This is fabulous, great work, with broad applicability. Train this transformer model on a good amount of text (or grab a pretrained model), and then, with mini

This is fabulous, great work, with broad applicability. Train this transformer model on a good amount of text (or grab a pretrained model), and then, with minimal fuss and very little tweaking, you can repurpose it to obtain state-of-the-art (or near state-of-the-art) results in a wide range of tasks, from document classification to textual entailment to semantic similarity. This stands in contrast to prior approaches that involve much more tweaking and/or careful discriminative finetuning of the pretrained model, such as as Jeremy Howard and Sebastian Ruder's also-impressive ULMFit.[a] The main downside to this new approach is that pretraining takes a long time. Anyone working on ML/DL/AI with text should take a look at this, right now. UPDATE: See Jeremy Howard's comment here: https://news.ycombinator.com/item?id=17288320 https://news.ycombinator.com/item?id=17288320 [a] https://arxiv.org/abs/1801.06146 https://arxiv.org/abs/1801.06146