Doubt on scope of text classification problem
In case you have a lot of cases where the same brand is described in different ways, e.g. "K.F.C", "KFC", "Kentucky Fried Chicken", then it might be worth using clusters based on textual similarity measures. This kind of problem is similar to record linkage.

But if this is not the case, then it would be a bad idea to try to merge brands based on their names since you would end up with groups of brands which have nothing in common except some part of their name.

At a more general level, it looks to me like the design of your task is flawed: why do you need a unique instance for every seller? A seller can have multiple brands, each from a different source, right? That would mean that there are multiple sources for each seller, how do you plan to deal with that? Are you sure you don't want to predict the source for a particular pair seller + brand instead?