Page Menu
Home
c4science
Search
Configure Global Search
Log In
Files
F60567721
configuration_layoutlm.py
No One
Temporary
Actions
Download File
Edit File
Delete File
View Transforms
Subscribe
Mute Notifications
Award Token
Subscribers
None
File Metadata
Details
File Info
Storage
Attached
Created
Wed, May 1, 03:52
Size
6 KB
Mime Type
text/x-python
Expires
Fri, May 3, 03:52 (2 d)
Engine
blob
Format
Raw Data
Handle
17376245
Attached To
R11484 ADDI
configuration_layoutlm.py
View Options
# 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
Event Timeline
Log In to Comment