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S )zELECTRA model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringz"google/electra-small-discriminator)Ú
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eed
<   dZeed<   dZeed<   dZeed<   dZeez  ed<   dZeez  ed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed <   dZeez  ed!<   d"Zed#z  ed$<   dZeed%<   d#Zeez  d#z  ed&<   d'Zeed(<   d'Zeed)<   d#Z ed#z  ed*<   d#Z!ee"e         z  d#z  ed+<   dZ#eed,<   d#S )-ÚElectraConfiga  
    summary_type (`str`, *optional*, defaults to `"first"`):
        Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
        Has to be one of the following options:
            - `"last"`: Take the last token hidden state (like XLNet).
            - `"first"`: Take the first token hidden state (like BERT).
            - `"mean"`: Take the mean of all tokens hidden states.
            - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
            - `"attn"`: Not implemented now, use multi-head attention.
    summary_use_proj (`bool`, *optional*, defaults to `True`):
        Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
        Whether or not to add a projection after the vector extraction.
    summary_activation (`str`, *optional*):
        Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
        Pass `"gelu"` for a gelu activation to the output, any other value will result in no activation.
    summary_last_dropout (`float`, *optional*, defaults to 0.0):
        Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
        The dropout ratio to be used after the projection and activation.

    Examples:

    ```python
    >>> from transformers import ElectraConfig, ElectraModel

    >>> # Initializing a ELECTRA electra-base-uncased style configuration
    >>> configuration = ElectraConfig()

    >>> # Initializing a model (with random weights) from the electra-base-uncased style configuration
    >>> model = ElectraModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úelectrai:w  Ú
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__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   r   r   r   r   r   Ústrr   Úfloatr   r   r   r   r   r   r    Úboolr!   r"   r#   r$   r%   r&   r'   r(   r)   Úlistr*   © ó    úo/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/electra/configuration_electra.pyr	   r	      së  € € € € € € ð ð  ðD €Jà€J�ÐÐÑØ€N�CÐÐÑØ€K�ÐÐÑØÐ�sÐÐÑØ Ð˜Ð Ð Ñ Ø!Ð�sÐ!Ð!Ñ!Ø€J�ÐÐÑØ'*Ð˜ ™Ð*Ð*Ñ*Ø03Ð  %¨#¡+Ð3Ð3Ñ3Ø#&Ð˜SÐ&Ð&Ñ&Ø€O�SÐÐÑØ#Ð�uÐ#Ð#Ñ#Ø!€N�EÐ!Ð!Ñ!Ø€L�#ÐÐÑØ!Ð�dÐ!Ð!Ñ!Ø$Ð˜Ð$Ð$Ñ$Ø(+Ð˜% #™+Ð+Ð+Ñ+Ø €L�#˜‘*Ð Ð Ñ Ø€IˆtÐÐÑØ-1Ð˜ ™ dÑ*Ð1Ð1Ñ1Ø€J�ÐÐÑØ %Ð˜Ð%Ð%Ñ%Ø#€L�#˜‘*Ð#Ð#Ñ#Ø+/€L�#˜˜Sœ	‘/ DÑ(Ð/Ð/Ñ/Ø $Ð˜Ð$Ð$Ñ$Ð$Ð$r7   r	   N)	r.   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r6   r7   r8   ú<module>r=      s£   ðð "Ð !à .Ð .Ð .Ð .Ð .Ð .à 3Ð 3Ð 3Ð 3Ð 3Ð 3Ø #Ð #Ð #Ð #Ð #Ð #ð €Ð?Ð@Ñ@Ô@Øð=%ð =%ð =%ð =%ð =%Ð$ñ =%ô =%ñ „ñ AÔ@ð=%ð@ Ð
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