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configuration_distilbert.py
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# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" DistilBERT model configuration """
from
...configuration_utils
import
PretrainedConfig
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"
,
}
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"
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
):
super
()
.
__init__
(
**
kwargs
,
pad_token_id
=
pad_token_id
)
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
@property
def
hidden_size
(
self
):
return
self
.
dim
@property
def
num_attention_heads
(
self
):
return
self
.
n_heads
@property
def
num_hidden_layers
(
self
):
return
self
.
n_layers
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