File size: 6,657 Bytes
9231ab9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
# 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
|