(self,
                vocab_size,
                hidden_size=768,
                num_hidden_layers=12,
                num_attention_heads=12,
                intermediate_size=3072,
                hidden_act="gelu",
                hidden_dropout_prob=0.1,
                attention_probs_dropout_prob=0.1,
                max_position_embeddings=512,
                type_vocab_size=16,
                initializer_range=0.02)
Constructs BertConfig.

        Args:
            vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
            hidden_size: Size of the encoder layers and the pooler layer.
            num_hidden_layers: Number of hidden layers in the Transformer encoder.
            num_attention_heads: Number of attention heads for each attention layer in
                the Transformer encoder.
            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                layer in the Transformer encoder.
            hidden_act: The non-linear activation function (function or string) in the
                encoder and pooler.
            hidden_dropout_prob: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob: The dropout ratio for the attention
                probabilities.
            max_position_embeddings: 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: The vocabulary size of the `token_type_ids` passed into
                `BertModel`.
            initializer_range: The sttdev of the truncated_normal_initializer for
                initializing all weight matrices.
Constructs BertConfig.
Constructs BertConfig .
def __init__(self,
                vocab_size,
                hidden_size=768,
                num_hidden_layers=12,
                num_attention_heads=12,
                intermediate_size=3072,
                hidden_act="gelu",
                hidden_dropout_prob=0.1,
                attention_probs_dropout_prob=0.1,
                max_position_embeddings=512,
                type_vocab_size=16,
                initializer_range=0.02):
        """Constructs BertConfig.

        Args:
            vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
            hidden_size: Size of the encoder layers and the pooler layer.
            num_hidden_layers: Number of hidden layers in the Transformer encoder.
            num_attention_heads: Number of attention heads for each attention layer in
                the Transformer encoder.
            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                layer in the Transformer encoder.
            hidden_act: The non-linear activation function (function or string) in the
                encoder and pooler.
            hidden_dropout_prob: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob: The dropout ratio for the attention
                probabilities.
            max_position_embeddings: 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: The vocabulary size of the `token_type_ids` passed into
                `BertModel`.
            initializer_range: The sttdev of the truncated_normal_initializer for
                initializing all weight matrices.
        """
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
def __init__ ( self , vocab_size , hidden_size = 768 , num_hidden_layers = 12 , num_attention_heads = 12 , intermediate_size = 3072 , hidden_act = "gelu" , hidden_dropout_prob = 0.1 , attention_probs_dropout_prob = 0.1 , max_position_embeddings = 512 , type_vocab_size = 16 , initializer_range = 0.02 ) : self . vocab_size = vocab_size self . hidden_size = hidden_size self . num_hidden_layers = num_hidden_layers self . num_attention_heads = num_attention_heads self . hidden_act = hidden_act self . intermediate_size = intermediate_size self . hidden_dropout_prob = hidden_dropout_prob self . attention_probs_dropout_prob = attention_probs_dropout_prob self . max_position_embeddings = max_position_embeddings self . type_vocab_size = type_vocab_size self . initializer_range = initializer_range