# coding=utf-8
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""" MobileBERT model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}
[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