GemmaCausalLMPreprocessor layer

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GemmaCausalLMPreprocessor class

keras_hub.models.GemmaCausalLMPreprocessor(
    tokenizer, sequence_length=1024, add_start_token=True, add_end_token=True, **kwargs
)

Gemma Causal LM preprocessor.

This preprocessing layer is meant for use with keras_hub.models.GemmaCausalLM. By default, it will take in batches of strings, and return outputs in a (x, y, sample_weight) format, where the y label is the next token id in the x sequence.

For use with generation, the layer also exposes two methods generate_preprocess() and generate_postprocess(). When this preprocessor is attached to a keras_hub.models.GemmaCausalLM instance, these methods will be called implicitly in generate(). They can also be called standalone (e.g. to precompute preprocessing inputs for generation in a separate process).

Arguments

• tokenizer: A keras_hub.models.GemmaTokenizer instance.
• sequence_length: The length of the packed inputs.
• add_start_token: If True, the preprocessor will prepend the tokenizer start token to each input sequence.
• add_end_token: If True, the preprocessor will append the tokenizer end token to each input sequence.

Call arguments

• x: A string, tf.Tensor or list of python strings.
• y: Label data. Should always be None as the layer generates labels.
• sample_weight: Label weights. Should always be None as the layer generates label weights.
• sequence_length: Pass to override the configured sequence_length of the layer.

Examples

# Load the preprocessor from a preset.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en"
)

# Tokenize and pack a single sentence.
preprocessor("The quick brown fox jumped.")

# Tokenize a batch of sentences.
preprocessor(["The quick brown fox jumped.", "Call me Ishmael."])

# Apply tokenization to a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
features = tf.constant(["The quick brown fox.", "Call me Ishmael."])
ds = tf.data.Dataset.from_tensor_slices(features)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

# Prepare tokens for generation (no end token).
preprocessor.generate_preprocess(["The quick brown fox jumped."])

# Map generation outputs back to strings.
preprocessor.generate_postprocess({
    'token_ids': np.array([[2, 714, 4320, 8426, 25341, 32292, 235265, 0]]),
    'padding_mask': np.array([[ 1,  1,  1,  1,  1,  1,  1, 0]]),
})

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from_preset method

GemmaCausalLMPreprocessor.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.CausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

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

tokenizer property

keras_hub.models.GemmaCausalLMPreprocessor.tokenizer

The tokenizer used to tokenize strings.