# T5v1.1
## Overview
T5v1.1 was released in the [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511)
repository by Colin Raffel et al. It's an improved version of the original T5 model.
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be
found [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511).
## Usage tips
One can directly plug in the weights of T5v1.1 into a T5 model, like so:
```python
>>> 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](https://arxiv.org/abs/2002.05202).
- 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](https://huggingface.co/datasets/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](https://huggingface.co/google/t5-v1_1-small)
- [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base)
- [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large)
- [google/t5-v1_1-xl](https://huggingface.co/google/t5-v1_1-xl)
- [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl).
Refer to [T5's documentation page](t5) for all API reference, tips, code examples and notebooks.
