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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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""" ALBERT model configuration """
from .configuration_utils import PretrainedConfig
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"albert-base-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v1-config.json",
"albert-large-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v1-config.json",
"albert-xlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v1-config.json",
"albert-xxlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v1-config.json",
"albert-base-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-config.json",
"albert-large-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-config.json",
"albert-xlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-config.json",
"albert-xxlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-config.json",
}
[docs]class AlbertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an :class:`~transformers.AlbertModel`.
It is used to instantiate an ALBERT 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 ALBERT `xxlarge <https://huggingface.co/albert-xxlarge-v2>`__ 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 30000):
Vocabulary size of the ALBERT model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.AlbertModel`.
embedding_size (:obj:`int`, optional, defaults to 128):
Dimensionality of vocabulary embeddings.
hidden_size (:obj:`int`, optional, defaults to 4096):
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_hidden_groups (:obj:`int`, optional, defaults to 1):
Number of groups for the hidden layers, parameters in the same group are shared.
num_attention_heads (:obj:`int`, optional, defaults to 64):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, optional, defaults to 16384):
The dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
inner_group_num (:obj:`int`, optional, defaults to 1):
The number of inner repetition of attention and ffn.
hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu_new"):
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):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0):
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 (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.AlbertModel`.
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.
classifier_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout ratio for attached classifiers.
Example::
from transformers import AlbertConfig, AlbertModel
# Initializing an ALBERT-xxlarge style configuration
albert_xxlarge_configuration = AlbertConfig()
# Initializing an ALBERT-base style configuration
albert_base_configuration = AlbertConfig(
hidden_size=768,
num_attention_heads=12,
intermediate_size=3072,
)
# Initializing a model from the ALBERT-base style configuration
model = AlbertModel(albert_xxlarge_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 = ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "albert"
def __init__(
self,
vocab_size=30000,
embedding_size=128,
hidden_size=4096,
num_hidden_layers=12,
num_hidden_groups=1,
num_attention_heads=64,
intermediate_size=16384,
inner_group_num=1,
hidden_act="gelu_new",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
classifier_dropout_prob=0.1,
pad_token_id=0,
bos_token_id=2,
eos_token_id=3,
**kwargs
):
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.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_hidden_groups = num_hidden_groups
self.num_attention_heads = num_attention_heads
self.inner_group_num = inner_group_num
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.classifier_dropout_prob = classifier_dropout_prob