lddbert-mlm / configuration_lddbert.py

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	# 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)