Keras 3 API documentation / KerasNLP / Models / Albert / AlbertBackbone model

AlbertBackbone model

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AlbertBackbone class

keras_nlp.models.AlbertBackbone(
    vocabulary_size,
    num_layers,
    num_heads,
    embedding_dim,
    hidden_dim,
    intermediate_dim,
    num_groups=1,
    num_inner_repetitions=1,
    dropout=0.0,
    max_sequence_length=512,
    num_segments=2,
    dtype=None,
    **kwargs
)

ALBERT encoder network.

This class implements a bi-directional Transformer-based encoder as described in "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations". ALBERT is a more efficient variant of BERT, and uses parameter reduction techniques such as cross-layer parameter sharing and factorized embedding parameterization. This model class includes the embedding lookups and transformer layers, but not the masked language model or sentence order prediction heads.

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

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, must be divisible by num_groups. The number of "virtual" layers, i.e., the total number of times the input sequence will be fed through the groups in one forward pass. The input will be routed to the correct group based on the layer index.
  • num_heads: int. The number of attention heads for each transformer. The hidden size must be divisible by the number of attention heads.
  • embedding_dim: int. The size of the embeddings.
  • hidden_dim: int. The size of the transformer encoding and pooler layers.
  • intermediate_dim: int. The output dimension of the first Dense layer in a two-layer feedforward network for each transformer.
  • num_groups: int. Number of groups, with each group having num_inner_repetitions number of TransformerEncoder layers.
  • num_inner_repetitions: int. Number of TransformerEncoder layers per group.
  • dropout: float. Dropout probability for the Transformer 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]]),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Randomly initialized ALBERT encoder
model = keras_nlp.models.AlbertBackbone(
    vocabulary_size=30000,
    num_layers=12,
    num_heads=12,
    num_groups=1,
    num_inner_repetitions=1,
    embedding_dim=128,
    hidden_dim=768,
    intermediate_dim=3072,
    max_sequence_length=12,
)
output = model(input_data)

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from_preset method

AlbertBackbone.from_preset()

Instantiate AlbertBackbone model from preset architecture and weights.

Arguments

  • preset: string. Must be one of "albert_base_en_uncased", "albert_large_en_uncased", "albert_extra_large_en_uncased", "albert_extra_extra_large_en_uncased".
  • load_weights: Whether to load pre-trained weights into model. Defaults to True.

Examples

# Load architecture and weights from preset
model = keras_nlp.models.AlbertBackbone.from_preset(
    "albert_base_en_uncased"
)

# Load randomly initialized model from preset architecture
model = keras_nlp.models.AlbertBackbone.from_preset(
    "albert_base_en_uncased",
    load_weights=False
)
Preset name Parameters Description
albert_base_en_uncased 11.68M 12-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.
albert_large_en_uncased 17.68M 24-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.
albert_extra_large_en_uncased 58.72M 24-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.
albert_extra_extra_large_en_uncased 222.60M 12-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus.

token_embedding property

keras_nlp.models.AlbertBackbone.token_embedding

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

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