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""" XLNet configuration """
import logging
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"xlnet-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
"xlnet-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
}
[docs]class XLNetConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a :class:`~transformers.XLNetModel`.
It is used to instantiate an XLNet model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the `xlnet-large-cased <https://huggingface.co/xlnet-large-cased>`__ architecture.
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 32000):
Vocabulary size of the XLNet model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLNetModel`.
d_model (:obj:`int`, optional, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
n_layer (:obj:`int`, optional, defaults to 24):
Number of hidden layers in the Transformer encoder.
n_head (:obj:`int`, optional, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
d_inner (:obj:`int`, optional, defaults to 4096):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
ff_activation (:obj:`string`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
untie_r (:obj:`boolean`, optional, defaults to :obj:`True`):
Untie relative position biases
attn_type (:obj:`string`, optional, defaults to "bi"):
The attention type used by the model. Set 'bi' for XLNet, 'uni' for Transformer-XL.
initializer_range (:obj:`float`, optional, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers.
dropout (:obj:`float`, optional, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
mem_len (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`):
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 (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`):
The number of tokens in the current batch to be cached and reused in the future.
bi_data (:obj:`boolean`, optional, defaults to :obj:`False`):
Whether to use bidirectional input pipeline. Usually set to `True` during
pretraining and `False` during finetuning.
clamp_len (:obj:`int`, optional, defaults to -1):
Clamp all relative distances larger than clamp_len.
Setting this attribute to -1 means no clamping.
same_length (:obj:`boolean`, optional, defaults to :obj:`False`):
Whether to use the same attention length for each token.
summary_type (:obj:`string`, optional, defaults to "last"):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
Is 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 (GPT/GPT-2)
- 'attn' => Not implemented now, use multi-head attention
summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
Add a projection after the vector extraction
summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
'tanh' => add a tanh activation to the output, Other => no activation.
summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
summary_last_dropout (:obj:`float`, optional, defaults to 0.1):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
Add a dropout after the projection and activation
start_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet.
end_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet.
Example::
from transformers import XLNetConfig, XLNetModel
# Initializing a XLNet configuration
configuration = XLNetConfig()
# Initializing a model from the configuration
model = XLNetModel(configuration)
# Accessing the model configuration
configuration = model.config
Attributes:
pretrained_config_archive_map (Dict[str, str]):
A dictionary containing all the available pre-trained checkpoints.
"""
pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "xlnet"
def __init__(
self,
vocab_size=32000,
d_model=1024,
n_layer=24,
n_head=16,
d_inner=4096,
ff_activation="gelu",
untie_r=True,
attn_type="bi",
initializer_range=0.02,
layer_norm_eps=1e-12,
dropout=0.1,
mem_len=None,
reuse_len=None,
bi_data=False,
clamp_len=-1,
same_length=False,
summary_type="last",
summary_use_proj=True,
summary_activation="tanh",
summary_last_dropout=0.1,
start_n_top=5,
end_n_top=5,
pad_token_id=5,
bos_token_id=1,
eos_token_id=2,
**kwargs
):
"""Constructs XLNetConfig.
"""
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.d_model = d_model
self.n_layer = n_layer
self.n_head = n_head
assert d_model % n_head == 0
self.d_head = d_model // n_head
self.ff_activation = ff_activation
self.d_inner = d_inner
self.untie_r = untie_r
self.attn_type = attn_type
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.dropout = dropout
self.mem_len = mem_len
self.reuse_len = reuse_len
self.bi_data = bi_data
self.clamp_len = clamp_len
self.same_length = same_length
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_last_dropout = summary_last_dropout
self.start_n_top = start_n_top
self.end_n_top = end_n_top
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.eos_token_id = eos_token_id
@property
def max_position_embeddings(self):
return -1
@property
def n_token(self): # Backward compatibility
return self.vocab_size
@n_token.setter
def n_token(self, value): # Backward compatibility
self.vocab_size = value
@property
def hidden_size(self):
return self.d_model
@property
def num_attention_heads(self):
return self.n_head
@property
def num_hidden_layers(self):
return self.n_layer