  num_heads and d_ff.
Note: T5 Version 1.1 was only pre-trained on C4 excluding any supervised
training. Therefore, this model has to be fine-tuned before it is useable on a downstream task, unlike the original T5
model. Since t5v1.1 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
Google has released the following variants:
google/t5-v1_1-small
google/t5-v1_1-base
google/t5-v1_1-large
google/t5-v1_1-xl
google/t5-v1_1-xxl.
One can refer to T5's documentation page for all tips, code examples and notebooks.
This model was contributed by patrickvonplaten. The original code can be
found here.
