Source code for transformers.models.albert.configuration_albert

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""" ALBERT model configuration """

from ...configuration_utils import PretrainedConfig

    "albert-base-v1": "",
    "albert-large-v1": "",
    "albert-xlarge-v1": "",
    "albert-xxlarge-v1": "",
    "albert-base-v2": "",
    "albert-large-v2": "",
    "albert-xlarge-v2": "",
    "albert-xxlarge-v2": "",

[docs]class AlbertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.AlbertModel` or a :class:`~transformers.TFAlbertModel`. It is used to instantiate an ALBERT 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 `xxlarge <>`__ 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 30000): 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:`~transformers.AlbertModel` or :class:`~transformers.TFAlbertModel`. embedding_size (:obj:`int`, `optional`, defaults to 128): Dimensionality of vocabulary embeddings. hidden_size (:obj:`int`, `optional`, defaults to 4096): 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_hidden_groups (:obj:`int`, `optional`, defaults to 1): Number of groups for the hidden layers, parameters in the same group are shared. num_attention_heads (:obj:`int`, `optional`, defaults to 64): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 16384): The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. inner_group_num (:obj:`int`, `optional`, defaults to 1): The number of inner repetition of attention and ffn. hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` 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. attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0): 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 (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.AlbertModel` or :class:`~transformers.TFAlbertModel`. 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. 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.) <>`__. 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.) <>`__. Examples:: >>> from transformers import AlbertConfig, AlbertModel >>> # Initializing an ALBERT-xxlarge style configuration >>> albert_xxlarge_configuration = AlbertConfig() >>> # Initializing an ALBERT-base style configuration >>> albert_base_configuration = AlbertConfig( ... hidden_size=768, ... num_attention_heads=12, ... intermediate_size=3072, ... ) >>> # Initializing a model from the ALBERT-base style configuration >>> model = AlbertModel(albert_xxlarge_configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "albert" def __init__( self, vocab_size=30000, 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, hidden_act="gelu_new", hidden_dropout_prob=0, attention_probs_dropout_prob=0, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, classifier_dropout_prob=0.1, position_embedding_type="absolute", pad_token_id=0, bos_token_id=2, eos_token_id=3, **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) 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.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 self.layer_norm_eps = layer_norm_eps self.classifier_dropout_prob = classifier_dropout_prob self.position_embedding_type = position_embedding_type