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from typing import Optional |
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from transformers import AutoConfig |
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from transformers.configuration_utils import PretrainedConfig |
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class RelikReaderConfig(PretrainedConfig): |
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model_type = "relik-reader" |
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def __init__( |
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self, |
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transformer_model: str = "microsoft/deberta-v3-base", |
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additional_special_symbols: int = 101, |
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additional_special_symbols_types: Optional[int] = 0, |
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num_layers: Optional[int] = None, |
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activation: str = "gelu", |
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linears_hidden_size: Optional[int] = 512, |
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use_last_k_layers: int = 1, |
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entity_type_loss: bool = False, |
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add_entity_embedding: bool = None, |
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training: bool = False, |
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default_reader_class: Optional[str] = None, |
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**kwargs |
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) -> None: |
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self.transformer_model = transformer_model |
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self.additional_special_symbols = additional_special_symbols |
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self.additional_special_symbols_types = additional_special_symbols_types |
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self.num_layers = num_layers |
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self.activation = activation |
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self.linears_hidden_size = linears_hidden_size |
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self.use_last_k_layers = use_last_k_layers |
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self.entity_type_loss = entity_type_loss |
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self.add_entity_embedding = ( |
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True |
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if add_entity_embedding is None and entity_type_loss |
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else add_entity_embedding |
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) |
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self.training = training |
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self.default_reader_class = default_reader_class |
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super().__init__(**kwargs) |
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AutoConfig.register("relik-reader", RelikReaderConfig) |
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