T5Preprocessor layer

[source]

T5Preprocessor class

keras_hub.models.T5Preprocessor(
    tokenizer, sequence_length=256, add_start_token=False, add_end_token=True, **kwargs
)

Base class for preprocessing layers.

A Preprocessor layer provides a complete preprocessing setup for a given task. It handles tokenization, audio/image conversion, and any other necessary preprocessing steps.

This class can be subclassed similar to any keras.layers.Layer, by defining build(), call() and get_config() methods. All subclasses should set the tokenizer or audio_converter or image_converter properties during construction as needed.

[source]

from_preset method

T5Preprocessor.from_preset(preset, config_file="preprocessor.json", **kwargs)

Instantiate a keras_hub.models.Preprocessor 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 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'

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

As there are usually multiple preprocessing classes for a given model, this method should be called on a specific subclass like keras_hub.models.BertTextClassifierPreprocessor.from_preset().

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.

Examples

# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.BertTextClassifierPreprocessor.from_preset(
    "bert_base_en",
)

tokenizer property

keras_hub.models.T5Preprocessor.tokenizer

The tokenizer used to tokenize strings.