code: 
  import numpy as np
  import pandas as pd
  import random
  
  import joblib
  
  from sklearn.feature_extraction.text import *
  
  print('Libraries imported.')
  
  """#Import Dataset e print it"""
  filename = 'train_dataset.jsonl'
  db = pd.read_json(filename,lines=True)
  #print(db)
  
  filename='test_dataset_blind.jsonl'
  test_db = pd.read_json(filename,lines=True)
  
  instructionExamples_train = []
  currentExample = ''
  
  """# Extracts mnemonics only"""
  for example in db.instructions:
      for instr in example:
          mnemonics=instr.split()[0]
          currentExample += mnemonics + ' '
      instructionExamples_train.append(currentExample)
      currentExample = ''
  
  instructionExamples_test = []
  currentExample = ''
  
  """# Extracts mnemonics only"""
  for example in test_db.instructions:
      for instr in example:
          mnemonics=instr.split()[0]
          currentExample += mnemonics + ' '
      instructionExamples_test.append(currentExample)
      currentExample = ''
  
  #Print a processed examples
  print(instructionExamples_train[random.randrange(0,len(instructionExamples_train))])
  print(instructionExamples_test[random.randrange(0,len(instructionExamples_test))])
  
  n_gram_vectorizer = CountVectorizer(ngram_range=(2, 7))
  n_gram_vectorizer.fit(instructionExamples_train)
  X_test_n_grams = n_gram_vectorizer.transform(instructionExamples_test)
  
  #LOAD BEST MODEL
  bestName = 'compilers[cnb_n_grams].joblib'
  bestModel = joblib.load(bestName)
  
  results_name = "blind_set_results[compilers].csv"
  results = open(results_name, "a")
  
  """# Predict on Test set"""
  y_pred = bestModel.predict(X_test_n_grams)
  df = pd.DataFrame(y_pred)
  df.to_csv(path_or_buf = results, index=False)
  results.close()
  
apis: pandas.DataFrame pandas.read_json