class_docstr: This is the configuration class to store the configuration of a [`IBertModel`]. It is used to instantiate a I-BERT
  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 IBERT
  [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) 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 30522):
          Vocabulary size of the I-BERT model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`IBertModel`]
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
      hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      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 [`IBertModel`]
      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.
      position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
          Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
          positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
          [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
          For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
          with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
      quant_mode (`bool`, *optional*, defaults to `False`):
          Whether to quantize the model or not.
      force_dequant (`str`, *optional*, defaults to `"none"`):
          Force dequantize specific nonlinear layer. Dequatized layers are then executed with full precision.
          `"none"`, `"gelu"`, `"softmax"`, `"layernorm"` and `"nonlinear"` are supported. As deafult, it is set as
          `"none"`, which does not dequantize any layers. Please specify `"gelu"`, `"softmax"`, or `"layernorm"` to
          dequantize GELU, Softmax, or LayerNorm, respectively. `"nonlinear"` will dequantize all nonlinear layers,
          i.e., GELU, Softmax, and LayerNorm.
class_docstr_tok: ["This","is","the","configuration","class","to","store","the","configuration","of","a","[","`","IBertModel","`","]",".","It","is","used","to","instantiate","a","I-BERT","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","IBERT","[","kssteven\/ibert-roberta-base","]","(","https",":","\/\/huggingface.co\/kssteven\/ibert-roberta-base",")","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","30522",")",":","Vocabulary","size","of","the","I-BERT","model",".","Defines","the","number","of","different","tokens","that","can","be","represented","by","the","`","inputs_ids","`","passed","when","calling","[","`","IBertModel","`","]","hidden_size","(","`","int","`",",","*","optional","*",",","defaults","to","768",")",":","Dimensionality","of","the","encoder","layers","and","the","pooler","layer",".","num_hidden_layers","(","`","int","`",",","*","optional","*",",","defaults","to","12",")",":","Number","of","hidden","layers","in","the","Transformer","encoder",".","num_attention_heads","(","`","int","`",",","*","optional","*",",","defaults","to","12",")",":","Number","of","attention","heads","for","each","attention","layer","in","the","Transformer","encoder",".","intermediate_size","(","`","int","`",",","*","optional","*",",","defaults","to","3072",")",":","Dimensionality","of","the","``","intermediate","''","(","often","named","feed-forward",")","layer","in","the","Transformer","encoder",".","hidden_act","(","`","str","`","or","`","Callable","`",",","*","optional","*",",","defaults","to","`","``","gelu","''","`",")",":","The","non-linear","activation","function","(","function","or","string",")","in","the","encoder","and","pooler",".","If","string",",","`","``","gelu","''","`",",","`","``","relu","''","`",",","`","``","silu","''","`","and","`","``","gelu_new","''","`","are","supported",".","hidden_dropout_prob","(","`","float","`",",","*","optional","*",",","defaults","to","0.1",")",":","The","dropout","probability","for","all","fully","connected","layers","in","the","embeddings",",","encoder",",","and","pooler",".","attention_probs_dropout_prob","(","`","float","`",",","*","optional","*",",","defaults","to","0.1",")",":","The","dropout","ratio","for","the","attention","probabilities",".","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","[","`","IBertModel","`","]","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",".","position_embedding_type","(","`","str","`",",","*","optional","*",",","defaults","to","`","``","absolute","''","`",")",":","Type","of","position","embedding",".","Choose","one","of","`","``","absolute","''","`",",","`","``","relative_key","''","`",",","`","``","relative_key_query","''","`",".","For","positional","embeddings","use","`","``","absolute","''","`",".","For","more","information","on","`","``","relative_key","''","`",",","please","refer","to","[","Self-Attention","with","Relative","Position","Representations","(","Shaw","et","al.",")","]","(","https",":","\/\/arxiv.org\/abs\/1803.02155",")",".","For","more","information","on","`","``","relative_key_query","''","`",",","please","refer","to","*","Method","4","*","in","[","Improve","Transformer","Models","with","Better","Relative","Position","Embeddings","(","Huang","et","al.",")","]","(","https",":","\/\/arxiv.org\/abs\/2009.13658",")",".","quant_mode","(","`","bool","`",",","*","optional","*",",","defaults","to","`","False","`",")",":","Whether","to","quantize","the","model","or","not",".","force_dequant","(","`","str","`",",","*","optional","*",",","defaults","to","`","``","none","''","`",")",":","Force","dequantize","specific","nonlinear","layer",".","Dequatized","layers","are","then","executed","with","full","precision",".","`","``","none","''","`",",","`","``","gelu","''","`",",","`","``","softmax","''","`",",","`","``","layernorm","''","`","and","`","``","nonlinear","''","`","are","supported",".","As","deafult",",","it","is","set","as","`","``","none","''","`",",","which","does","not","dequantize","any","layers",".","Please","specify","`","``","gelu","''","`",",","`","``","softmax","''","`",",","or","`","``","layernorm","''","`","to","dequantize","GELU",",","Softmax",",","or","LayerNorm",",","respectively",".","`","``","nonlinear","''","`","will","dequantize","all","nonlinear","layers",",","i.e.",",","GELU",",","Softmax",",","and","LayerNorm","."]
code: def __init__(
      self,
      vocab_size=30522,
      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=2,
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      pad_token_id=1,
      bos_token_id=0,
      eos_token_id=2,
      position_embedding_type="absolute",
      quant_mode=False,
      force_dequant="none",
      **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.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.hidden_act = hidden_act
      self.intermediate_size = intermediate_size
      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.position_embedding_type = position_embedding_type
      self.quant_mode = quant_mode
      self.force_dequant = force_dequant
  
file_path: transformers/src/transformers/models/ibert/configuration_ibert.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 IBertConfig object from inherited base classes: PretrainedConfig with example: obj = IBertConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, bos_token_id, eos_token_id, position_embedding_type, quant_mode, force_dequant)