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ff_activation (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the If string, `"gelu"`, `"relu"`, `"silu"` and
`"gelu_new"` are supported.
attn_type (`str`, *optional*, defaults to `"bi"`):
The attention type used by the model. Set `"bi"` for XLNet, `"uni"` for Transformer-XL.
mem_len (`int` or `None`, *optional*):
The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous
forward pass won't be re-computed. See the
[quickstart](https://huggingface.co/transformers/quickstart.html#using-the-past) for more information.
reuse_len (`int`, *optional*):
The number of tokens in the current batch to be cached and reused in the future.
use_mems_eval (`bool`, *optional*, defaults to `True`):
Whether or not the model should make use of the recurrent memory mechanism in evaluation mode.
use_mems_train (`bool`, *optional*, defaults to `False`):
Whether or not the model should make use of the recurrent memory mechanism in train mode.
For pretraining, it is recommended to set `use_mems_train` to `True`. For fine-tuning, it is recommended to
set `use_mems_train` to `False` as discussed
[here](https://github.com/zihangdai/xlnet/issues/41#issuecomment-505102587). If `use_mems_train` is set to
`True`, one has to make sure that the train batches are correctly pre-processed, *e.g.* `batch_1 = [[This
line is], [This is the]]` and `batch_2 = [[ the first line], [ second line]]` and that all batches are of
equal size.
bi_data (`bool`, *optional*, defaults to `False`):
Whether or not to use bidirectional input pipeline. Usually set to `True` during pretraining and `False`
during finetuning.
clamp_len (`int`, *optional*, defaults to -1):
Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping.
same_length (`bool`, *optional*, defaults to `False`):
Whether or not to use the same attention length for each token.
summary_type (`str`, *optional*, defaults to "last"):
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 `"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_last_dropout (`float`, *optional*, defaults to 0.1):
Used in the sequence classification and multiple choice models.
The dropout ratio to be used after the projection and activation.
start_n_top (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
end_n_top (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
Examples:
```python
>>> from transformers import XLNetConfig, XLNetModel
>>> # Initializing a XLNet configuration
>>> configuration = XLNetConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = XLNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
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