text: from Data import Data
  from Model import NERModel
  
  data = Data()
  train_sentences = data.get_train_sentences()
  val_sentence = data.get_val_sentences()
  test_sentence = data.get_test_sentences()
  
  train_sentences, word_length, _ = data.formated_dataset(train_sentences[:20])
  val_sentence, _, _ = data.formated_dataset(val_sentence[:20], batch=True, batch_size=10)
  test_sentence, _, _ = data.formated_dataset(test_sentence[:20], batch=False)
  
  case_embeddings = data.get_case_embeddings()
  pos_embedings = data.get_pos_embeddings()
  word_embeddings = data.get_glove_embeddings()
  char_embedding = data.get_char_embeddings()
  char_case_embedding = data.get_char_case_embeddings()
  labels = data.get_label_one_hot()
  
  nerModel = NERModel(word_length=word_length, labels=labels,
                      case_embeddings=case_embeddings, pos_embedings=pos_embedings,
                      word_embeddings=word_embeddings,
                      char_embedding=char_embedding, char_case_embedding=char_case_embedding)
  nerModel.build_model()
  nerModel.train(inputs=train_sentences, validation_data=val_sentence,epochs=40)
  