| from transformers import PretrainedConfig |
|
|
| class MetaLATTEConfig(PretrainedConfig): |
| model_type = "metalatte" |
|
|
| def __init__( |
| self, |
| num_labels=15, |
| hidden_size=1280, |
| num_hidden_layers=33, |
| num_attention_heads=20, |
| intermediate_size=5120, |
| hidden_act="gelu", |
| hidden_dropout_prob=0.0, |
| attention_probs_dropout_prob=0.0, |
| max_position_embeddings=1026, |
| initializer_range=0.02, |
| layer_norm_eps=1e-5, |
| esm_model_name="facebook/esm2_t33_650M_UR50D", |
| num_layers_to_finetune=2, |
| num_linear_layers=3, |
| hidden_dim=512, |
| **kwargs |
| ): |
| super().__init__(**kwargs) |
| self.num_labels = num_labels |
| self.hidden_size = hidden_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
| self.intermediate_size = intermediate_size |
| self.hidden_act = hidden_act |
| self.hidden_dropout_prob = hidden_dropout_prob |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob |
| self.max_position_embeddings = max_position_embeddings |
| self.initializer_range = initializer_range |
| self.layer_norm_eps = layer_norm_eps |
| self.esm_model_name = esm_model_name |
| self.num_layers_to_finetune = num_layers_to_finetune |
| self.num_linear_layers = num_linear_layers |
| self.hidden_dim = hidden_dim |
| |
| @classmethod |
| def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): |
| return super().from_pretrained(pretrained_model_name_or_path, **kwargs) |
|
|
| def save_pretrained(self, save_directory): |
| super().save_pretrained(save_directory) |
