# coding=utf-8 # Copyright 2021, The Microsoft Research Asia MarkupLM 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. """MarkupLM model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class MarkupLMConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a MarkupLM 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 MarkupLM [microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture. Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the documentation from [`BertConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`MarkupLMModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`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 (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the tree id unit embedding might ever use. Typically set this to something large just in case (e.g., 1024). max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256): The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large just in case (e.g., 256). max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something large just in case (e.g., 1024). tag_pad_id (`int`, *optional*, defaults to 216): The id of the padding token in the xpath tags. subs_pad_id (`int`, *optional*, defaults to 1001): The id of the padding token in the xpath subscripts. xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32): The hidden size of each tree id unit. One complete tree index will have (50*xpath_tag_unit_hidden_size)-dim. max_depth (`int`, *optional*, defaults to 50): The maximum depth in xpath. Examples: ```python >>> from transformers import MarkupLMModel, MarkupLMConfig >>> # Initializing a MarkupLM microsoft/markuplm-base style configuration >>> configuration = MarkupLMConfig() >>> # Initializing a model from the microsoft/markuplm-base style configuration >>> model = MarkupLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "markuplm" 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, bos_token_id=0, eos_token_id=2, max_xpath_tag_unit_embeddings=256, max_xpath_subs_unit_embeddings=1024, tag_pad_id=216, subs_pad_id=1001, xpath_unit_hidden_size=32, max_depth=50, position_embedding_type="absolute", use_cache=True, classifier_dropout=None, **kwargs, ): super().__init__( pad_token_id=pad_token_id, 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.hidden_act = hidden_act self.intermediate_size = intermediate_size 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.position_embedding_type = position_embedding_type self.use_cache = use_cache self.classifier_dropout = classifier_dropout # additional properties self.max_depth = max_depth self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings self.tag_pad_id = tag_pad_id self.subs_pad_id = subs_pad_id self.xpath_unit_hidden_size = xpath_unit_hidden_size __all__ = ["MarkupLMConfig"]