Tokenizer

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Tokenizer class

keras_nlp.tokenizers.Tokenizer()

A base class for tokenizer layers.

Tokenizers in the KerasHub library should all subclass this layer. The class provides two core methods tokenize() and detokenize() for going from plain text to sequences and back. A tokenizer is a subclass of keras.layers.Layer and can be combined into a keras.Model.

Subclassers should always implement the tokenize() method, which will also be the default when calling the layer directly on inputs.

Subclassers can optionally implement the detokenize() method if the tokenization is reversible. Otherwise, this can be skipped.

Subclassers should implement get_vocabulary(), vocabulary_size(), token_to_id() and id_to_token() if applicable. For some simple "vocab free" tokenizers, such as a whitespace splitter show below, these methods do not apply and can be skipped.

Example

class WhitespaceSplitterTokenizer(keras_hub.tokenizers.Tokenizer):
    def tokenize(self, inputs):
        return tf.strings.split(inputs)

    def detokenize(self, inputs):
        return tf.strings.reduce_join(inputs, separator=" ", axis=-1)

tokenizer = WhitespaceSplitterTokenizer()

# Tokenize some inputs.
tokenizer.tokenize("This is a test")

# Shorthard for `tokenize()`.
tokenizer("This is a test")

# Detokenize some outputs.
tokenizer.detokenize(["This", "is", "a", "test"])

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from_preset method

Tokenizer.from_preset(preset, config_file="tokenizer.json", **kwargs)

Instantiate a keras_hub.models.Tokenizer 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 Tokenizer subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

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

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 preset tokenizer.
tokenizer = keras_hub.tokenizer.Tokenizer.from_preset("bert_base_en")

# Tokenize some input.
tokenizer("The quick brown fox tripped.")

# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])

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save_to_preset method

Tokenizer.save_to_preset(preset_dir)

Save tokenizer to a preset directory.

Arguments

• preset_dir: The path to the local model preset directory.