text: from sklearn.ensemble import RandomForestRegressor
  from sklearn.metrics import r2_score
  from gensim.models import KeyedVectors
  from sklearn.ensemble import GradientBoostingRegressor
  
  import numpy as np
  
  
  f1 = "models/sent_emb_lstm_POS_word2vec_20.emb"
  f2 = "models/sent_emb_pos_lstm.emb"
  
  pairs = [[], []]
  
  wv1 = KeyedVectors.load_word2vec_format(f1, binary=False)
  wv2 = KeyedVectors.load_word2vec_format(f2, binary=False)
  
  vocab = list(wv1.vocab)
  for sent_id in vocab:
      pairs[0].append(wv1[sent_id])
      pairs[1].append(wv2[sent_id])
  
  pairs = np.array(pairs)
  
  x, y = pairs[0, :, :], pairs[1, :, :]
  
  
  #model_xy = GradientBoostingRegressor()
  #model_yx = GradientBoostingRegressor()
  
  model_xy = RandomForestRegressor(random_state=42)
  model_yx = RandomForestRegressor(random_state=42)
  
  model_xy.fit(x, y)
  model_yx.fit(y, x)
  
  pred_y = model_xy.predict(x)
  pred_x = model_yx.predict(y)
  
  score_xy = r2_score(y, pred_y)
  score_yx = r2_score(x, pred_x)
  
  print("score w2v-20dim POS -> 1-of-k POS", score_xy)
  print("score 1-of-k POS -> w2v-20dim POS", score_yx)