157 lines
7.2 KiB
Python
157 lines
7.2 KiB
Python
# coding=utf-8
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# Copyright 2021, The Microsoft Research Asia MarkupLM Team authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" MarkupLM model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class MarkupLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
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MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the MarkupLM
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[microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture.
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Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
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documentation from [`BertConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the
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*inputs_ids* passed to the forward method of [`MarkupLMModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024):
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The maximum value that the tree id unit embedding might ever use. Typically set this to something large
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just in case (e.g., 1024).
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max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256):
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The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large
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just in case (e.g., 256).
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max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024):
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The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something
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large just in case (e.g., 1024).
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tag_pad_id (`int`, *optional*, defaults to 216):
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The id of the padding token in the xpath tags.
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subs_pad_id (`int`, *optional*, defaults to 1001):
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The id of the padding token in the xpath subscripts.
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xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32):
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The hidden size of each tree id unit. One complete tree index will have
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(50*xpath_tag_unit_hidden_size)-dim.
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max_depth (`int`, *optional*, defaults to 50):
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The maximum depth in xpath.
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Examples:
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```python
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>>> from transformers import MarkupLMModel, MarkupLMConfig
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>>> # Initializing a MarkupLM microsoft/markuplm-base style configuration
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>>> configuration = MarkupLMConfig()
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>>> # Initializing a model from the microsoft/markuplm-base style configuration
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>>> model = MarkupLMModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "markuplm"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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bos_token_id=0,
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eos_token_id=2,
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max_xpath_tag_unit_embeddings=256,
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max_xpath_subs_unit_embeddings=1024,
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tag_pad_id=216,
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subs_pad_id=1001,
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xpath_unit_hidden_size=32,
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max_depth=50,
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position_embedding_type="absolute",
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use_cache=True,
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classifier_dropout=None,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.classifier_dropout = classifier_dropout
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# additional properties
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self.max_depth = max_depth
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self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
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self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
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self.tag_pad_id = tag_pad_id
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self.subs_pad_id = subs_pad_id
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self.xpath_unit_hidden_size = xpath_unit_hidden_size
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