fThSrSSKJr SSKJr SSKJr SSKJr SSK J r \ R"\ 5r "SS \5r"S S \5rS S /rg ) zSqueezeBERT model configuration) OrderedDict)Mapping)PretrainedConfig) OnnxConfig)loggingc\^\rSrSrSrSrSU4SjjrSrU=r$)SqueezeBertConfiga This is the configuration class to store the configuration of a [`SqueezeBertModel`]. It is used to instantiate a SqueezeBERT 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 SqueezeBERT [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-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 SqueezeBERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SqueezeBertModel`]. 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): pad_token_id (`int`, *optional*, defaults to 0): The ID of the token in the word embedding to use as padding. embedding_size (`int`, *optional*, defaults to 768): The dimension of the word embedding vectors. q_groups (`int`, *optional*, defaults to 4): The number of groups in Q layer. k_groups (`int`, *optional*, defaults to 4): The number of groups in K layer. v_groups (`int`, *optional*, defaults to 4): The number of groups in V layer. post_attention_groups (`int`, *optional*, defaults to 1): The number of groups in the first feed forward network layer. intermediate_groups (`int`, *optional*, defaults to 4): The number of groups in the second feed forward network layer. output_groups (`int`, *optional*, defaults to 4): The number of groups in the third feed forward network layer. Examples: ```python >>> from transformers import SqueezeBertConfig, SqueezeBertModel >>> # Initializing a SqueezeBERT configuration >>> configuration = SqueezeBertConfig() >>> # Initializing a model (with random weights) from the configuration above >>> model = SqueezeBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` squeezebertc >[TU]"SSU 0UD6 XlX lX0lX@lX`lXPlXplXl Xl Xl Xl Xl XlXlUUlUUlUUlUUlUUlg)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_epsembedding_sizeq_groupsk_groupsv_groupspost_attention_groupsintermediate_groups output_groups)selfrrrrrrrrrrrrrrrr r!r"r#r$kwargs __class__s q/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/squeezebert/configuration_squeezebert.pyrSqueezeBertConfig.__init__gs0 =l=f=$&!2#6 $!2#6 ,H)'>$.!2,,     %:"#6 *)rrrrrrr#rr rrrrr$r"rrr!r)i:w r,i gelu皙?r.ig{Gz?g-q=rr+r0r0r0r0) __name__ __module__ __qualname____firstlineno____doc__ model_typer__static_attributes__ __classcell__)r's@r(r r sYFPJ%( #+,+,+r*r c@\rSrSr\S\\\\\444Sj5rSr g)SqueezeBertOnnxConfigreturncbURS:XaSSSS.nOSSS.n[SU4SU4S U4/5$) Nzmultiple-choicebatchchoicesequence)rr1r/)rr1 input_idsattention_masktoken_type_ids)taskr)r% dynamic_axiss r(inputsSqueezeBertOnnxConfig.inputssO 99) )&8 CL&:6Ll+!<0!<0   r*rN) r2r3r4r5propertyrstrintrGr8rr*r(r;r;s.   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