Source code for transformers.models.electra.configuration_electra

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""" ELECTRA model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

    "google/electra-small-generator": "",
    "google/electra-base-generator": "",
    "google/electra-large-generator": "",
    "google/electra-small-discriminator": "",
    "google/electra-base-discriminator": "",
    "google/electra-large-discriminator": "",

[docs]class ElectraConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel` or a :class:`~transformers.TFElectraModel`. It is used to instantiate a ELECTRA 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 ELECTRA `google/electra-small-discriminator <>`__ 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 ELECTRA model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.ElectraModel` or :class:`~transformers.TFElectraModel`. embedding_size (:obj:`int`, `optional`, defaults to 128): Dimensionality of the encoder layers and the pooler layer. hidden_size (:obj:`int`, `optional`, defaults to 256): 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 4): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 1024): Dimensionality of the "intermediate" (i.e., 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 :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout probability 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 :obj:`token_type_ids` passed when calling :class:`~transformers.ElectraModel` or :class:`~transformers.TFElectraModel`. 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. summary_type (:obj:`str`, `optional`, defaults to :obj:`"first"`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Has to be one of the following options: - :obj:`"last"`: Take the last token hidden state (like XLNet). - :obj:`"first"`: Take the first token hidden state (like BERT). - :obj:`"mean"`: Take the mean of all tokens hidden states. - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - :obj:`"attn"`: Not implemented now, use multi-head attention. summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Whether or not to add a projection after the vector extraction. summary_activation (:obj:`str`, `optional`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Pass :obj:`"gelu"` for a gelu activation to the output, any other value will result in no activation. summary_last_dropout (:obj:`float`, `optional`, defaults to 0.0): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. 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 ElectraModel, ElectraConfig >>> # Initializing a ELECTRA electra-base-uncased style configuration >>> configuration = ElectraConfig() >>> # Initializing a model from the electra-base-uncased style configuration >>> model = ElectraModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "electra" def __init__( self, vocab_size=30522, embedding_size=128, hidden_size=256, num_hidden_layers=12, num_attention_heads=4, intermediate_size=1024, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, summary_type="first", summary_use_proj=True, summary_activation="gelu", summary_last_dropout=0.1, pad_token_id=0, position_embedding_type="absolute", **kwargs ): super().__init__(pad_token_id=pad_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_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act 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.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_last_dropout = summary_last_dropout self.position_embedding_type = position_embedding_type