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# Copyright 2021 TF-Transformers Authors.
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# ====================================================================
""" Bert model configuration """
from tf_transformers.core import TransformerConfig
[docs]class BertConfig(TransformerConfig):
r"""
This is the configuration class to store the configuration of a :class:`~tf_transformers.models.BertModel`.
It is used to instantiate an BERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the
ALBERT `base <https://huggingface.co/bert-base-uncased>`__ architecture.
Configuration objects inherit from :class:`~tf_transformers.models.TransformerConfig` and can be used to control the model
outputs. Read the documentation from :class:`~tf_transformers.models.TransformerConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 30522):
Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~tf_transformers.model.BertModel` or
:class:`~tf_transformers.models.BertEncoder`.
embedding_size (:obj:`int`, `optional`, defaults to 128):
Dimensionality of vocabulary embeddings.
embedding_projection_size (:obj:`int`):
Dimensionality of the encoder layers and the pooler layer. Useful for Bert.
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
attention_head_size (:obj:`int`):
Size of attention heads in each layer. Normally (embedding_size//num_attention_heads).
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and many are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
(e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or
:class:`~transformers.TFBertModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-12):
The epsilon used by the layer normalization layers.
classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for attached classifiers.
position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
:obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
<https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
`Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
<https://arxiv.org/abs/2009.13658>`__.
num_hidden_groups (:obj:`int`, `optional`, defaults to 1):
Number of groups for the hidden layers, parameters in the same group are shared.
Examples::
>>> from tf_transformers.models import BertConfig, BertModel
>>> # Initializing an bert-base-uncased style configuration
>>> configuration = BertConfig()
>>> # Initializing an Bert different style configuration
>>> configuration_new = BertConfig(
... embedding_size=768,
... num_attention_heads=12,
... intermediate_size=3072,
... )
>>> # Initializing a model from the original configuration
>>> model = BertModel.from_config(configuration)
>>> # Accessing the model configuration
>>> configuration = model._config_dict # This has more details than original configuration
"""
def __init__(
self,
vocab_size=30522,
embedding_size=768,
num_hidden_layers=12,
num_attention_heads=12,
attention_head_size=64,
intermediate_size=3072,
hidden_act="gelu",
intermediate_act="gelu",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_epsilon=1e-12,
position_embedding_type="absolute",
):
super().__init__(
vocab_size=vocab_size,
embedding_size=embedding_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
attention_head_size=attention_head_size,
hidden_act=hidden_act,
intermediate_act=intermediate_act,
intermediate_size=intermediate_size,
hidden_dropout_prob=hidden_dropout_prob,
attention_probs_dropout_prob=attention_probs_dropout_prob,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
initializer_range=initializer_range,
layer_norm_epsilon=layer_norm_epsilon,
position_embedding_type=position_embedding_type,
)