BERT file went into the BERT directory as a package, removing left-over file

scripts/ML/BERT.py

-from loaders import get_device
-from transformers import BertForSequenceClassification, BertTokenizer
-
-def loader(f):
-    def wrapped(*args, **kwargs):
-        name = f.__name__.replace('_init_', '')
-        print(f' - {name}', end='')
-        f(*args, **kwargs)
-        print(f'\r✔️  {name}')
-    return wrapped
-
-class BERT:
-    model_name = 'bert-base-multilingual-cased'
-
-    def __init__(self, root_path, training=False):
-        self.device = get_device()
-        print('Loading BERT tools')
-        self._init_tokenizer()
-        self.root_path = root_path
-        _init_classifier(training)
-
-    @loader
-    def _init_tokenizer():
-        self.tokenizer = BertTokenizer.from_pretrained(BERT.model_name)
-
-    @loader
-    def _init_classifier(training)
-        if training
-            bert = BertForSequenceClassification.from_pretrained(self.root_path)
-        else:
-            bert = BertForSequenceClassification.from_pretrained(
-                    model_name, # Use the 12-layer BERT model, with an uncased vocab.
-                    num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
-                                                  # You can increase this
-                                                  # for multi-class tasks.
-                    output_attentions = False, # Whether the model returns attentions weights.
-                    output_hidden_states = False, # Whether the model returns all hidden-states.
-                    )
-        self.model = bert.to(self.device.type)
-
-    def import_data(self, data):
-        return map(lambda d: d.to(self.device), data)
-
-    def save(self):
-        self.model.save_pretrained(self.root_path)