| from transformers import PretrainedConfig | |
| class SAUTEConfig(PretrainedConfig): | |
| model_type = "saute" | |
| def __init__( | |
| self, | |
| vocab_size=30522, | |
| hidden_size=768, | |
| max_position_embeddings=512, | |
| max_edus_per_dialog=100, | |
| max_edu_length=128, | |
| num_attention_heads=12, | |
| num_hidden_layers=6, | |
| intermediate_size=3072, | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| num_speaker_embeddings=512, | |
| speaker_embeddings_size=768, | |
| max_speakers=200, | |
| num_token_layers=2, | |
| num_edu_layers=2, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.max_edu_length = max_edu_length | |
| self.max_speakers = max_speakers | |
| self.max_edus_per_dialog = max_edus_per_dialog | |
| self.num_attention_heads = num_attention_heads | |
| self.num_hidden_layers = num_hidden_layers | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.num_speaker_embeddings = num_speaker_embeddings | |
| self.speaker_embeddings_size = speaker_embeddings_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.num_token_layers = num_token_layers | |
| self.num_edu_layers = num_edu_layers |