BloomPreprocessor layer

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BloomPreprocessor class

keras_nlp.models.BloomPreprocessor(
    tokenizer, sequence_length=2048, add_start_token=True, add_end_token=True, **kwargs
)

BLOOM preprocessing layer which tokenizes and packs inputs.

This preprocessing layer will do 2 things:

• Tokenize the inputs using the tokenizer.
• Construct a dictionary with keys "token_ids", "padding_mask", that can be passed directly to a keras_nlp.models.BloomBackbone.

This layer can be used directly with tf.data.Dataset.map to preprocess string data in the (x, y, sample_weight) format used by keras.Model.fit.

The call method of this layer accepts three arguments, x, y, and sample_weight. x can be a python string or tensor representing a single segment, a list of python strings representing a batch of single segments, or a list of tensors representing multiple segments to be packed together. y and sample_weight are both optional, can have any format, and will be passed through unaltered.

Arguments

• tokenizer: A keras_nlp.models.BloomTokenizer 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: Any label data. Will be passed through unaltered.
• sample_weight: Any label weight data. Will be passed through unaltered.
• sequence_length: Pass to override the configured sequence_length of the layer.

Examples

Directly calling the layer on data.

preprocessor = keras_nlp.models.BloomPreprocessor.from_preset(
    "bloom_560m_multi"
)
# Tokenize and pack a single sentence.
preprocessor("The quick brown fox jumped.")

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

# Custom vocabulary.
features = ["a quick fox.", "a fox quick."]
vocab = {"<pad>": 0, "<s>":1, "</s>":2, "a": 3, "Ġquick": 4, "Ġfox": 5}
merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
merges += ["Ġ f", "o x", "Ġf ox"]
tokenizer = keras_nlp.models.BloomTokenizer(
    vocabulary=vocab,
    merges=merges,
)
preprocessor = keras_nlp.models.BloomPreprocessor(tokenizer=tokenizer)
preprocessor("The quick brown fox jumped.")

Mapping with tf.data.Dataset.

preprocessor = keras_nlp.models.BloomPreprocessor.from_preset(
    "bloom_560m_multi"
)

text = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
label = tf.constant([1, 1])

# Map labeled single sentences.
ds = tf.data.Dataset.from_tensor_slices((text, label))
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

# Map unlabeled single sentences.
ds = tf.data.Dataset.from_tensor_slices(text)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

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from_preset method

BloomPreprocessor.from_preset(preset, **kwargs)

Instantiate a keras_nlp.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 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'

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_nlp.models.BertPreprocessor.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_nlp.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

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

tokenizer property

keras_nlp.models.BloomPreprocessor.tokenizer

The tokenizer used to tokenize strings.