code: def fit(self, X, y, model_filename=None):
          
          train_file = "temp.train"
          X = [x.replace("\n", " ") for x in X]
          y = [item[0] for item in y]
          y = [_.replace(" ", "-") for _ in y]
          lines = ["__label__{} , {}".format(j, i) for i, j in zip(X, y)]
          content = "\n".join(lines)
          write(train_file, content)
          if model_filename:
              self.estimator = fasttext.supervised(train_file, model_filename)
          else:
              self.estimator = fasttext.supervised(train_file)
          os.remove(train_file)
docs: Fit FastText according to X, y
  
          Parameters:
          ----------
          X : list of text
              each item is a text
          y: list
             each item is either a label (in multi class problem) or list of
             labels (in multi label problem)