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# coding=utf-8
# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
#
# 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.
""" LayoutLM model configuration """
from ...utils import logging
from ..bert.configuration_bert import BertConfig
logger = logging.get_logger(__name__)
LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"layoutlm-base-uncased": "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json",
"layoutlm-large-uncased": "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json",
}
class LayoutLMConfig(BertConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.LayoutLMModel`. It is used to
instantiate a LayoutLM 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 LayoutLM `layoutlm-base-uncased
<https://huggingface.co/microsoft/layoutlm-base-uncased>`__ architecture.
Configuration objects inherit from :class:`~transformers.BertConfig` and can be used to control the model outputs.
Read the documentation from :class:`~transformers.BertConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 30522):
Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
`inputs_ids` passed to the forward method of :class:`~transformers.LayoutLMModel`.
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:`"silu"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability 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 into :class:`~transformers.LayoutLMModel`.
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.
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
max_2d_position_embeddings (:obj:`int`, `optional`, defaults to 1024):
The maximum value that the 2D position embedding might ever used. Typically set this to something large
just in case (e.g., 1024).
Examples::
>>> from transformers import LayoutLMModel, LayoutLMConfig
>>> # Initializing a LayoutLM configuration
>>> configuration = LayoutLMConfig()
>>> # Initializing a model from the configuration
>>> model = LayoutLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "layoutlm"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
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=0,
gradient_checkpointing=False,
max_2d_position_embeddings=1024,
**kwargs
):
super().__init__(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
intermediate_size=intermediate_size,
hidden_act=hidden_act,
hidden_dropout_prob=hidden_dropout_prob,
attention_probs_dropout_prob=attention_probs_dropout_prob,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
initializer_range=initializer_range,
layer_norm_eps=layer_norm_eps,
pad_token_id=pad_token_id,
gradient_checkpointing=gradient_checkpointing,
**kwargs,
)
self.max_2d_position_embeddings = max_2d_position_embeddings
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