ESMBackbone model

[source]

ESMBackbone class

keras_hub.models.ESMBackbone(
    vocabulary_size,
    num_layers,
    num_heads,
    hidden_dim,
    intermediate_dim,
    use_bias=True,
    activation="gelu",
    dropout=0.1,
    dtype=None,
    max_sequence_length=1024,
    max_wavelength=10000,
    layer_norm_eps=1e-12,
    use_pre_layer_norm=False,
    position_embedding_type="rotary",
    pad_token_id=0,
    **kwargs
)

A ESM2 and ESM encoder network.

This class implements a bi-directional Transformer-based encoder as described in "ESM".

The default constructor gives a fully customizable, randomly initialized ESM2 encoder with any number of layers, heads, and embed dim.To load preset architectures and weights, use the from_preset() constructor.

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 and pooler layers.
• 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 Transformer encoder. Defaults to 0.1
• use_pre_layer_norm:bool.If true, then layer norm will be used before entering the transformer block. Since it's pre-norm, the default is false.
• 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.
• position_embedding_type: str. The position embedding type to use. One of "absolute" and "rotary". Use "absolute" for ESM1. Use "rotary" for ESM2. Defaults to "rotary"
• max_wavelength : int. The maximum angular wavelength of the sine/cosine curves, for rotary embeddings. Defaults to 10000.
• activation :string or keras.activations. The activation to use for the transformer. Defaults to "gelu".
• pad_token_id: int.padding token id. Normally 0, but is set to 1 in the esm2 model. Defaults to 0.
• dtype: None or str 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"),
}

# Pretrained ESM2 encoder.
model = keras_hub.models.ESM2Backbone.from_preset('hf://facebook/esm2_t6_8M_UR50D')
model(input_data)

# Randomly initialized ESM2 encoder with a custom config.
model = keras_hub.models.ESM2Backbone(
    vocabulary_size=30552,
    num_layers=4,
    num_heads=4,
    hidden_dim=256,
    intermediate_dim=512,
)
model(input_data)

[source]

from_preset method

ESMBackbone.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.ESMBackbone.token_embedding

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

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