Source code for transformers.configuration_layoutlm

# coding=utf-8
# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
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""" LayoutLM model configuration """

from .configuration_bert import BertConfig
from .utils import logging

logger = logging.get_logger(__name__)

    "layoutlm-base-uncased": "",
    "layoutlm-large-uncased": "",

[docs]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 <>`__ 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:`"swish"` 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 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