text:   Transformers",8,"['Check out our new paper, presenting insights on scaling Transformers/T5 within the context of the pretraining-finetuning setup.  \n<LINK>\n\nWith @YiTayML, @Jeffy_Sailing, @LiamFedus, @samiraabnar, @hwchung27, @sharan0909, @DaniYogatama, @ashVaswani, and @metzlerd. <LINK>', 'In search of the best scaling recipe, we ran an extensive search over different knobs and evaluated our modes w.r.t not only upstream performance (perplexity) but also the performance on different downstream tasks/benchmarks after finetuning. \nA few cool take-home messages:', '(1) If you, by default, take the T5-Base for your next amazing project to build on top of it, you may want to reconsider that. Transformer/T5 Base is not necessarily the Pareto-efficient configuration. We present some better alternatives.', '(2) Although model size is a key factor determining the scaling behavior of Transformers in pretraining, the model shape matters a lot for fine-tuning. So blindly scaling up may look good upstream, but can be disappointing when you finetune your model on a downstream task.', '(3) ""How to scale up?"" is pretty much depending on the region you\'re in. If you\'re working with Tiny models and want to go to Base size, the best recipe differs from the case of going from Large to XL.', 'We have many more observations and insights. You should check the paper.', 'Oh, btw, we are releasing 100+ pretrained checkpoints from our experiments, which will be an amazing playground for doing further analysis and interesting research :)', ""You should also check @YiTayML's great thread that highlights more points from the paper:\nhttps://t.co/MLj7rdrq1b""]",21,09,1593