T5 (Text-to-Text Transfer Transformer) is a machine learning-based natural language processing model developed by Google AI. It is an encoder-decoder neural network architecture which applies self-attention to generate powerful representations for natural language understanding tasks. Over the past several years we’ve observed exponential growth of Natural Language Processing (NLP) models trained in a self-supervised manner, with massive volumes of unlabeled data (Figure 1). From GPT-1 to BERT to GPT-2 to T5, GPT-3, GPT-J, GPT-NeoX and MT-NLG, the number of parameters has exploded from hundreds of thousands to hundreds of billions. These models demonstrate an ability to perform amazingly well on a wide variety of NLP tasks, such as long document summarization, sentiment analysis, and question answering, to name just a few. 

T5’s primary use is for text classification, machine translation, summarization, question answering, and generation. T5 has achieved state-of-the-art results on various machine learning benchmarks and is being used by researchers in many different fields. It is also a popular choice for natural language processing applications such as chatbots and voice assistants. T5’s flexible architecture allows it to be effective at a wide range of tasks, making it a powerful tool for machine learning developers. 

Today, the Cerebras AI Model Studio enables users to train generative pre-trained Transformer (GPT)-class models like those that can be found in our Model Zoo repository. This includes, but is not limited to, GPT 1.3B, 6B, 6.7B, 13B, 20B, and the T5 11B models. We enable a simple, push-button approach to training these large language models by providing users with pre-configured Python scripts to match a user’s training specifications. This reduces the development hours needed to prepare a training run, lowering the overall total cost of training.