1. Add the following snippet (at the beginning of the source code) to set the data type for the current JVM runtime:

Nd4j.setDataType(DataBuffer.Type.DOUBLE);

2. Write word vectors into a file:

WordVectorSerializer.writeWordVectors(model.lookupTable(),new File("words.txt"));

3. Separate the weights of the unique words into their own list using WordVectorSerializer:

Pair<InMemoryLookupTable,VocabCache> vectors = WordVectorSerializer.loadTxt(new File("words.txt"));
VocabCache cache = vectors.getSecond();
INDArray weights = vectors.getFirst().getSyn0(); 

4. Create a list to add all unique words:

 List<String> cacheList = new ArrayList<>();
 for(int i=0;i<cache.numWords();i++){
 cacheList.add(cache.wordAtIndex(i));
 }

5. Build a dual-tree t-SNE model for dimensionality reduction using BarnesHutTsne: 
   

BarnesHutTsne tsne = new BarnesHutTsne.Builder()
 .setMaxIter(100)
 .theta(0.5)
 .normalize(false)
 .learningRate(500)
 .useAdaGrad(false)
 .build();

6. Establish the t-SNE values and save them to a file:

tsne.fit(weights);
tsne.saveAsFile(cacheList,"tsne-standard-coords.csv");