(self,
                 vocab_size,
                 embedding_size=128,
                 hidden_size=4096,
                 num_hidden_layers=12,
                 num_hidden_groups=1,
                 num_attention_heads=64,
                 intermediate_size=16384,
                 inner_group_num=1,
                 down_scale_factor=1,
                 hidden_act="gelu",
                 hidden_dropout_prob=0,
                 attention_probs_dropout_prob=0,
                 max_position_embeddings=512,
                 type_vocab_size=2,
                 initializer_range=0.02)
Constructs AlbertConfig.
        Args:
          vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`.
          embedding_size: size of voc embeddings.
          hidden_size: Size of the encoder layers and the pooler layer.
          num_hidden_layers: Number of hidden layers in the Transformer encoder.
          num_hidden_groups: Number of group for the hidden layers, parameters in
            the same group are shared.
          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.
          inner_group_num: int, number of inner repetition of attention and ffn.
          down_scale_factor: float, the scale to apply
          hidden_act: The non-linear activation function (function or string) in the
            encoder and pooler.
          hidden_dropout_prob: The dropout probability 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
            `AlbertModel`.
          initializer_range: The stdev of the truncated_normal_initializer for
            initializing all weight matrices.
Constructs AlbertConfig.
        Args:
          vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`.
          embedding_size: size of voc embeddings.
          hidden_size: Size of the encoder layers and the pooler layer.
          num_hidden_layers: Number of hidden layers in the Transformer encoder.
          num_hidden_groups: Number of group for the hidden layers, parameters in
            the same group are shared.
          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.
          inner_group_num: int, number of inner repetition of attention and ffn.
          down_scale_factor: float, the scale to apply
          hidden_act: The non-linear activation function (function or string) in the
            encoder and pooler.
          hidden_dropout_prob: The dropout probability 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
            `AlbertModel`.
          initializer_range: The stdev of the truncated_normal_initializer for
            initializing all weight matrices.
Constructs AlbertConfig . Args : vocab_size : Vocabulary size of inputs_ids in AlbertModel . embedding_size : size of voc embeddings . hidden_size : Size of the encoder layers and the pooler layer . num_hidden_layers : Number of hidden layers in the Transformer encoder . num_hidden_groups : Number of group for the hidden layers parameters in the same group are shared . 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 . inner_group_num : int number of inner repetition of attention and ffn . down_scale_factor : float the scale to apply hidden_act : The non - linear activation function ( function or string ) in the encoder and pooler . hidden_dropout_prob : The dropout probability 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 AlbertModel . initializer_range : The stdev of the truncated_normal_initializer for initializing all weight matrices .
def __init__(self,
                 vocab_size,
                 embedding_size=128,
                 hidden_size=4096,
                 num_hidden_layers=12,
                 num_hidden_groups=1,
                 num_attention_heads=64,
                 intermediate_size=16384,
                 inner_group_num=1,
                 down_scale_factor=1,
                 hidden_act="gelu",
                 hidden_dropout_prob=0,
                 attention_probs_dropout_prob=0,
                 max_position_embeddings=512,
                 type_vocab_size=2,
                 initializer_range=0.02):
        """Constructs AlbertConfig.
        Args:
          vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`.
          embedding_size: size of voc embeddings.
          hidden_size: Size of the encoder layers and the pooler layer.
          num_hidden_layers: Number of hidden layers in the Transformer encoder.
          num_hidden_groups: Number of group for the hidden layers, parameters in
            the same group are shared.
          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.
          inner_group_num: int, number of inner repetition of attention and ffn.
          down_scale_factor: float, the scale to apply
          hidden_act: The non-linear activation function (function or string) in the
            encoder and pooler.
          hidden_dropout_prob: The dropout probability 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
            `AlbertModel`.
          initializer_range: The stdev of the truncated_normal_initializer for
            initializing all weight matrices.
        """
        self.vocab_size = vocab_size
        self.embedding_size = embedding_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_hidden_groups = num_hidden_groups
        self.num_attention_heads = num_attention_heads
        self.inner_group_num = inner_group_num
        self.down_scale_factor = down_scale_factor
        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 , embedding_size = 128 , hidden_size = 4096 , num_hidden_layers = 12 , num_hidden_groups = 1 , num_attention_heads = 64 , intermediate_size = 16384 , inner_group_num = 1 , down_scale_factor = 1 , hidden_act = "gelu" , hidden_dropout_prob = 0 , attention_probs_dropout_prob = 0 , max_position_embeddings = 512 , type_vocab_size = 2 , initializer_range = 0.02 ) : self . vocab_size = vocab_size self . embedding_size = embedding_size self . hidden_size = hidden_size self . num_hidden_layers = num_hidden_layers self . num_hidden_groups = num_hidden_groups self . num_attention_heads = num_attention_heads self . inner_group_num = inner_group_num self . down_scale_factor = down_scale_factor 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