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configuration_convbert.py
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# coding=utf-8
# Copyright The HuggingFace team. All rights reserved.
#
# 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.
""" ConvBERT model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json",
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class ConvBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.ConvBertModel`. It is used to
instantiate an ConvBERT 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 ConvBERT `conv-bert-base
<https://huggingface.co/YituTech/conv-bert-base>`__ 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 ConvBERT model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.ConvBertModel` or
:class:`~transformers.TFConvBertModel`.
hidden_size (:obj:`int`, `optional`, defaults to 768):
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_attention_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
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 0.1):
The dropout probabilitiy 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.ConvBertModel`
or :class:`~transformers.TFConvBertModel`.
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.
head_ratio (:obj:`int`, `optional`, defaults to 2):
Ratio gamma to reduce the number of attention heads.
num_groups (:obj:`int`, `optional`, defaults to 1):
The number of groups for grouped linear layers for ConvBert model
conv_kernel_size (:obj:`int`, `optional`, defaults to 9):
The size of the convolutional kernel.
Example::
>>> from transformers import ConvBertModel, ConvBertConfig
>>> # Initializing a ConvBERT convbert-base-uncased style configuration
>>> configuration = ConvBertConfig()
>>> # Initializing a model from the convbert-base-uncased style configuration
>>> model = ConvBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "convbert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
is_encoder_decoder=False,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
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=1,
bos_token_id=0,
eos_token_id=2,
embedding_size=768,
head_ratio=2,
conv_kernel_size=9,
num_groups=1,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
is_encoder_decoder=is_encoder_decoder,
bos_token_id=bos_token_id,
eos_token_id=eos_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.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.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.head_ratio = head_ratio
self.conv_kernel_size = conv_kernel_size
self.num_groups = num_groups

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