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109 lines
6.3 KiB
109 lines
6.3 KiB
# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
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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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from ..layoutlmv2.configuration_layoutlmv2 import LayoutLMv2Config
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class LayoutXLMConfig(LayoutLMv2Config):
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r"""
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This is the configuration class to store the configuration of a [`LayoutXLMModel`]. It is used to instantiate an
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LayoutXLM model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the LayoutXLM
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[microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] 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 LayoutXLM model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`LayoutXLMModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimension 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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Dimension 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"`, `"selu"` 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 when calling [`LayoutXLMModel`].
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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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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
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The maximum value that the 2D position embedding might ever be used with. Typically set this to something
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large just in case (e.g., 1024).
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max_rel_pos (`int`, *optional*, defaults to 128):
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The maximum number of relative positions to be used in the self-attention mechanism.
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rel_pos_bins (`int`, *optional*, defaults to 32):
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The number of relative position bins to be used in the self-attention mechanism.
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fast_qkv (`bool`, *optional*, defaults to `True`):
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Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
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max_rel_2d_pos (`int`, *optional*, defaults to 256):
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The maximum number of relative 2D positions in the self-attention mechanism.
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rel_2d_pos_bins (`int`, *optional*, defaults to 64):
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The number of 2D relative position bins in the self-attention mechanism.
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convert_sync_batchnorm (`bool`, *optional*, defaults to `True`):
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Whether or not to convert batch normalization layers to synchronized batch normalization layers.
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image_feature_pool_shape (`list[int]`, *optional*, defaults to `[7, 7, 256]`):
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The shape of the average-pooled feature map.
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coordinate_size (`int`, *optional*, defaults to 128):
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Dimension of the coordinate embeddings.
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shape_size (`int`, *optional*, defaults to 128):
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Dimension of the width and height embeddings.
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has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use a relative attention bias in the self-attention mechanism.
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has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use a spatial attention bias in the self-attention mechanism.
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has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
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Whether or not to add visual segment embeddings.
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detectron2_config_args (`dict`, *optional*):
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Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this
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file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutxlm/detectron2_config.py)
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for details regarding default values.
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Example:
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```python
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>>> from transformers import LayoutXLMConfig, LayoutXLMModel
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>>> # Initializing a LayoutXLM microsoft/layoutxlm-base style configuration
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>>> configuration = LayoutXLMConfig()
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>>> # Initializing a model (with random weights) from the microsoft/layoutxlm-base style configuration
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>>> model = LayoutXLMModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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pass
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__all__ = ["LayoutXLMConfig"]
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