You can set the problem_type of an xxxForSequenceClassification model to multi_label_classification when instantiating it, like so:
from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("bert-base-german-dbmz-uncased", problem_type="multi_label_classification", num_labels=num_labels_cla)

This ensures that the BCEWithLogitsLoss is used instead of the CrossEntropyLoss, which is necessary for multi-label classification. You can then fine-tune just like you would do with multi-class classification.