#
T5v1.1

##
Overview

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. This model was contributed by patrickvonplaten. The original code can be found here.

##
Usage tips

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 `d_model` and smaller `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 usable 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.

Refer to T5’s documentation page for all API reference, tips, code examples and notebooks.