fTh{SrSSKJr SSKJr SSKJr SSKJr SSK J r \ R"\ 5r "SS \5r"S S \5rS S /rg ) zRemBERT model configuration) OrderedDict)Mapping)PretrainedConfig) OnnxConfig)loggingcZ^\rSrSrSrSrSU4SjjrSrU=r$) RemBertConfiga 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 probability 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 ```rembertc >[TU]"SUUUS.UD6 XlXPlX`lXlX lX0lX@lXpl Xl Xl Xl Xl XlXlXlUUlSUlg)N) pad_token_id bos_token_id eos_token_idF)super__init__ vocab_sizeinput_embedding_sizeoutput_embedding_sizemax_position_embeddings hidden_sizenum_hidden_layersnum_attention_headsintermediate_size hidden_acthidden_dropout_probattention_probs_dropout_probclassifier_dropout_probinitializer_rangetype_vocab_sizelayer_norm_eps use_cachetie_word_embeddings)selfrrrrrrrrrrrrr!r r"r#rrrkwargs __class__s i/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/rembert/configuration_rembert.pyrRemBertConfig.__init__bs. sl\hslrs$$8!%:"'>$&!2#6 !2$#6 ,H)'>$!2.,"#( )rrrrrr rrr"rrrrr$r!r#r)ii iigelur/g?ig{Gz?g-q=Tri8i9) __name__ __module__ __qualname____firstlineno____doc__ model_typer__static_attributes__ __classcell__)r's@r(r r sVA FJ "%( # #)))))r*r cX\rSrSr\S\\\\\444Sj5r\S\ 4Sj5r Sr g)RemBertOnnxConfigreturncbURS:XaSSSS.nOSSS.n[SU4SU4S U4/5$) Nzmultiple-choicebatchchoicesequence)rr0)rrA input_idsattention_masktoken_type_ids)taskr)r% dynamic_axiss r(inputsRemBertOnnxConfig.inputssO 99) )&8 CL&:6Ll+!<0!<0   r*cg)Ng-C6?r)r%s r(atol_for_validation%RemBertOnnxConfig.atol_for_validationsr*rN) r1r2r3r4propertyrstrintrGfloatrJr7rr*r(r:r:sI   WS#X%6 67    Ur*r:N)r5 collectionsrtypingrconfiguration_utilsronnxrutilsr get_loggerr1loggerr r:__all__rr*r(rXsT"#3   H %o)$o)d ( / 0r*