docs: @Deprecated
      public static Word2Vec loadFullModel(@NonNull String path) throws FileNotFoundException {
          /*
              // TODO: implementation is in process
              We need to restore:
                       1. WeightLookupTable, including syn0 and syn1 matrices
                       2. VocabCache + mark it as SPECIAL, to avoid accidental word removals
           */
          BasicLineIterator iterator = new BasicLineIterator(new File(path));
  
          // first 3 lines should be processed separately
          String confJson = iterator.nextSentence();
          log.info("Word2Vec conf. JSON: " + confJson);
          VectorsConfiguration configuration = VectorsConfiguration.fromJson(confJson);
  
  
          // actually we dont need expTable, since it produces exact results on subsequent runs untill you dont modify expTable size :)
          String eTable = iterator.nextSentence();
          double[] expTable;
  
  
          String nTable = iterator.nextSentence();
          if (configuration.getNegative() > 0) {
              // TODO: we probably should parse negTable, but it's not required until vocab changes are introduced. Since on the predefined vocab it will produce exact nTable, the same goes for expTable btw.
          }
  
          /*
                  Since we're restoring vocab from previously serialized model, we can expect minWordFrequency appliance in its vocabulary, so it should NOT be truncated.
                  That's why i'm setting minWordFrequency to configuration value, but applying SPECIAL to each word, to avoid truncation
           */
          VocabularyHolder holder = new VocabularyHolder.Builder().minWordFrequency(configuration.getMinWordFrequency())
                  .hugeModelExpected(configuration.isHugeModelExpected())
                  .scavengerActivationThreshold(configuration.getScavengerActivationThreshold())
                  .scavengerRetentionDelay(configuration.getScavengerRetentionDelay()).build();
  
          AtomicInteger counter = new AtomicInteger(0);
          AbstractCache<VocabWord> vocabCache = new AbstractCache.Builder<VocabWord>().build();
          while (iterator.hasNext()) {
              //    log.info("got line: " + iterator.nextSentence());
              String wordJson = iterator.nextSentence();
              VocabularyWord word = VocabularyWord.fromJson(wordJson);
              word.setSpecial(true);
  
              VocabWord vw = new VocabWord(word.getCount(), word.getWord());
              vw.setIndex(counter.getAndIncrement());
  
              vw.setIndex(word.getHuffmanNode().getIdx());
              vw.setCodeLength(word.getHuffmanNode().getLength());
              vw.setPoints(arrayToList(word.getHuffmanNode().getPoint(), word.getHuffmanNode().getLength()));
              vw.setCodes(arrayToList(word.getHuffmanNode().getCode(), word.getHuffmanNode().getLength()));
  
              vocabCache.addToken(vw);
              vocabCache.addWordToIndex(vw.getIndex(), vw.getLabel());
              vocabCache.putVocabWord(vw.getWord());
          }
  
          // at this moment vocab is restored, and it's time to rebuild Huffman tree
          // since word counters are equal, huffman tree will be equal too
          //holder.updateHuffmanCodes();
  
          // we definitely don't need UNK word in this scenarion
  
  
          //        holder.transferBackToVocabCache(vocabCache, false);
  
          // now, it's time to transfer syn0/syn1/syn1 neg values
          InMemoryLookupTable lookupTable =
                  (InMemoryLookupTable) new InMemoryLookupTable.Builder().negative(configuration.getNegative())
                          .useAdaGrad(configuration.isUseAdaGrad()).lr(configuration.getLearningRate())
                          .cache(vocabCache).vectorLength(configuration.getLayersSize()).build();
  
          // we create all arrays
          lookupTable.resetWeights(true);
  
          iterator.reset();
  
          // we should skip 3 lines from file
          iterator.nextSentence();
          iterator.nextSentence();
          iterator.nextSentence();
  
          // now, for each word from vocabHolder we'll just transfer actual values
          while (iterator.hasNext()) {
              String wordJson = iterator.nextSentence();
              VocabularyWord word = VocabularyWord.fromJson(wordJson);
  
              // syn0 transfer
              INDArray syn0 = lookupTable.getSyn0().getRow(vocabCache.indexOf(word.getWord()));
              syn0.assign(Nd4j.create(word.getSyn0()));
  
              // syn1 transfer
              // syn1 values are being accessed via tree points, but since our goal is just deserialization - we can just push it row by row
              INDArray syn1 = lookupTable.getSyn1().getRow(vocabCache.indexOf(word.getWord()));
              syn1.assign(Nd4j.create(word.getSyn1()));
  
              // syn1Neg transfer
              if (configuration.getNegative() > 0) {
                  INDArray syn1Neg = lookupTable.getSyn1Neg().getRow(vocabCache.indexOf(word.getWord()));
                  syn1Neg.assign(Nd4j.create(word.getSyn1Neg()));
              }
          }
  
          Word2Vec vec = new Word2Vec.Builder(configuration).vocabCache(vocabCache).lookupTable(lookupTable)
                  .resetModel(false).build();
  
          vec.setModelUtils(new BasicModelUtils());
  
          return vec;
      }