| from transformers.configuration_utils import PretrainedConfig | |
| class UbkeConfig(PretrainedConfig): | |
| model_type = "ubke" | |
| def __init__( | |
| self, | |
| vocab_size=50267, | |
| entity_vocab_size=500000, | |
| num_category_entities=0, | |
| hidden_size=768, | |
| entity_emb_size=256, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=512, | |
| type_vocab_size=2, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-12, | |
| use_entity_aware_attention=True, | |
| classifier_dropout=None, | |
| normalize_entity_embeddings=False, | |
| entity_temperature=1.0, | |
| pad_token_id=1, | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| **kwargs, | |
| ) | |
| self.vocab_size = vocab_size | |
| self.entity_vocab_size = entity_vocab_size | |
| self.num_category_entities = num_category_entities | |
| self.hidden_size = hidden_size | |
| self.entity_emb_size = entity_emb_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.use_entity_aware_attention = use_entity_aware_attention | |
| self.classifier_dropout = classifier_dropout | |
| self.normalize_entity_embeddings = normalize_entity_embeddings | |
| self.entity_temperature = entity_temperature | |