sgLdZddlmZddlmZddlmZddlmZddl m Z e je Z Gdd eZGd d eZy ) zBERT model configuration) OrderedDict)Mapping)PretrainedConfig) OnnxConfig)loggingcHeZdZdZdZ dfd ZxZS) BertConfiga This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to instantiate a 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 BERT [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) 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 BERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`]. 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 [`BertModel`] or [`TFBertModel`]. 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). 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`. classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. Examples: ```python >>> from transformers import BertConfig, BertModel >>> # Initializing a BERT google-bert/bert-base-uncased style configuration >>> configuration = BertConfig() >>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration >>> model = BertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```bertc t|dd| i|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ ||_||_||_y)N pad_token_id)super__init__ vocab_size hidden_sizenum_hidden_layersnum_attention_heads hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epsposition_embedding_type use_cacheclassifier_dropout)selfrrrrrrrrrrrrr rrrkwargs __class__s ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/bert/configuration_bert.pyrzBertConfig.__init__cs( =l=f=$&!2#6 $!2#6 ,H)'>$.!2,'>$""4)i:wi r%i gelu皙?r'ig{Gz?g-q=rabsoluteTN)__name__ __module__ __qualname____doc__ model_typer __classcell__)r"s@r#r r sMA FJ%( # *#$5$5r$r c6eZdZedeeeeefffdZy)BertOnnxConfigreturnc`|jdk(rdddd}nddd}td|fd|fd |fgS) Nzmultiple-choicebatchchoicesequence)rr()rr7 input_idsattention_masktoken_type_ids)taskr)r dynamic_axiss r#inputszBertOnnxConfig.inputssO 99) )&8 CL&:6Ll+!<0!<0   r$N)r*r+r,propertyrstrintr=rr$r#r1r1s.   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