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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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