Of course not. The use of pre-training on a large unlabeled corpus and subsequent fine-tuning is what the paper is about. It is stated repeatedly in the paper and the post.
It is totally correct and in no way misleading to say we need only 100 labeled examples. Anyone can get similar results on their own datasets without even needing to train their own wikitext model, since we've made the pre-trained model available.
(BTW, I see you work at a company that sells something that claims to "categorize SKUs to a standard taxonomy using neural networks." This seems like something you maybe could have mentioned.)
It is totally correct and in no way misleading to say we need only 100 labeled examples. Anyone can get similar results on their own datasets without even needing to train their own wikitext model, since we've made the pre-trained model available.
(BTW, I see you work at a company that sells something that claims to "categorize SKUs to a standard taxonomy using neural networks." This seems like something you maybe could have mentioned.)