9i*SrSSKJr "SS\5rg)zPLUG model configuration )PretrainedConfigcL^\rSrSrSrSrSU4SjjrSrU=r$) PlugConfiga Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.BertModel` or :class:`~transformers.TFBertModel`. hidden_size (:obj:`int`, `optional`, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (:obj:`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 (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or :class:`~transformers.TFBertModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layernorm_epsilon (:obj:`float`, `optional`, defaults to 1e-12): The epsilon used by the layer normalization layers. dec_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer decoder. attn_separate (:obj:`bool`, `optional`, defaults to false): Whether or not to separate the q, k, v of attention. Examples:: >>> import PlugModel, PlugConfig >>> configuration = PlugConfig() >>> # Initializing a model from the configuration >>> model = PlugModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config plugc  >[TU]"S0U D6 XlX lX0lX@lXPlX`lXplXl Xl Xl Xl Xl g)N)super__init__encoder encoder_pthmax_pos share_emb dec_layersdec_hidden_size dec_heads dec_ff_size dec_dropout use_bert_emblabel_smoothing block_trigram)selfr r rrrrrrrrrrkwargs __class__s l/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/models/nlp/fid_plug/configuration.pyr PlugConfig.__init__JsX "6" & "$."&&(.*) rrrrrrr r rrrr) robertaz roberta-baseiF ii g?Tg?F) __name__ __module__ __qualname____firstlineno____doc__ model_typer __static_attributes__ __classcell__)rs@rrrs@2fJ#+ !$! "!$$++rrN)r% transformers.configuration_utilsrrr rrr*s !=Q+!Q+r