Fair question. I didn't personally supervise our last intern, it was my turn the summer before, so I'm not as deeply familiar with it. Now that you bring this up though, I think perhaps I may have misspoken. When I said muti-label, I think that was our goal originally, but because of the constraints of Snorkel you mentioned, we ended up reframing the problem into many single class models instead. They would both work, but because of how our business users worked, multi-label wasn't super important. For example, not all business users are interested in every label, so I think what happened was more than one model was trained, one for each label, and then ensembled based on the business users interests. Our final output allowed users to effectively sort, filter, and search documents based on any combination of these labels. Keep in mind too, some of these labels are fairly abstract, so just one of them was fairly powerful by itself and could perhaps power an entire team in some cases. I hope that helps, I'm sorry I can't go into too much more detail.