U 4A·fÑã@s ddlmZGdd„deƒZdS)é)ÚPretrainedConfigcs&eZdZdZdZd‡fdd„ Z‡ZS)Ú NezhaConfiga­ This is the configuration class to store the configuration of an [`NezhaModel`]. It is used to instantiate an Nezha 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 Nezha [sijunhe/nezha-cn-base](https://huggingface.co/sijunhe/nezha-cn-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 21128): Vocabulary size of the NEZHA model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`NezhaModel`]. 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): The 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. 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 (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, optional, defaults to 2): The vocabulary size of the *token_type_ids* passed into [`NezhaModel`]. 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. classifier_dropout (`float`, optional, defaults to 0.1): The dropout ratio for attached classifiers. 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. Example: ```python >>> from transformers import NezhaConfig, NezhaModel >>> # Initializing an Nezha configuration >>> configuration = NezhaConfig() >>> # Initializing a model (with random weights) from the Nezha-base style configuration model >>> model = NezhaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZnezhaéˆRéé é Úgeluçš™™™™™¹?éé@éç{®Gáz”?çê-™—q=ééTc sxtƒjf|||dœ|—Ž||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_dS)N)Ú pad_token_idÚ bos_token_idÚ eos_token_id)ÚsuperÚ__init__Ú vocab_sizeÚ hidden_sizeÚnum_hidden_layersÚnum_attention_headsÚ hidden_actÚintermediate_sizeÚhidden_dropout_probÚattention_probs_dropout_probÚmax_position_embeddingsÚmax_relative_positionÚtype_vocab_sizeÚinitializer_rangeÚlayer_norm_epsÚclassifier_dropoutÚ use_cache)Úselfrrrrrrrrrrr r!r"r#rrrr$Úkwargs©Ú __class__©ú\/tmp/pip-unpacked-wheel-zw5xktn0/transformers/models/deprecated/nezha/configuration_nezha.pyr@s zNezhaConfig.__init__)rrrrrrr r r r r r rr rr rT)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__r)r)r'r*rs*9írN)Úrrr)r)r)r*Ús