Source code for transformers.configuration_xlnet

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""" XLNet configuration """

import warnings

from .configuration_utils import PretrainedConfig
from .utils import logging

logger = logging.get_logger(__name__)

    "xlnet-base-cased": "",
    "xlnet-large-cased": "",

[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 <>`__ 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): 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 <>`__ for more information. reuse_len (:obj:`int` or :obj:`None`, optional): 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): 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. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last pre-computed hidden states. .. note:: This flag behaves differently from with other models: it just controls the inference behavior, during training the model always uses ``use_cache=True``. 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 """ 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 if "d_head" in kwargs: assert ( kwargs["d_head"] == d_model // n_head ), f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" 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 if mem_len is None or mem_len == 0: warnings.warn( "This config doesn't use attention memories, a core feature of XLNet." " Consider setting `men_len` to a non-zero value, for example " "`xlnet = XLNetLMHeadModel.from_pretrained('xlnet-base-cased'', mem_len=1024)`," " for accurate training performance as well as an order of magnitude faster inference." " Starting from version 3.5.0, the default parameter will be 1024, following" " the implementation in", FutureWarning, ) @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