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\\S	'   Sr\\S
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XmodConfigé   aì  
pre_norm (`bool`, *optional*, defaults to `False`):
    Whether to apply layer normalization before each block.
adapter_reduction_factor (`int` or `float`, *optional*, defaults to 2):
    The factor by which the dimensionality of the adapter is reduced relative to `hidden_size`.
adapter_layer_norm (`bool`, *optional*, defaults to `False`):
    Whether to apply a new layer normalization before the adapter modules (shared across all adapters).
adapter_reuse_layer_norm (`bool`, *optional*, defaults to `True`):
    Whether to reuse the second layer normalization and apply it before the adapter modules as well.
ln_before_adapter (`bool`, *optional*, defaults to `True`):
    Whether to apply the layer normalization before the residual connection around the adapter module.
languages (`Iterable[str]`, *optional*, defaults to `["en_XX"]`):
    An iterable of language codes for which adapter modules should be initialized.
default_language (`str`, *optional*):
    Language code of a default language. It will be assumed that the input is in this language if no language
    codes are explicitly passed to the forward method.

Examples:

```python
>>> from transformers import XmodConfig, XmodModel

>>> # Initializing an X-MOD facebook/xmod-base style configuration
>>> configuration = XmodConfig()

>>> # Initializing a model (with random weights) from the facebook/xmod-base style configuration
>>> model = XmodModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úxmodi:w  Ú
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