To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

Then you can load T5 with [TFAutoModelForSeq2SeqLM]:

from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     tokenized_billsum["train"],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_test_set = model.prepare_tf_dataset(
     tokenized_billsum["test"],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile.