# coding=utf-8
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""" RemBERT model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
# See all RemBERT models at https://huggingface.co/models?filter=rembert
}
[docs]class RemBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.RemBertModel`. It is used to
instantiate an RemBERT 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 remert-large 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 250300):
Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.RemBertModel` or
:class:`~transformers.TFRemBertModel`. Vocabulary size of the model. Defines the different tokens that can
be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.RemBertModel`.
hidden_size (:obj:`int`, `optional`, defaults to 1152):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, `optional`, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 18):
Number of attention heads for each attention layer in the Transformer encoder.
input_embedding_size (:obj:`int`, `optional`, defaults to 256):
Dimensionality of the input embeddings.
output_embedding_size (:obj:`int`, `optional`, defaults to 1664):
Dimensionality of the output embeddings.
intermediate_size (:obj:`int`, `optional`, defaults to 4608):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0):
The dropout probabilitiy 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.
classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the classifier layer when fine-tuning.
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.RemBertModel` or
:class:`~transformers.TFRemBertModel`.
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.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if ``config.is_decoder=True``.
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
Example::
>>> from transformers import RemBertModel, RemBertConfig
>>> # Initializing a RemBERT rembert style configuration
>>> configuration = RemBertConfig()
>>> # Initializing a model from the rembert style configuration
>>> model = RemBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "rembert"
def __init__(
self,
vocab_size=250300,
hidden_size=1152,
num_hidden_layers=32,
num_attention_heads=18,
input_embedding_size=256,
output_embedding_size=1664,
intermediate_size=4608,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
classifier_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
use_cache=True,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=312,
eos_token_id=313,
**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.input_embedding_size = input_embedding_size
self.output_embedding_size = output_embedding_size
self.max_position_embeddings = max_position_embeddings
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.classifier_dropout_prob = classifier_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.tie_word_embeddings = False