104 lines
4.8 KiB
Python
104 lines
4.8 KiB
Python
from ... import PretrainedConfig
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from ..deprecated._archive_maps import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class NezhaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`NezhaModel`]. It is used to instantiate an Nezha
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Nezha
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[sijunhe/nezha-cn-base](https://huggingface.co/sijunhe/nezha-cn-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, optional, defaults to 21128):
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Vocabulary size of the NEZHA model. Defines the different tokens that can be represented by the
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*inputs_ids* passed to the forward method of [`NezhaModel`].
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hidden_size (`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 (`int`, optional, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`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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intermediate_size (`int`, optional, defaults to 3072):
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The dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, optional, defaults to "gelu"):
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The non-linear activation function (function or string) in the encoder and pooler.
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hidden_dropout_prob (`float`, optional, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, optional, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, optional, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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(e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, optional, defaults to 2):
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The vocabulary size of the *token_type_ids* passed into [`NezhaModel`].
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initializer_range (`float`, optional, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, optional, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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classifier_dropout (`float`, optional, defaults to 0.1):
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The dropout ratio for attached classifiers.
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is_decoder (`bool`, *optional*, defaults to `False`):
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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Example:
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```python
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>>> from transformers import NezhaConfig, NezhaModel
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>>> # Initializing an Nezha configuration
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>>> configuration = NezhaConfig()
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>>> # Initializing a model (with random weights) from the Nezha-base style configuration model
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>>> model = NezhaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "nezha"
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def __init__(
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self,
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vocab_size=21128,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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max_relative_position=64,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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classifier_dropout=0.1,
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pad_token_id=0,
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bos_token_id=2,
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eos_token_id=3,
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use_cache=True,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **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.intermediate_size = intermediate_size
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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.max_relative_position = max_relative_position
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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_eps = layer_norm_eps
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self.classifier_dropout = classifier_dropout
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self.use_cache = use_cache
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