T5Backbone model

[source]

T5Backbone class

keras_hub.models.T5Backbone(
    vocabulary_size,
    num_layers,
    num_heads,
    hidden_dim,
    intermediate_dim,
    key_value_dim=None,
    dropout=0.1,
    activation="relu",
    use_gated_activation=True,
    layer_norm_epsilon=1e-06,
    tie_embedding_weights=True,
    dtype=None,
    **kwargs
)

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 prefixex 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.

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.
• dtype: string or keras.mixed_precision.DTypePolicy. The dtype to use for model computations and weights. Note that some computations, such as softmax and layer normalization, will always be done at float32 precision regardless of dtype.

[source]

from_preset method

T5Backbone.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Backbone from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as a one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

This constructor can be called in one of two ways. Either from the base class like keras_hub.models.Backbone.from_preset(), or from a model class like keras_hub.models.GemmaBackbone.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

For any Backbone subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a Gemma backbone with pre-trained weights.
model = keras_hub.models.Backbone.from_preset(
    "gemma_2b_en",
)

# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)

token_embedding property

keras_hub.models.T5Backbone.token_embedding

A keras.layers.Embedding instance for embedding token ids.

This layer embeds integer token ids to the hidden dim of the model.