T5v1.1 was released in the google-research/text-to-text-transfer-transformer repository by Colin Raffel et al. It’s an improved version of the original T5 model.
One can directly plug in the weights of T5v1.1 into a T5 model, like so:
from transformers import T5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained('google/t5-v1_1-base')
T5 Version 1.1 includes the following improvements compared to the original T5 model:
GEGLU activation in the feed-forward hidden layer, rather than ReLU. See this paper.
Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
Pre-trained on C4 only without mixing in the downstream tasks.
No parameter sharing between the embedding and classifier layer.
“xl” and “xxl” replace “3B” and “11B”. The model shapes are a bit different - larger
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:
One can refer to T5’s documentation page for all tips, code examples and notebooks.