From T5 to T5X: A Game-Changing Evolution with JAX & FLAX

Key Takeaways

hello Community let's talk about Transformer based language model and we are at the current state of affairs we look at the t5x model and of course the Palm model by Google and we look at the last video where I showed you that bird and jpt are more or less the encoder stack of the Transformer architecture this is bird and the decoder stack of the Transformer architecture which became TPT by open AI now GPD as being just only directional we found out that we can use the decoder stack of the Transformer as a language model so a model trained purely for the task of next step word prediction rate burden on the head is bi-directional it is an anchored only stack and it is great at doing the single prediction period input token so great for classification tasks currently if you ask we have T5 and this is a multi-task unified model or short mum and this is the technology that powers Google search today if you believe current research publication of course to be specific since the mid of 2022 you have t5x you have the text to text transfer Transformer in Jacks and in Flags so utilizing the compute infrastructure by Google and if you want to know more about Jax I have a video on about Jacks in tensorflow and pytorch and if you want to know about the hardware configuration of h100 gpus by Nvidia or the A6 by Google I have also a video for this now in July 2020 we had the publication by Google about its T5 architecture and their ideas behind this in a pre-print publication they focused specifically on transfer learning in natural language processing and they say we often have this pre-training part on unsupervised learning and unlabeled data that we have so much data free from the internet and I examined this transfer learning model when they had a first pre-training on a data Rich task before then fine-tuning the system on a downstream task now actual Downstream task a question answering document summarization sentiment classification and their idea was to put everything in a text to text framework and I wanted to explore the limits of transfer learning and of course they used a transfer Transformer architecture this means more or less that they're used on the encoder stack also something like birds MLM modeling they had a drop out of 15 of the token in the input sequence and they even cared about to reduce the computational cost of pre-training their model but the great thing about Google is they open sourced it you have here the GitHub directory by Google research where they provide you the code and as you see it has been updated just two weeks ago and they even provide you here with a free Google collab notebook where you can experience T5 yourself of course not the full-fledged half trillion parameter model but the smaller model that fit within the Google collab notebook and here we are now here in our collab notebook from Google about T5 and as you can see it is about fine-tuning text to text transfer Transformers four closed book question answering and you have here all the code you'll see how you set it up on a TPU is some Easy Pathways then you have natural questions you have the code to code this in detail if you want I can make a video going with you step by step through the code but it is really easy to implement you have to transfer to new tasks they explain in detail to you how it is done how it is coded a very nice implementation an expected results you how you evaluate your model of course and they give you all this code to play around and then of course most important and predict functionality of the model and you have here your question that you can Define and you see the output here now on the T5 model of course since you're working here on a free Google collab notebook you cannot use the highest and the half trillion parameter model but also the smaller T5 implementations show you what you can achieve with the current and open source T5 code it is free to you for you to explore here on a free Google collab notebook so summarizing we can say t5x combines the pre-training and defined tuning for specific tasks it is pre-trained on multitask mixture before fine tuning for a specific task and Google itself claims it is 1 000 times more powerful than bird I can verify this but we have a question now what about the future of this and especially if you think about conversational AI tools like jet GPT now the answer is easy but before I give you the answer in my next video I want to show you that T5 also evolved in flan T5 I have two videos for you where I show you the code and the tuning of the hyper parameters I hope you enjoyed this and I see you in my next video