Can I fine-tune the BERT on a dissimilar/unrelated task?
The sentence &quot;During pre-training, the model is trained on unlabeled data over different pre-training tasks.&quot; means that BERT was pre-trained on normal textual data on two tasks: masked language model (MLM) and next sentence prediction (NSP).  There were no other classification/tagging labels present in the data, as the MLM predicts the text itself and the NSP label is derived from the textual data itself. Both tasks were trained simultaneously from a single textual dataset that was prepared to feed the input text and the expected outputs for both tasks.
Therefore &quot;different&quot; here refers to the two pre-training tasks I mentioned: MLM and NSP.
When fine-tuning, you do not need to train again on the same sentence classification task, you just simply train it on the task you need. It is perfectly fine to fine-tune BERT on a sentence tagging task on your own dataset.