from typing import Optional from transformers import AutoConfig from transformers.configuration_utils import PretrainedConfig class WSLReaderConfig(PretrainedConfig): model_type = "wsl-reader" def __init__( self, transformer_model: str = "microsoft/deberta-v3-base", additional_special_symbols: int = 101, additional_special_symbols_types: Optional[int] = 0, num_layers: Optional[int] = None, activation: str = "gelu", linears_hidden_size: Optional[int] = 512, use_last_k_layers: int = 1, entity_type_loss: bool = False, add_entity_embedding: bool = None, binary_end_logits: bool = False, training: bool = False, default_reader_class: Optional[str] = None, threshold: Optional[float] = 0.5, **kwargs ) -> None: # TODO: add name_or_path to kwargs self.transformer_model = transformer_model self.additional_special_symbols = additional_special_symbols self.additional_special_symbols_types = additional_special_symbols_types self.num_layers = num_layers self.activation = activation self.linears_hidden_size = linears_hidden_size self.use_last_k_layers = use_last_k_layers self.entity_type_loss = entity_type_loss self.add_entity_embedding = ( True if add_entity_embedding is None and entity_type_loss else add_entity_embedding ) self.threshold = threshold self.binary_end_logits = binary_end_logits self.training = training self.default_reader_class = default_reader_class super().__init__(**kwargs)