| from transformers import PretrainedConfig | |
| class AutextificationMTLConfig(PretrainedConfig): | |
| model_type = "custom-text-classifier" | |
| def __init__( | |
| self, | |
| transformer_name: str = "xlm-roberta-base", | |
| hidden_nodes: int = 64, | |
| threshold: float = 0.9919, | |
| **kwargs, | |
| ): | |
| if hidden_nodes <= 0: | |
| raise ValueError( | |
| f"`hidden_size` must be a positive number, got {hidden_nodes}." | |
| ) | |
| self.transformer_name = transformer_name | |
| self.hidden_nodes = hidden_nodes | |
| self.threshold = threshold | |
| super().__init__(**kwargs) | |