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""" DistilBERT model configuration """
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
"distilbert-base-uncased-distilled-squad": "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json",
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
"distilbert-base-cased-distilled-squad": "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json",
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
"distilbert-base-multilingual-cased": "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json",
"distilbert-base-uncased-finetuned-sst-2-english": "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json",
}
[docs]class DistilBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.DistilBertModel` or a
:class:`~transformers.TFDistilBertModel`. It is used to instantiate a DistilBERT 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 DistilBERT `distilbert-base-uncased
<https://huggingface.co/distilbert-base-uncased>`__ 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 DistilBERT model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.DistilBertModel` or
:class:`~transformers.TFDistilBertModel`.
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).
sinusoidal_pos_embds (:obj:`boolean`, `optional`, defaults to :obj:`False`):
Whether to use sinusoidal positional embeddings.
n_layers (:obj:`int`, `optional`, defaults to 6):
Number of hidden layers in the Transformer encoder.
n_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
dim (:obj:`int`, `optional`, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
hidden_dim (:obj:`int`, `optional`, defaults to 3072):
The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
activation (:obj:`str` or :obj:`Callable`, `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:`"silu"` and :obj:`"gelu_new"` are supported.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qa_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probabilities used in the question answering model
:class:`~transformers.DistilBertForQuestionAnswering`.
seq_classif_dropout (:obj:`float`, `optional`, defaults to 0.2):
The dropout probabilities used in the sequence classification and the multiple choice model
:class:`~transformers.DistilBertForSequenceClassification`.
Examples::
>>> from transformers import DistilBertModel, DistilBertConfig
>>> # Initializing a DistilBERT configuration
>>> configuration = DistilBertConfig()
>>> # Initializing a model from the configuration
>>> model = DistilBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "distilbert"
attribute_map = {
"hidden_size": "dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__(
self,
vocab_size=30522,
max_position_embeddings=512,
sinusoidal_pos_embds=False,
n_layers=6,
n_heads=12,
dim=768,
hidden_dim=4 * 768,
dropout=0.1,
attention_dropout=0.1,
activation="gelu",
initializer_range=0.02,
qa_dropout=0.1,
seq_classif_dropout=0.2,
pad_token_id=0,
**kwargs
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.sinusoidal_pos_embds = sinusoidal_pos_embds
self.n_layers = n_layers
self.n_heads = n_heads
self.dim = dim
self.hidden_dim = hidden_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation = activation
self.initializer_range = initializer_range
self.qa_dropout = qa_dropout
self.seq_classif_dropout = seq_classif_dropout
super().__init__(**kwargs, pad_token_id=pad_token_id)
class DistilBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
]
)
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("last_hidden_state", {0: "batch", 1: "sequence"})])