Source code for transformers.models.deberta.configuration_deberta

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# Copyright 2020, Microsoft and the HuggingFace Inc. team.
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""" DeBERTa model configuration """

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

logger = logging.get_logger(__name__)

    "microsoft/deberta-base": "",
    "microsoft/deberta-large": "",
    "microsoft/deberta-xlarge": "",
    "microsoft/deberta-base-mnli": "",
    "microsoft/deberta-large-mnli": "",
    "microsoft/deberta-xlarge-mnli": "",

[docs]class DebertaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.DebertaModel` or a :class:`~transformers.TFDebertaModel`. It is used to instantiate a DeBERTa 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 DeBERTa `microsoft/deberta-base <>`__ 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. Arguments: vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.DebertaModel` or :class:`~transformers.TFDebertaModel`. 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" (often named 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"`, :obj:`"gelu"`, :obj:`"tanh"`, :obj:`"gelu_fast"`, :obj:`"mish"`, :obj:`"linear"`, :obj:`"sigmoid"` 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.DebertaModel` or :class:`~transformers.TFDebertaModel`. 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. relative_attention (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether use relative position encoding. max_relative_positions (:obj:`int`, `optional`, defaults to 1): The range of relative positions :obj:`[-max_position_embeddings, max_position_embeddings]`. Use the same value as :obj:`max_position_embeddings`. pad_token_id (:obj:`int`, `optional`, defaults to 0): The value used to pad input_ids. position_biased_input (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether add absolute position embedding to content embedding. pos_att_type (:obj:`List[str]`, `optional`): The type of relative position attention, it can be a combination of :obj:`["p2c", "c2p", "p2p"]`, e.g. :obj:`["p2c"]`, :obj:`["p2c", "c2p"]`, :obj:`["p2c", "c2p", 'p2p"]`. layer_norm_eps (:obj:`float`, optional, defaults to 1e-12): The epsilon used by the layer normalization layers. """ model_type = "deberta" def __init__( self, vocab_size=50265, 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=0, initializer_range=0.02, layer_norm_eps=1e-7, relative_attention=False, max_relative_positions=-1, pad_token_id=0, position_biased_input=True, pos_att_type=None, pooler_dropout=0, pooler_hidden_act="gelu", **kwargs ): super().__init__(**kwargs) 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.relative_attention = relative_attention self.max_relative_positions = max_relative_positions self.pad_token_id = pad_token_id self.position_biased_input = position_biased_input # Backwards compatibility if type(pos_att_type) == str: pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] self.pos_att_type = pos_att_type self.vocab_size = vocab_size self.layer_norm_eps = layer_norm_eps self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size) self.pooler_dropout = pooler_dropout self.pooler_hidden_act = pooler_hidden_act