DistilBertBackbone
classkeras_nlp.models.DistilBertBackbone(
vocabulary_size,
num_layers,
num_heads,
hidden_dim,
intermediate_dim,
dropout=0.1,
max_sequence_length=512,
dtype=None,
**kwargs
)
A DistilBERT encoder network.
This network implements a bi-directional Transformer-based encoder as described in "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter". It includes the embedding lookups and transformer layers, but not the masked language model or classification task networks.
The default constructor gives a fully customizable, randomly initialized
DistilBERT 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. The underlying model is provided by a third party and subject to a separate license, available here.
Arguments
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.Examples
input_data = {
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}
# Pretrained DistilBERT encoder.
model = keras_nlp.models.DistilBertBackbone.from_preset(
"distil_bert_base_en_uncased"
)
model(input_data)
# Randomly initialized DistilBERT encoder with custom config.
model = keras_nlp.models.DistilBertBackbone(
vocabulary_size=30552,
num_layers=4,
num_heads=4,
hidden_dim=256,
intermediate_dim=512,
max_sequence_length=128,
)
model(input_data)
from_preset
methodDistilBertBackbone.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 |
---|---|---|
distil_bert_base_en_uncased | 66.36M | 6-layer DistilBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus using BERT as the teacher model. |
distil_bert_base_en | 65.19M | 6-layer DistilBERT model where case is maintained. Trained on English Wikipedia + BooksCorpus using BERT as the teacher model. |
distil_bert_base_multi | 134.73M | 6-layer DistilBERT model where case is maintained. Trained on Wikipedias of 104 languages |
token_embedding
propertykeras_nlp.models.DistilBertBackbone.token_embedding
A keras.layers.Embedding
instance for embedding token ids.
This layer embeds integer token ids to the hidden dim of the model.