class_docstr: This is the configuration class to store the configuration of a [`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 RemBERT
  [google/rembert](https://huggingface.co/google/rembert) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  
  Args:
      vocab_size (`int`, *optional*, defaults to 250300):
          Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`RemBertModel`] or [`TFRemBertModel`]. Vocabulary size of the model.
          Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of
          [`RemBertModel`].
      hidden_size (`int`, *optional*, defaults to 1152):
          Dimensionality of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 32):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 18):
          Number of attention heads for each attention layer in the Transformer encoder.
      input_embedding_size (`int`, *optional*, defaults to 256):
          Dimensionality of the input embeddings.
      output_embedding_size (`int`, *optional*, defaults to 1664):
          Dimensionality of the output embeddings.
      intermediate_size (`int`, *optional*, defaults to 4608):
          Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"` and `"gelu_new"` are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0):
          The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0):
          The dropout ratio for the attention probabilities.
      classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the classifier layer when fine-tuning.
      max_position_embeddings (`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 (`int`, *optional*, defaults to 2):
          The vocabulary size of the `token_type_ids` passed when calling [`RemBertModel`] or [`TFRemBertModel`].
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      is_decoder (`bool`, *optional*, defaults to `False`):
          Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
      use_cache (`bool`, *optional*, defaults to `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:
  
  ```python
  >>> 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
  ```
code: 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,
      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
  
file_path: transformers/src/transformers/models/rembert/configuration_rembert.py
imports: from collections import OrderedDict
  from typing import Mapping
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new RemBertConfig object from inherited base classes: PretrainedConfig with example: obj = RemBertConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, input_embedding_size, output_embedding_size, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, classifier_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, use_cache, pad_token_id, bos_token_id, eos_token_id)