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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 | |