DebertaV3Backbone model

• Original Link : https://keras.io/api/keras_hub/models/deberta_v3/deberta_v3_backbone/
• Last Checked at : 2024-11-25

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DebertaV3Backbone class

keras_hub.models.DebertaV3Backbone(
    vocabulary_size,
    num_layers,
    num_heads,
    hidden_dim,
    intermediate_dim,
    dropout=0.1,
    max_sequence_length=512,
    bucket_size=256,
    dtype=None,
    **kwargs
)

DeBERTa encoder network.

This network implements a bi-directional Transformer-based encoder as described in “DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing”. It includes the embedding lookups and transformer layers, but does not include the enhanced masked decoding head used during pretraining.

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

Note: DebertaV3Backbone has a performance issue on TPUs, and we recommend other models for TPU training and inference.

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

• vocabulary_size: int. The size of the token vocabulary.
• num_layers: int. The number of transformer layers.
• num_heads: int. The number of attention heads for each transformer. The hidden size must be divisible by the number of attention heads.
• hidden_dim: int. The size of the transformer encoding layer.
• intermediate_dim: int. The output dimension of the first Dense layer in a two-layer feedforward network for each transformer.
• dropout: float. Dropout probability for the DeBERTa model.
• max_sequence_length: int. The maximum sequence length this encoder can consume. The sequence length of the input must be less than max_sequence_length.
• bucket_size: int. The size of the relative position buckets. Generally equal to max_sequence_length // 2.
• 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.

Example

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 DeBERTa encoder.
model = keras_hub.models.DebertaV3Backbone.from_preset(
    "deberta_v3_base_en",
)
model(input_data)
# Randomly initialized DeBERTa encoder with custom config
model = keras_hub.models.DebertaV3Backbone(
    vocabulary_size=128100,
    num_layers=12,
    num_heads=6,
    hidden_dim=384,
    intermediate_dim=1536,
    max_sequence_length=512,
    bucket_size=256,
)
# Call the model on the input data.
model(input_data)

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from_preset method

DebertaV3Backbone.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.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_hub.models.Backbone.from_preset(), or from a model class like keras_hub.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_hub.models.Backbone.from_preset(
    "gemma_2b_en",
)
# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)

token_embedding property

keras_hub.models.DebertaV3Backbone.token_embedding

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

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