File size: 5,469 Bytes
f57c906 e03b2ec f57c906 e03b2ec f57c906 e03b2ec f57c906 |
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 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# 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.
""" LddBERT model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
LDDBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
# "lddbert-base-uncased": "https://huggingface.co/lddbert-base-uncased/resolve/main/config.json",
}
class LddBertConfig(PretrainedConfig):
r"""
模型配置。
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LddBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`LddBertModel`] or [`TFLddBertModel`].
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).
sinusoidal_pos_embds (`boolean`, *optional*, defaults to `False`):
Whether to use sinusoidal positional embeddings.
n_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
n_gru_layers (`int`, *optional*, defaults to 1):
GRU 层数.
n_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
dim (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
hidden_dim (`int`, *optional*, defaults to 3072):
The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
activation (`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.
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qa_dropout (`float`, *optional*, defaults to 0.1):
The dropout probabilities used in the question answering model [`LddBertForQuestionAnswering`].
seq_classif_dropout (`float`, *optional*, defaults to 0.2):
The dropout probabilities used in the sequence classification and the multiple choice model
[`LddBertForSequenceClassification`].
Examples:
```python
>>> from transformers import LddBertModel, LddBertConfig
>>> # Initializing a LddBERT configuration
>>> configuration = LddBertConfig()
>>> # Initializing a model from the configuration
>>> model = LddBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "lddbert"
attribute_map = {
"hidden_size": "dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__(
self,
n_layers=6,
n_heads=12,
dim=768,
hidden_dim=4*768,
activation="gelu",
initializer_range=0.02,
vocab_size=30522,
max_position_embeddings=512,
sinusoidal_pos_embds=False,
pad_token_id=0,
type_vocab_size=2,
dropout=0.1,
attention_dropout=0.1,
qa_dropout=0.1,
seq_classif_dropout=0.2,
n_gru_layers=6,
n_cnn_layers=6,
cnn_kernel_size=5,
**kwargs
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.sinusoidal_pos_embds = sinusoidal_pos_embds
self.n_layers = n_layers
self.n_gru_layers = n_gru_layers
self.n_cnn_layers = n_cnn_layers
self.cnn_kernel_size = cnn_kernel_size
self.n_heads = n_heads
self.dim = dim
self.hidden_dim = hidden_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation = activation
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.qa_dropout = qa_dropout
self.seq_classif_dropout = seq_classif_dropout
super().__init__(**kwargs, pad_token_id=pad_token_id)
|