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from transformers import PretrainedConfig
class Seq2LabelsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Seq2LabelsModel`]. It is used to
instantiate a Seq2Labels 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 Seq2Labels 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 30522):
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
pretrained_name_or_path (`str`, *optional*, defaults to `bert-base-cased`):
Pretrained BERT-like model path
load_pretrained (`bool`, *optional*, defaults to `False`):
Whether to load pretrained model from `pretrained_name_or_path`
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`.
predictor_dropout (`float`, *optional*):
The dropout ratio for the classification head.
special_tokens_fix (`bool`, *optional*, defaults to `False`):
Whether to add additional tokens to the BERT's embedding layer.
Examples:
```python
>>> from transformers import BertModel, BertConfig
>>> # Initializing a Seq2Labels style configuration
>>> configuration = Seq2LabelsConfig()
>>> # Initializing a model from the bert-base-uncased style configuration
>>> model = Seq2LabelsModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "bert"
def __init__(
self,
pretrained_name_or_path="bert-base-cased",
vocab_size=15,
num_detect_classes=4,
load_pretrained=False,
initializer_range=0.02,
pad_token_id=0,
use_cache=True,
predictor_dropout=0.0,
special_tokens_fix=False,
label_smoothing=0.0,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.num_detect_classes = num_detect_classes
self.pretrained_name_or_path = pretrained_name_or_path
self.load_pretrained = load_pretrained
self.initializer_range = initializer_range
self.use_cache = use_cache
self.predictor_dropout = predictor_dropout
self.special_tokens_fix = special_tokens_fix
self.label_smoothing = label_smoothing
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