README.md

---
library_name: keras-hub
---
### Model Overview
⚠️ T5 is currently only available via the `keras-hub-nightly` package. Use `pip install keras-hub-nightly` to try this model.

T5 encoder-decoder backbone model.

T5 is a LLM pretrained on a mix of unsupervised and supervised tasks,
where each task is converted to a sequence-to-sequence format.
T5 works well on a variety of tasks out-of-the-box by prepending
various prefixes to the input sequence, e.g., for translation:
`"translate English to German: ..."`, for summarization:
`"summarize: ..."`.

T5 was introduced in
[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)

The default constructor gives a fully customizable, randomly initialized T5
model with any number of layers, heads, and embedding dimensions. To load
preset architectures and weights, use the `from_preset` constructor.

Disclaimer: Pre-trained models are provided on an "as is" basis, without
warranties or conditions of any kind.

## Links

* [T5 Quickstart Notebook](coming soon)
* [T5  API Documentation](https://keras.io/keras_hub/api/models/t5/)
* [T5 Model Card](https://github.com/google-research/text-to-text-transfer-transformer/tree/main)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)

## Installation

Keras and KerasHub can be installed with:

```
pip install -U -q keras-hub
pip install -U -q keras
```

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.

## Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name    | Parameters | Description                                      |
|----------------|------------|--------------------------------------------------|
| t5_small_multi |   0    | 8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).|
| t5_base_multi|    0  | 12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| t5_large_multi |   0   | 24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).   |
| flan_small_multi |   0    |  8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).  |
| flan_base_multi |   0    |  12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| flan_large_multi |   0    |  	24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).  |
| t5_1.1_small |   60.51M |    |
| tt5_1.1_base |  247.58M |    | 
| t5_1.1_large | 750.25M  |    | 
| t5_1.1_xl | 2.85B  |    | 
| t5_1.1_xxl | 11.14B  |    | 

__Arguments__


- __vocabulary_size__: int. The size of the token vocabulary.
- __num_layers__: int. The number of Transformer layers.
- __num_heads__: int. The number of attention heads for each Transformer.
    The hidden size must be divisible by the number of attention heads.
- __hidden_dim__: int. The hidden size of the Transformer layers.
- __intermediate_dim__: int. The output dimension of the first Dense layer in
    a two-layer feedforward network for each Transformer layer.
- __key_value_dim__: int. The dimension of each head of the key/value
    projections in the multi-head attention layers. Defaults to
    hidden_dim / num_heads.
- __dropout__: float. Dropout probability for the Transformer layers.
- __activation__: activation function (or activation string name). The
    activation to be used in the inner dense blocks of the
    Transformer layers. Defaults to `"relu"`.
- __use_gated_activation__: boolean. Whether to use activation gating in
    the inner dense blocks of the Transformer layers.
    The original T5 architecture didn't use gating, but more
    recent versions do. Defaults to `True`.
- __layer_norm_epsilon__: float. Epsilon factor to be used in the
    layer normalization layers in the Transformer layers.
- __tie_embedding_weights__: boolean. If `True`, the weights of the token
    embedding and the weights projecting language model outputs from
    `hidden_dim`