Class BertConfig

bert/configuration_bert.py:53–143

r""" This is the configuration class to store the configuration of a :class:`~transformers.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 s

class BertConfig(PretrainedConfig):
    r"""
        This is the configuration class to store the configuration of a :class:`~transformers.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 BERT `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture.

        Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
        to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
        for more information.


        Args:
            vocab_size (:obj:`int`, optional, defaults to 30522):
                Vocabulary size of the BERT model. Defines the different tokens that
                can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
            hidden_size (:obj:`int`, optional, defaults to 768):
                Dimensionality of the encoder layers and the pooler layer.
            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.
            intermediate_size (:obj:`int`, optional, defaults to 3072):
                Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
            hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
                The non-linear activation function (function or string) in the encoder and pooler.
                If string, "gelu", "relu", "swish" and "gelu_new" are supported.
            hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
                The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
                The dropout ratio for the attention probabilities.
            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 just in case (e.g., 512 or 1024 or 2048).
            type_vocab_size (:obj:`int`, optional, defaults to 2):
                The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
            initializer_range (:obj:`float`, optional, defaults to 0.02):
                The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
            layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
                The epsilon used by the layer normalization layers.
            gradient_checkpointing (:obj:`bool`, optional, defaults to False):
                If True, use gradient checkpointing to save memory at the expense of slower backward pass.

        Example::

            >>> from transformers import BertModel, BertConfig

            >>> # Initializing a BERT bert-base-uncased style configuration
            >>> configuration = BertConfig()

            >>> # Initializing a model from the bert-base-uncased style configuration
            >>> model = BertModel(configuration)

            >>> # Accessing the model configuration
            >>> configuration = model.config
    """
    model_type = "bert"