tfm.nlp.networks.BertEncoderV2

Bi-directional Transformer-based encoder network.

tfm.nlp.networks.BertEncoderV2(
    vocab_size: int,
    hidden_size: int = 768,
    num_layers: int = 12,
    num_attention_heads: int = 12,
    max_sequence_length: int = 512,
    type_vocab_size: int = 16,
    inner_dim: int = 3072,
    inner_activation: _Activation = _approx_gelu,
    output_dropout: float = 0.1,
    attention_dropout: float = 0.1,
    initializer: _Initializer = tf.keras.initializers.TruncatedNormal(stddev=0.02),
    output_range: Optional[int] = None,
    embedding_width: Optional[int] = None,
    embedding_layer: Optional[tf.keras.layers.Layer] = None,
    norm_first: bool = False,
    with_dense_inputs: bool = False,
    return_attention_scores: bool = False,
    **kwargs
)

This network implements a bi-directional Transformer-based encoder as described in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (https://arxiv.org/abs/1810.04805). It includes the embedding lookups and transformer layers, but not the masked language model or classification task networks.

The default values for this object are taken from the BERT-Base implementation in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding".

Args
vocab_size	The size of the token vocabulary.
hidden_size	The size of the transformer hidden layers.
num_layers	The number of transformer layers.
num_attention_heads	The number of attention heads for each transformer. The hidden size must be divisible by the number of attention heads.
max_sequence_length	The maximum sequence length that this encoder can consume. This determines the variable shape for positional embeddings.
type_vocab_size	The number of types that the 'type_ids' input can take.
inner_dim	The output dimension of the first Dense layer in a two-layer feedforward network for each transformer.
inner_activation	The activation for the first Dense layer in a two-layer feedforward network for each transformer.
output_dropout	Dropout probability for the post-attention and output dropout.
attention_dropout	The dropout rate to use for the attention layers within the transformer layers.
initializer	The initialzer to use for all weights in this encoder.
output_range	The sequence output range, [0, output_range), by slicing the target sequence of the last transformer layer. None means the entire target sequence will attend to the source sequence, which yields the full output.
embedding_width	The width of the word embeddings. If the embedding width is not equal to hidden size, embedding parameters will be factorized into two matrices in the shape of ['vocab_size', 'embedding_width'] and 'embedding_width', 'hidden_size'.
embedding_layer	An optional Layer instance which will be called to generate embeddings for the input word IDs.
norm_first	Whether to normalize inputs to attention and intermediate dense layers. If set False, output of attention and intermediate dense layers is normalized.
with_dense_inputs	Whether to accept dense embeddings as the input.
return_attention_scores	Whether to add an additional output containing the attention scores of all transformer layers. This will be a list of length num_layers, and each element will be in the shape [batch_size, num_attention_heads, seq_dim, seq_dim].

Attributes
pooler_layer	The pooler dense layer after the transformer layers.
transformer_layers	List of Transformer layers in the encoder.

Methods

call

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call(
    inputs
)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances, in __init__(), or in the build() method that is called automatically before call() executes for the first time.

Args
inputs	Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument. NumPy array or Python scalar values in inputs get cast as tensors. Keras mask metadata is only collected from inputs. Layers are built (build(input_shape) method) using shape info from inputs only. input_spec compatibility is only checked against inputs. Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually. The SavedModel input specification is generated using inputs only. Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args	Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs	Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference. mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns
A tensor or list/tuple of tensors.

get_embedding_layer

View source

get_embedding_layer()

get_embedding_table

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get_embedding_table()