| from typing import Optional | |
| from transformers import AutoConfig | |
| from transformers.configuration_utils import PretrainedConfig | |
| class RelikReaderConfig(PretrainedConfig): | |
| model_type = "relik-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, | |
| threshold: Optional[float] = 0.5, | |
| entity_type_loss: bool = False, | |
| add_entity_embedding: bool = None, | |
| training: bool = False, | |
| default_reader_class: Optional[str] = None, | |
| **kwargs | |
| ) -> None: | |
| 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.training = training | |
| self.threshold = threshold | |
| self.default_reader_class = default_reader_class | |
| super().__init__(**kwargs) | |
| AutoConfig.register("relik-reader", RelikReaderConfig) | |