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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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""" ELECTRA model configuration """
from .configuration_utils import PretrainedConfig
from .utils import logging
logger = logging.get_logger(__name__)
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/electra-small-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-generator/config.json",
"google/electra-base-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-generator/config.json",
"google/electra-large-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-generator/config.json",
"google/electra-small-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-discriminator/config.json",
"google/electra-base-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-discriminator/config.json",
"google/electra-large-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-discriminator/config.json",
}
[docs]class ElectraConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel`.
It is used to instantiate an ELECTRA 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 ELECTRA `google/electra-small-discriminator <https://huggingface.co/google/electra-small-discriminator>`__
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 30522):
Vocabulary size of the ELECTRA model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.ElectraModel`.
embedding_size (:obj:`int`, optional, defaults to 128):
Dimensionality of the encoder layers and the pooler layer.
hidden_size (:obj:`int`, optional, defaults to 256):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, optional, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, optional, defaults to 4):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, optional, defaults to 1024):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the encoder and pooler.
If string, "gelu", "relu", "swish" and "gelu_new" are supported.
hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, optional, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, optional, defaults to 2):
The vocabulary size of the `token_type_ids` passed into :class:`~transformers.ElectraModel`.
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.
summary_type (:obj:`string`, optional, defaults to "first"):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.ElectraForMultipleChoice`.
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.ElectraForMultipleChoice`.
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.ElectraForMultipleChoice`.
'gelu' => add a gelu activation to the output, Other => no activation.
summary_last_dropout (:obj:`float`, optional, defaults to 0.0):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.ElectraForMultipleChoice`.
Add a dropout after the projection and activation
Example::
>>> from transformers import ElectraModel, ElectraConfig
>>> # Initializing a ELECTRA electra-base-uncased style configuration
>>> configuration = ElectraConfig()
>>> # Initializing a model from the electra-base-uncased style configuration
>>> model = ElectraModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "electra"
def __init__(
self,
vocab_size=30522,
embedding_size=128,
hidden_size=256,
num_hidden_layers=12,
num_attention_heads=4,
intermediate_size=1024,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
summary_type="first",
summary_use_proj=True,
summary_activation="gelu",
summary_last_dropout=0.1,
pad_token_id=0,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_last_dropout = summary_last_dropout