wehub-resource-sync
118 lines
5.2 KiB
Python
118 lines
5.2 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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"""MPNet model configuration"""
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from __future__ import annotations
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from paddlenlp.transformers.configuration_utils import PretrainedConfig
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__all__ = [
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"MPNET_PRETRAINED_INIT_CONFIGURATION",
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"MPNetConfig",
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]
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MPNET_PRETRAINED_INIT_CONFIGURATION = {}
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class MPNetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MPNetModel`]. It is used to
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instantiate a MPNet model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the MPNet.
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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 30527):
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Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MPNetModel`].
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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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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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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 514):
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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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just in case.
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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-5):
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The epsilon used by the layer normalization layers.
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relative_attention_num_buckets (`int`, *optional*, defaults to 32):
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The number of buckets to use for each attention layer.
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Examples:
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```python
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>>> from paddlenlp.transformers import MPNetModel, MPNetConfig
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>>> # Initializing a MPNet mpnet-base style configuration
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>>> configuration = MPNetConfig()
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>>> # Initializing a model from the MPNet mpnet-base style configuration
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>>> model = MPNetModel(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 = "mpnet"
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attribute_map = {
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"num_classes": "num_labels",
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}
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def __init__(
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self,
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vocab_size: int = 30527,
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hidden_size: int = 768,
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num_hidden_layers: int = 12,
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num_attention_heads: int = 12,
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intermediate_size: int = 3072,
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hidden_act: str = "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 = 514,
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initializer_range: float = 0.02,
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layer_norm_eps: float = 1e-5,
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relative_attention_num_buckets: int = 32,
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pad_token_id: int = 1,
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bos_token_id: int = 0,
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eos_token_id: int = 2,
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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.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.relative_attention_num_buckets = relative_attention_num_buckets
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