What is tensor2tensor?

Tensor2Tensor, often shortened to T2T, is a library of pre-built deep learning models and datasets that aims to make deep learning more accessible and to speed up machine learning research. Deep learning is a branch of machine learning that uses neural networks with many layers, and T2T packages up the building blocks so researchers do not have to start from scratch for each new experiment. Inside the library, a "problem" describes a dataset and the task you want to do with it, and a "model" describes the neural network shape. T2T comes with ready-made problems for image classification using datasets like ImageNet, CIFAR-10, CIFAR-100 and MNIST, image generation using CelebA, LSUN Bedrooms and MS-COCO, language modeling using PTB and the billion-word LM1B corpus, speech recognition using Librispeech and Mozilla Common Voice, sentiment analysis using IMDB, question answering using bAbi, and machine translation. It includes several flavours of the Transformer model, plus alternatives like ResNet, Xception and Shake-Shake. You pick a problem, pick a model, pick a set of hyperparameters, and a single command-line trainer called t2t-trainer runs the experiment. Someone would actually use this if they were doing machine learning research and wanted a head start on standard benchmarks, or if they wanted to learn how transformer-style models behave on a particular dataset. The maintainers note that T2T was created by the Google Brain team and is now deprecated, bug fixes are still accepted, but users are encouraged to move to the successor library called Trax. The full README is longer than what was provided.