AlbertBackbone
classkeras_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
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_inner_repetitions
number of TransformerEncoder
layers.TransformerEncoder
layers per
group.max_sequence_length
uses the value from
sequence length. This determines the variable shape for positional
embeddings.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.Example
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)
from_preset
methodAlbertBackbone.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:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./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
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,
)
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
propertykeras_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.