def __init__()

in src/bert_model.py [0:0]


    def __init__(self, model_name_or_dir, num_classes, fine_tune=True, state_dict=None, bert_config=None):
        super().__init__()
        assert model_name_or_dir is not None or bert_config is not None, "Either a name or directory containing a pretrained model or a custom bert config must be provided"
        if bert_config is None:
            self.model = BertForSequenceClassification.from_pretrained(model_name_or_dir, num_labels=num_classes,
                                                                       state_dict=state_dict)
        else:
            self.model = BertForSequenceClassification(config=bert_config)

        # Fine tune, freeze all other weights except classifier
        if fine_tune:
            self._freeze_base_weights()