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# coding=utf-8 | |
# Copyright 2020 The Google AI Language Team Authors and The HuggingFace Inc. team. | |
# | |
# 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. | |
""" BertGeneration model configuration""" | |
from ...configuration_utils import PretrainedConfig | |
class BertGenerationConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`BertGenerationPreTrainedModel`]. It is used to | |
instantiate a BertGeneration model according to the specified arguments, defining the model architecture. | |
Instantiating a configuration with the defaults will yield a similar configuration to that of the BertGeneration | |
[google/bert_for_seq_generation_L-24_bbc_encoder](https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder) | |
architecture. | |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
documentation from [`PretrainedConfig`] for more information. | |
Args: | |
vocab_size (`int`, *optional*, defaults to 50358): | |
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the | |
`inputs_ids` passed when calling [`BertGeneration`]. | |
hidden_size (`int`, *optional*, defaults to 1024): | |
Dimensionality of the encoder layers and the pooler layer. | |
num_hidden_layers (`int`, *optional*, defaults to 24): | |
Number of hidden layers in the Transformer encoder. | |
num_attention_heads (`int`, *optional*, defaults to 16): | |
Number of attention heads for each attention layer in the Transformer encoder. | |
intermediate_size (`int`, *optional*, defaults to 3072): | |
Dimensionality of the "intermediate" (often called feed-forward) layer in the Transformer encoder. | |
hidden_act (`str` or `function`, *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. | |
hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for the attention probabilities. | |
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). | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
The epsilon used by the layer normalization layers. | |
position_embedding_type (`str`, *optional*, defaults to `"absolute"`): | |
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For | |
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to | |
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). | |
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models | |
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). | |
is_decoder (`bool`, *optional*, defaults to `False`): | |
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. | |
use_cache (`bool`, *optional*, defaults to `True`): | |
Whether or not the model should return the last key/values attentions (not used by all models). Only | |
relevant if `config.is_decoder=True`. | |
Examples: | |
```python | |
>>> from transformers import BertGenerationConfig, BertGenerationEncoder | |
>>> # Initializing a BertGeneration config | |
>>> configuration = BertGenerationConfig() | |
>>> # Initializing a model (with random weights) from the config | |
>>> model = BertGenerationEncoder(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "bert-generation" | |
def __init__( | |
self, | |
vocab_size=50358, | |
hidden_size=1024, | |
num_hidden_layers=24, | |
num_attention_heads=16, | |
intermediate_size=4096, | |
hidden_act="gelu", | |
hidden_dropout_prob=0.1, | |
attention_probs_dropout_prob=0.1, | |
max_position_embeddings=512, | |
initializer_range=0.02, | |
layer_norm_eps=1e-12, | |
pad_token_id=0, | |
bos_token_id=2, | |
eos_token_id=1, | |
position_embedding_type="absolute", | |
use_cache=True, | |
**kwargs, | |
): | |
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |
self.vocab_size = vocab_size | |
self.hidden_size = hidden_size | |
self.num_hidden_layers = num_hidden_layers | |
self.num_attention_heads = num_attention_heads | |
self.hidden_act = hidden_act | |
self.intermediate_size = intermediate_size | |
self.hidden_dropout_prob = hidden_dropout_prob | |
self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
self.max_position_embeddings = max_position_embeddings | |
self.initializer_range = initializer_range | |
self.layer_norm_eps = layer_norm_eps | |
self.position_embedding_type = position_embedding_type | |
self.use_cache = use_cache | |