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| # coding=utf-8 | |
| # Copyright 2023-present NAVER Corp, The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """ Bros model configuration""" | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| BROS_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "jinho8345/bros-base-uncased": "https://huggingface.co./jinho8345/bros-base-uncased/blob/main/config.json", | |
| "jinho8345/bros-large-uncased": "https://huggingface.co./jinho8345/bros-large-uncased/blob/main/config.json", | |
| } | |
| class BrosConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`BrosModel`] or a [`TFBrosModel`]. It is used to | |
| instantiate a Bros 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 Bros | |
| [jinho8345/bros-base-uncased](https://huggingface.co./jinho8345/bros-base-uncased) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 30522): | |
| Vocabulary size of the Bros model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`BrosModel`] or [`TFBrosModel`]. | |
| 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" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (`str` or `Callable`, *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 when calling [`BrosModel`] or [`TFBrosModel`]. | |
| 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. | |
| pad_token_id (`int`, *optional*, defaults to 0): | |
| The index of the padding token in the token vocabulary. | |
| dim_bbox (`int`, *optional*, defaults to 8): | |
| The dimension of the bounding box coordinates. (x0, y1, x1, y0, x1, y1, x0, y1) | |
| bbox_scale (`float`, *optional*, defaults to 100.0): | |
| The scale factor of the bounding box coordinates. | |
| n_relations (`int`, *optional*, defaults to 1): | |
| The number of relations for SpadeEE(entity extraction), SpadeEL(entity linking) head. | |
| classifier_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the classifier head. | |
| Examples: | |
| ```python | |
| >>> from transformers import BrosConfig, BrosModel | |
| >>> # Initializing a BROS jinho8345/bros-base-uncased style configuration | |
| >>> configuration = BrosConfig() | |
| >>> # Initializing a model from the jinho8345/bros-base-uncased style configuration | |
| >>> model = BrosModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "bros" | |
| 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, | |
| dim_bbox=8, | |
| bbox_scale=100.0, | |
| n_relations=1, | |
| classifier_dropout_prob=0.1, | |
| **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, | |
| **kwargs, | |
| ) | |
| self.dim_bbox = dim_bbox | |
| self.bbox_scale = bbox_scale | |
| self.n_relations = n_relations | |
| self.dim_bbox_sinusoid_emb_2d = self.hidden_size // 4 | |
| self.dim_bbox_sinusoid_emb_1d = self.dim_bbox_sinusoid_emb_2d // self.dim_bbox | |
| self.dim_bbox_projection = self.hidden_size // self.num_attention_heads | |
| self.classifier_dropout_prob = classifier_dropout_prob | |