Source code for transformers.models.distilbert.configuration_distilbert

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""" DistilBERT model configuration """

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

logger = logging.get_logger(__name__)

    "distilbert-base-uncased": "",
    "distilbert-base-uncased-distilled-squad": "",
    "distilbert-base-cased": "",
    "distilbert-base-cased-distilled-squad": "",
    "distilbert-base-german-cased": "",
    "distilbert-base-multilingual-cased": "",
    "distilbert-base-uncased-finetuned-sst-2-english": "",

[docs]class DistilBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.DistilBertModel` or a :class:`~transformers.TFDistilBertModel`. It is used to instantiate a DistilBERT 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 DistilBERT `distilbert-base-uncased <>`__ 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 DistilBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.DistilBertModel` or :class:`~transformers.TFDistilBertModel`. 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). sinusoidal_pos_embds (:obj:`boolean`, `optional`, defaults to :obj:`False`): Whether to use sinusoidal positional embeddings. n_layers (:obj:`int`, `optional`, defaults to 6): Number of hidden layers in the Transformer encoder. n_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. dim (:obj:`int`, `optional`, defaults to 768): Dimensionality of the encoder layers and the pooler layer. hidden_dim (:obj:`int`, `optional`, defaults to 3072): The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder. dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention probabilities. activation (: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. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. qa_dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probabilities used in the question answering model :class:`~transformers.DistilBertForQuestionAnswering`. seq_classif_dropout (:obj:`float`, `optional`, defaults to 0.2): The dropout probabilities used in the sequence classification and the multiple choice model :class:`~transformers.DistilBertForSequenceClassification`. Examples:: >>> from transformers import DistilBertModel, DistilBertConfig >>> # Initializing a DistilBERT configuration >>> configuration = DistilBertConfig() >>> # Initializing a model from the configuration >>> model = DistilBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "distilbert" def __init__( self, vocab_size=30522, max_position_embeddings=512, sinusoidal_pos_embds=False, n_layers=6, n_heads=12, dim=768, hidden_dim=4 * 768, dropout=0.1, attention_dropout=0.1, activation="gelu", initializer_range=0.02, qa_dropout=0.1, seq_classif_dropout=0.2, pad_token_id=0, **kwargs ): super().__init__(**kwargs, pad_token_id=pad_token_id) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.sinusoidal_pos_embds = sinusoidal_pos_embds self.n_layers = n_layers self.n_heads = n_heads self.dim = dim self.hidden_dim = hidden_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation = activation self.initializer_range = initializer_range self.qa_dropout = qa_dropout self.seq_classif_dropout = seq_classif_dropout @property def hidden_size(self): return self.dim @property def num_attention_heads(self): return self.n_heads @property def num_hidden_layers(self): return self.n_layers