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