MoonshineTokenizer

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MoonshineTokenizer class

keras_hub.tokenizers.MoonshineTokenizer(proto, **kwargs)

Moonshine tokenizer layer based on keras_hub.models.LlamaTokenizer.

This tokenizer class is an alias of LlamaTokenizer but for the Moonshine model. It uses a SentencePiece vocabulary to handle tokenization.

Arguments

• proto: str or bytes. Either a string path to a SentencePiece proto file or a bytes object containing a serialized SentencePiece proto. See the SentencePiece repository for details on the format.
• **kwargs: Additional keyword arguments passed to the parent LlamaTokenizer.

Examples

from keras_hub.tokenizers import MoonshineTokenizer

# Initialize tokenizer.
tokenizer = MoonshineTokenizer(
    "keras_hub/src/tests/test_data/llama_test_vocab.spm"
)

# Single input example.
single_input = "the quick brown fox"
single_tokens = tokenizer(single_input)
print("Single input tokenization:")
print(f"Input text: {single_input}")
print(f"Tokenized: {single_tokens}")

# Batched input example.
batch_input = ["the quick brown fox", "the earth is round"]
batch_tokens = tokenizer(batch_input)
print("Batch input tokenization:")
print(f"Input texts: {batch_input}")
print(f"Tokenized: {batch_tokens}")

# Detokenization example.
encoded = tokenizer(single_input)
decoded = tokenizer.detokenize(encoded)
print("Detokenization:")
print(f"Original text: {single_input}")
print(f"Encoded: {encoded}")
print(f"Decoded: {decoded}")

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from_preset method

MoonshineTokenizer.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])