Source code for transformers.models.mobilebert.configuration_mobilebert

# coding=utf-8
# Copyright 2020 The HuggingFace Team. 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" MobileBERT model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

    "mobilebert-uncased": ""

[docs]class MobileBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.MobileBertModel` or a :class:`~transformers.TFMobileBertModel`. It is used to instantiate a MobileBERT model according to the specified arguments, defining the model 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 MobileBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.MobileBertModel` or :class:`~transformers.TFMobileBertModel`. hidden_size (:obj:`int`, `optional`, defaults to 512): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (:obj:`int`, `optional`, defaults to 24): 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 512): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.0): The dropout probability 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 :obj:`token_type_ids` passed when calling :class:`~transformers.MobileBertModel` or :class:`~transformers.TFMobileBertModel`. 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. pad_token_id (:obj:`int`, `optional`, defaults to 0): The ID of the token in the word embedding to use as padding. embedding_size (:obj:`int`, `optional`, defaults to 128): The dimension of the word embedding vectors. trigram_input (:obj:`bool`, `optional`, defaults to :obj:`True`): Use a convolution of trigram as input. use_bottleneck (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to use bottleneck in BERT. intra_bottleneck_size (:obj:`int`, `optional`, defaults to 128): Size of bottleneck layer output. use_bottleneck_attention (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to use attention inputs from the bottleneck transformation. key_query_shared_bottleneck (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to use the same linear transformation for query&key in the bottleneck. num_feedforward_networks (:obj:`int`, `optional`, defaults to 4): Number of FFNs in a block. normalization_type (:obj:`str`, `optional`, defaults to :obj:`"no_norm"`): The normalization type in MobileBERT. Examples:: >>> from transformers import MobileBertModel, MobileBertConfig >>> # Initializing a MobileBERT configuration >>> configuration = MobileBertConfig() >>> # Initializing a model from the configuration above >>> model = MobileBertModel(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 = MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP model_type = "mobilebert" def __init__( self, vocab_size=30522, hidden_size=512, num_hidden_layers=24, num_attention_heads=4, intermediate_size=512, hidden_act="relu", hidden_dropout_prob=0.0, 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, embedding_size=128, trigram_input=True, use_bottleneck=True, intra_bottleneck_size=128, use_bottleneck_attention=False, key_query_shared_bottleneck=True, num_feedforward_networks=4, normalization_type="no_norm", classifier_activation=True, **kwargs ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size 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.embedding_size = embedding_size self.trigram_input = trigram_input self.use_bottleneck = use_bottleneck self.intra_bottleneck_size = intra_bottleneck_size self.use_bottleneck_attention = use_bottleneck_attention self.key_query_shared_bottleneck = key_query_shared_bottleneck self.num_feedforward_networks = num_feedforward_networks self.normalization_type = normalization_type self.classifier_activation = classifier_activation if self.use_bottleneck: self.true_hidden_size = intra_bottleneck_size else: self.true_hidden_size = hidden_size