Yes. I too agree that a large model in not required for detecting simple words like "Please subscribe to our channel" or "Here is the sponsor of our video". I also have another idea which I think should help in getting better accuracies. Use the channel's unique identifier (UID) or the channel's name as input ( and generate conditional probabilities conditioned on the channel's UID). This should help because any particular YouTube channel almost always use the same phrase to introduce their sponsors in almost all of their videos. Think of LinusTechTips, you always here the same thing, "here's the segue to our sponsor yada yada." So this should definitely allow the model to do more accurate inference. Alternatively, you can just reduce the model complexity to save client's resources.

The other thing you mentioned about the average user not hitting the right arrow two times, I think (and this is my hypothesis), the graph of users using adblocking softwares is just increasing monotonically, because once a user gets to savour the internet without ads, they don't go back. Only the old aged folks and the absolutely-not-computer-savvy people don't use adblockers, and IMO that population is decreasing and in the (near) future, that population would simply vanish. This is similar to what Steve Jobs said when he was asked whether people would ever use the mouse. Look at now, everyone uses the mouse. Coming to sponsor blocking, not hitting the right arrow is just more convenient than hitting the right arrow two times. Sometimes hitting it x number of times does not get the job done and you need to hit it further. Also, you might miss the beginning of the non-sponsored segment, so you need to hit the left arrow once too. All of this is made convenient by the current SOTA SponsorBlock extension. It has just begun its journey and I have no doubt that just like the adblocking extensions, sponsorblocking is going to take off and see an exponential growth.