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176 lines
8.3 KiB
176 lines
8.3 KiB
# Copyright 2022 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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"""LayoutLMv3 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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class LayoutLMv3Config(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an
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LayoutLMv3 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 LayoutLMv3
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[microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-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 50265):
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Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`LayoutLMv3Model`].
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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 [`LayoutLMv3Model`].
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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-5):
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The epsilon used by the layer normalization layers.
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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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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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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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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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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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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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visual_embed (`bool`, *optional*, defaults to `True`):
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Whether or not to add patch embeddings.
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input_size (`int`, *optional*, defaults to `224`):
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The size (resolution) of the images.
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num_channels (`int`, *optional*, defaults to `3`):
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The number of channels of the images.
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patch_size (`int`, *optional*, defaults to `16`)
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The size (resolution) of the patches.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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Example:
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```python
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>>> from transformers import LayoutLMv3Config, LayoutLMv3Model
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>>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
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>>> configuration = LayoutLMv3Config()
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>>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
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>>> model = LayoutLMv3Model(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 = "layoutlmv3"
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def __init__(
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self,
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vocab_size=50265,
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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-5,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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max_2d_position_embeddings=1024,
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coordinate_size=128,
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shape_size=128,
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has_relative_attention_bias=True,
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rel_pos_bins=32,
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max_rel_pos=128,
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rel_2d_pos_bins=64,
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max_rel_2d_pos=256,
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has_spatial_attention_bias=True,
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text_embed=True,
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visual_embed=True,
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input_size=224,
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num_channels=3,
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patch_size=16,
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classifier_dropout=None,
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**kwargs,
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):
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super().__init__(**kwargs)
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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.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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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.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.max_2d_position_embeddings = max_2d_position_embeddings
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self.coordinate_size = coordinate_size
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self.shape_size = shape_size
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self.has_relative_attention_bias = has_relative_attention_bias
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self.rel_pos_bins = rel_pos_bins
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self.max_rel_pos = max_rel_pos
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self.has_spatial_attention_bias = has_spatial_attention_bias
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self.rel_2d_pos_bins = rel_2d_pos_bins
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self.max_rel_2d_pos = max_rel_2d_pos
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self.text_embed = text_embed
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self.visual_embed = visual_embed
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self.input_size = input_size
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.classifier_dropout = classifier_dropout
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__all__ = ["LayoutLMv3Config"]
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