| 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, |
| 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: |
| |
| 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) |
|
|
