Is it possible to classify documents of corpus using labels?
You could simply encode the documents using BERT and cluster the documents based on their content provided they sufficiently different in terms of the kind of content they contain.

Another approach would be to train a document segmentation model which would segment documents based on their semantic structures and then classify the documents based on their masked skeletons. This however would require a large dataset to train. Fortunately you can find one online called PubLayNet. Augment that with a few representations of your documents for better generalization over the test set.


I've read about the second approach being implemented to classify patents, legal documents, research papers etc. With good results. However it would take a long time to train.
I'd recommend simply clustering the documents based on their text embedding (point 1) and then naming the clusters. If that doesn't work satisfactorily, try the deep learning method for document semantic masking.