Source code for transformers.configuration_electra

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ELECTRA model configuration """


import logging

from .configuration_utils import PretrainedConfig


logger = logging.getLogger(__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. 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 Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained checkpoints. """ pretrained_config_archive_map = ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP 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, 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