Source code for transformers.models.rembert.configuration_rembert

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""" RemBERT model configuration """

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

logger = logging.get_logger(__name__)

    "rembert": "",
    # See all RemBERT models at

[docs]class RemBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.RemBertModel`. It is used to instantiate an RemBERT 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 remert-large 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 250300): Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.RemBertModel` or :class:`~transformers.TFRemBertModel`. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.RemBertModel`. hidden_size (:obj:`int`, `optional`, defaults to 1152): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (:obj:`int`, `optional`, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 18): Number of attention heads for each attention layer in the Transformer encoder. input_embedding_size (:obj:`int`, `optional`, defaults to 256): Dimensionality of the input embeddings. output_embedding_size (:obj:`int`, `optional`, defaults to 1664): Dimensionality of the output embeddings. intermediate_size (:obj:`int`, `optional`, defaults to 4608): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`function`, `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:`"selu"` and :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0): The dropout probabilitiy 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. classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the classifier layer when fine-tuning. 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.RemBertModel` or :class:`~transformers.TFRemBertModel`. 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. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if ``config.is_decoder=True``. Example:: >>> from transformers import RemBertModel, RemBertConfig >>> # Initializing a RemBERT rembert style configuration >>> configuration = RemBertConfig() >>> # Initializing a model from the rembert style configuration >>> model = RemBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "rembert" def __init__( self, vocab_size=250300, hidden_size=1152, num_hidden_layers=32, num_attention_heads=18, input_embedding_size=256, output_embedding_size=1664, intermediate_size=4608, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, classifier_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, use_cache=True, is_encoder_decoder=False, pad_token_id=0, bos_token_id=312, eos_token_id=313, **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.input_embedding_size = input_embedding_size self.output_embedding_size = output_embedding_size self.max_position_embeddings = max_position_embeddings 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.classifier_dropout_prob = classifier_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache self.tie_word_embeddings = False