| # Offically Supported TensorFlow 2.1+ Models on Cloud TPU | |
| ## Natural Language Processing | |
| * [bert](nlp/bert): A powerful pre-trained language representation model: | |
| BERT, which stands for Bidirectional Encoder Representations from | |
| Transformers. | |
| [BERT FineTuning with Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/bert-2.x) provides step by step instructions on Cloud TPU training. You can look [Bert MNLI Tensorboard.dev metrics](https://tensorboard.dev/experiment/LijZ1IrERxKALQfr76gndA) for MNLI fine tuning task. | |
| * [transformer](nlp/transformer): A transformer model to translate the WMT | |
| English to German dataset. | |
| [Training transformer on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/transformer-2.x) for step by step instructions on Cloud TPU training. | |
| ## Computer Vision | |
| * [efficientnet](vision/image_classification): A family of convolutional | |
| neural networks that scale by balancing network depth, width, and | |
| resolution and can be used to classify ImageNet's dataset of 1000 classes. | |
| See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/KnaWjrq5TXGfv0NW5m7rpg/#scalars). | |
| * [mnist](vision/image_classification): A basic model to classify digits | |
| from the MNIST dataset. See [Running MNIST on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/mnist-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/mIah5lppTASvrHqWrdr6NA). | |
| * [mask-rcnn](vision/detection): An object detection and instance segmentation model. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/LH7k0fMsRwqUAcE09o9kPA). | |
| * [resnet](vision/image_classification): A deep residual network that can | |
| be used to classify ImageNet's dataset of 1000 classes. | |
| See [Training ResNet on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/resnet-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/CxlDK8YMRrSpYEGtBRpOhg). | |
| * [retinanet](vision/detection): A fast and powerful object detector. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/b8NRnWU3TqG6Rw0UxueU6Q). | |