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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) |