Tensor2Tensor

Use the algorithmic_math_two_variables problem.

Select a transformer model (e.g., transformer_tiny).

Train the model on the MLU dataset.

Evaluate the model's accuracy on mathematical expression evaluation.

Use the babi_qa_concat_task1_1k problem with the bAbi dataset.

Choose a specific bAbi task from the range [1, 20].

Train a transformer model on the selected task.

Evaluate the model's accuracy in answering questions about the story.

Use the image_imagenet problem for ImageNet or image_cifar10 for CIFAR-10.

Select a ResNet or Xception model for ImageNet, or shake-shake for CIFAR-10.

Train the model on the selected image dataset.

Evaluate the model's top-1 accuracy on image classification.

Use the languagemodel_ptb10k problem for PTB or languagemodel_lm1b32k for LM1B.

Choose a transformer model.

Train the model on the language modeling dataset.

Evaluate the model's perplexity on predicting the next word.

Use the sentiment_imdb problem with the IMDB dataset.

Select a transformer_encoder model.

Train the model on the sentiment analysis dataset.

Evaluate the model's accuracy in classifying sentiment as positive or negative.