FNetBackbone model

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FNetBackbone class

keras_nlp.models.FNetBackbone(
    vocabulary_size,
    num_layers,
    hidden_dim,
    intermediate_dim,
    dropout=0.1,
    max_sequence_length=512,
    num_segments=4,
    dtype=None,
    **kwargs
)

A FNet encoder network.

This class implements a bi-directional Fourier Transform-based encoder as described in "FNet: Mixing Tokens with Fourier Transforms". It includes the embedding lookups and keras_nlp.layers.FNetEncoder layers, but not the masked language model or next sentence prediction heads.

The default constructor gives a fully customizable, randomly initialized FNet encoder with any number of layers and embedding dimensions. To load preset architectures and weights, use the from_preset() constructor.

Note: unlike other models, FNet does not take in a "padding_mask" input, the "<pad>" token is handled equivalently to all other tokens in the input sequence.

Disclaimer: Pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind.

Arguments

• vocabulary_size: int. The size of the token vocabulary.
• num_layers: int. The number of FNet layers.
• hidden_dim: int. The size of the FNet encoding and pooler layers.
• intermediate_dim: int. The output dimension of the first Dense layer in a two-layer feedforward network for each FNet layer.
• dropout: float. Dropout probability for the embeddings and FNet encoder.
• max_sequence_length: int. The maximum sequence length that this encoder can consume. If None, max_sequence_length uses the value from sequence length. This determines the variable shape for positional embeddings.
• num_segments: int. The number of types that the 'segment_ids' input can take.
• dtype: string or keras.mixed_precision.DTypePolicy. The dtype to use for model computations and weights. Note that some computations, such as softmax and layer normalization, will always be done at float32 precision regardless of dtype.

Examples

input_data = {
    "token_ids": np.ones(shape=(1, 12), dtype="int32"),
    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained BERT encoder.
model = keras_nlp.models.FNetBackbone.from_preset("f_net_base_en")
model(input_data)

# Randomly initialized FNet encoder with a custom config.
model = keras_nlp.models.FNetBackbone(
    vocabulary_size=32000,
    num_layers=4,
    hidden_dim=256,
    intermediate_dim=512,
    max_sequence_length=128,
)
model(input_data)

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from_preset method

FNetBackbone.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_nlp.models.Backbone 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'

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

For any Backbone subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

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 Gemma backbone with pre-trained weights.
model = keras_nlp.models.Backbone.from_preset(
    "gemma_2b_en",
)

# Load a Bert backbone with a pre-trained config and random weights.
model = keras_nlp.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)

token_embedding property

keras_nlp.models.FNetBackbone.token_embedding

A keras.layers.Embedding instance for embedding token ids.

This layer embeds integer token ids to the hidden dim of the model.