Word2Vec Embeddings

class Embeddings():
  def __init__(self, model_path):
    self.model_path = model_path
    self.model = None
    self.__load_model__()
    
  def __load_model__(self):
    #word_vectors = api.load("glove-wiki-gigaword-100")  
    model_name = 'glove-twitter-25' #'word2vec-google-news-50' #'glove-twitter-25'  
    if not os.path.exists(self.model_path+ model_name):
      print("Downloading model")
      self.model = api.load(model_name)
      self.model.save(self.model_path+ model_name)
    else:
      print("Loading model from Drive")
      self.model = KeyedVectors.load(self.model_path+ model_name)
    
  def get_oov_from_model(self, document_vocabulary):
    ## the below words are not available in our pre-trained model model_name
    print("The below words are not found in our pre-trained model")
    words = []
    for word in set(document_vocabulary):  
      if word not in self.model:
        words.append(word)
    print(words)  

  def get_sentence_embeddings(self, data_df, column_name):
    sentence_embeddings_list = []
    for sentence in data_df[column_name]:      
      sentence_embeddings = np.repeat(0, self.model.vector_size)
      try:
        tokens = sentence.split(" ")
        ## get the word embedding
        for word in tokens:
          if word in self.model:
            word_embedding = self.model[word]
          else:
            word_embedding = np.repeat(0, self.model.vector_size)          
          sentence_embeddings = sentence_embeddings + word_embedding
          ## take the average for sentence embeddings
          #sentence_embeddings = sentence_embeddings / len(tokens)
        sentence_embeddings_list.append(sentence_embeddings.reshape(1, -1))
      except Exception as e:
          print(e)
        
    return sentence_embeddings_list