_5f...")
    val filePath = "./resources/unstemmed_product_desc.txt"
    val path2Save = "./resources/noinit_unstemmed_adagrad_v1000_5f.bin"

    println("Input : " + filePath)
    println("Output : " + filePath)
    val iter = new BasicLineIterator(filePath)
    val t = new DefaultTokenizerFactory()
    t.setTokenPreProcessor(new CommonPreprocessor())

    val cache = new InMemoryLookupCache()
    val table = new InMemoryLookupTable.Builder[VocabWord]()
      .vectorLength(1000)
      .useAdaGrad(true)
      .cache(cache)
      .lr(0.025f).build()

    println("Building model....")
    val word2Vec: Word2Vec = new Word2Vec.Builder()
      .minWordFrequency(5)
      .iterations(1)
      .epochs(2)
      .layerSize(1000)
      .seed(42)
      .windowSize(5)
      .iterate(iter)
      .tokenizerFactory(t)
      .lookupTable(table)
      .vocabCache(cache)
      .build()

    println("Fitting noinit_unstemmed_adagrad_v1000_5f model....")
    word2Vec.fit()
    println("Training finished...")
    WordVectorSerializer.writeFullModel(word2Vec, path2Save)
    println("Saved to " + path2Save)
  }
}

// ------ Second file -----

import org.deeplearning4j.models.word2vec.{VocabWord, Word2Vec}
import org.slf4j.LoggerFactory
import scala.io.Source
import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer

object ContextMatch {
  def main(args: Array[String]) : Unit = {
    val path2Save = "./resources/noinit_unstemmed_adagrad_v1000_5f.bin"
    
    val word2Vec: Word2Vec = WordVectorSerializer.loadFullModel(path2Save)
    
    val readMatch = "./resources/match.txt" // Should be some file on your system

    for (line <- Source.fromFile(readMatch).getLines) {
      val lst2 = word2Vec.wordsNearest(line, 10)
      println(s"Closest words to $line are : " + lst2)
    }

  }
}