Keras 3 API documentation / KerasNLP / Models / FNet / FNetPreprocessor layer

FNetPreprocessor layer

[source]

FNetPreprocessor class

keras_nlp.models.FNetPreprocessor(
    tokenizer, sequence_length=512, truncate="round_robin", **kwargs
)

An FNet preprocessing layer which tokenizes and packs inputs.

This preprocessing layer will do three things:

  1. Tokenize any number of input segments using the tokenizer.
  2. Pack the inputs together using a keras_nlp.layers.MultiSegmentPacker. with the appropriate "[CLS]", "[SEP]" and "<pad>" tokens.
  3. Construct a dictionary with keys "token_ids", and "segment_ids" that can be passed directly to keras_nlp.models.FNetBackbone.

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.

Arguments

  • tokenizer: A keras_nlp.models.FNetTokenizer instance.
  • sequence_length: The length of the packed inputs.
  • truncate: string. The algorithm to truncate a list of batched segments to fit within sequence_length. The value can be either round_robin or waterfall: - "round_robin": Available space is assigned one token at a time in a round-robin fashion to the inputs that still need some, until the limit is reached. - "waterfall": The allocation of the budget is done using a "waterfall" algorithm that allocates quota in a left-to-right manner and fills up the buckets until we run out of budget. It supports an arbitrary number of segments.

Call arguments

  • x: A tensor of single string sequences, or a tuple of multiple tensor sequences to be packed together. Inputs may be batched or unbatched. For single sequences, raw python inputs will be converted to tensors. For multiple sequences, pass tensors directly.
  • y: Any label data. Will be passed through unaltered.
  • sample_weight: Any label weight data. Will be passed through unaltered.

Examples

Directly calling the from_preset().

preprocessor = keras_nlp.models.FNetPreprocessor.from_preset(
    "f_net_base_en"
)

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

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

# Preprocess a batch of sentence pairs.
# When handling multiple sequences, always convert to tensors first!
first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
second = tf.constant(["The fox tripped.", "Oh look, a whale."])
preprocessor((first, second))

Mapping with tf.data.Dataset.

preprocessor = keras_nlp.models.FNetPreprocessor.from_preset(
    "f_net_base_en"
)
first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
second = tf.constant(["The fox tripped.", "Oh look, a whale."])
label = tf.constant([1, 1])

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

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

# Map labeled sentence pairs.
ds = tf.data.Dataset.from_tensor_slices(((first, second), label))
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

# Map unlabeled sentence pairs.
ds = tf.data.Dataset.from_tensor_slices((first, second))

# Watch out for tf.data's default unpacking of tuples here!
# Best to invoke the `preprocessor` directly in this case.
ds = ds.map(
    lambda first, second: preprocessor(x=(first, second)),
    num_parallel_calls=tf.data.AUTOTUNE,
)

[source]

from_preset method

FNetPreprocessor.from_preset()

Instantiate FNetPreprocessor from preset architecture.

Arguments

  • preset: string. Must be one of "f_net_base_en", "f_net_large_en".

Examples

# Load a preprocessor layer from a preset.
preprocessor = keras_nlp.models.FNetPreprocessor.from_preset(
    "f_net_base_en",
)
Preset name Parameters Description
f_net_base_en 82.86M 12-layer FNet model where case is maintained. Trained on the C4 dataset.
f_net_large_en 236.95M 24-layer FNet model where case is maintained. Trained on the C4 dataset.

tokenizer property

keras_nlp.models.FNetPreprocessor.tokenizer

The tokenizer used to tokenize strings.