keras/t5_base_multi

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

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
• T5 Model Card
• KerasHub Beginner Guide
• KerasHub Model Publishing Guide

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 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