mirror of
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107 lines
5.2 KiB
Python
107 lines
5.2 KiB
Python
# coding=utf-8
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# Copyright 2010, XXX authors
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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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""" XXX model configuration """
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import logging
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from typing import Callable, Union
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from .configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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XXX_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"xxx-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/xxx-base-uncased-config.json",
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"xxx-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/xxx-large-uncased-config.json",
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}
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class XxxConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:`~transformers.XXXModel`.
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It is used to instantiate a XXX model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the XXX `xxx-base-uncased <https://huggingface.co/xxx/xxx-base-uncased>`__ architecture.
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
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to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
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for more information.
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Args:
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vocab_size (:obj:`int`, optional, defaults to 30522):
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Vocabulary size of the XXX model. Defines the different tokens that
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can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XXXModel`.
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hidden_size (:obj:`int`, optional, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (:obj:`int`, optional, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (:obj:`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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hidden_act (:obj:`str` or :obj:`function`, optional, defaults to :obj:`"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler.
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If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
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hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
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The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (:obj:`int`, optional, defaults to 512):
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The maximum sequence length that this model might ever be used with.
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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (:obj:`int`, optional, defaults to 2):
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The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
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initializer_range (:obj:`float`, optional, defaults to 0.02):
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The standard deviation of the :obj:`truncated_normal_initializer` for initializing all weight matrices.
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layer_norm_eps (:obj:`float`, optional, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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gradient_checkpointing (:obj:`bool`, optional, defaults to :obj:`False`):
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If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
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kwargs:
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Additional arguments for common configurations, passed to :class:`~transformers.PretrainedConfig`.
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"""
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model_type = "xxx"
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def __init__(
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self,
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vocab_size: int = 50257,
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hidden_size: int = 1024,
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num_hidden_layers: int = 12,
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num_attention_heads: int = 12,
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hidden_act: Union[str, Callable] = "gelu",
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hidden_dropout_prob: float = 0.1,
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attention_probs_dropout_prob: float = 0.1,
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max_position_embeddings: int = 512,
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type_vocab_size: int = 2,
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initializer_range: float = 0.02,
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layer_norm_epsilon: float = 1e-5,
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gradient_checkpointing: bool = False,
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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.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_epsilon = layer_norm_epsilon
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self.gradient_checkpointing = gradient_checkpointing
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