How to use the fasttext.supervised function in fasttext

* silent                 disable the log output from the C++ extension [1]
        * encoding               specify input_file encoding [utf-8]
        * pretrained_vectors     pretrained word vectors (.vec file) for supervised learning []
        """
        config = get_config()
        kwargs.setdefault('lr', config.get('model', 'lr'))
        kwargs.setdefault('lr_update_rate', config.get('model', 'lr_update_rate'))
        kwargs.setdefault('dim', config.get('model', 'dim'))
        kwargs.setdefault('ws', config.get('model', 'ws'))
        kwargs.setdefault('epoch', config.get('model', 'epoch'))
        kwargs.setdefault('word_ngrams', config.get('model', 'word_ngrams'))
        kwargs.setdefault('loss', config.get('model', 'loss'))
        kwargs.setdefault('bucket', config.get('model', 'bucket'))
        kwargs.setdefault('thread', config.get('model', 'thread'))
        kwargs.setdefault('silent', config.get('model', 'silent'))
        cls.__model = ft.supervised(input_file, output, **kwargs)
        return cls.__model

cleaned = clean_text(tweet[1])
            i+=1
            if i%10==0: # to test
                for htag in tweet[0]:
                    _=fo2.write("__label__{} ".format(htag))
                _=fo2.write("{}\n".format(cleaned))
            else: # to train
                for htag in tweet[0]:
                    _=fo.write("__label__{} ".format(htag))
                _=fo.write("{}\n".format(cleaned))

        fo.close()
        fo2.close()

        # epoch improves precision at least on smallish sets. make it a variable?
        self.classifier = fasttext.supervised(train_file, '/tmp/model', epoch=35)

        self.analyze_model(test_file)

        return self.classifier

def train_classifier(output):
            self._classifier = fasttext.supervised(input_file=train_path, output=output,
                                                   label_prefix=label_prefix or self.LABEL_PREFIX, **self.get_params())

def create_classifier(self):
        return fasttext.supervised(self.train_file,
                                   self.model_name,
                                   epoch=self.params['epoch'],
                                   dim=10,
                                   word_ngrams=self.params['word_ngrams'],
                                   lr=self.params['lr'],
                                   min_count=self.params['min_count'],
                                   bucket=2000000,
                                   loss='ns')