Is there any way I can create a multioutput model with the fasttext python client and the sklearn MultiOutputClassifier object? I have multiple training datasets for each of the target columns. The example below contains the training dataset train_df_addresses.txt.

import fasttext
from sklearn.multioutput import MultiOutputClassifier

fasttext_params = {
    'input': '/content/fastText-0.1.0/train_df_addresses.txt',
    'lr': 0.1,
    'lrUpdateRate': 1000,
    'thread': 8,
    'epoch': 10,
    'wordNgrams': 3,
    #'dim': 100,
    'loss': 'softmax'
}

with tensorflow.device('/device:GPU:0'):
  model = fasttext.train_supervised(**fasttext_params, verbose=True)