official/README-TPU.md
# Offically Supported TensorFlow 2.1+ Models on Cloud TPU
## Natural Language Processing
* [bert](https://arxiv.org/abs/1810.04805): 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/ai-platform/training/docs/algorithms/bert-start) 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](https://github.com/tensorflow/models/blob/master/official/vision/modeling/backbones/efficientnet.py): 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](https://www.tensorflow.org/datasets/catalog/mnist): 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](https://www.tensorflow.org/api_docs/python/tfm/vision/configs/maskrcnn/MaskRCNN): An object detection and instance segmentation model. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/LH7k0fMsRwqUAcE09o9kPA).
* [resnet]((https://www.tensorflow.org/api_docs/python/tfm/vision/configs/image_classification/image_classification_imagenet)): 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](https://www.tensorflow.org/api_docs/python/tfm/vision/retinanet): A fast and powerful object detector. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/b8NRnWU3TqG6Rw0UxueU6Q).
* [shapemask](https://cloud.google.com/tpu/docs/tutorials/shapemask-2.x): An object detection and instance segmentation model using shape priors. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/ZbXgVoc6Rf6mBRlPj0JpLA).
## Recommendation
* [dlrm](recommendation/ranking): [Deep Learning Recommendation Model for
Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091).
* [dcn v2](recommendation/ranking): [Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535).
* [ncf](recommendation): Neural Collaborative Filtering. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/0k3gKjZlR1ewkVTRyLB6IQ).