| from transformers import PretrainedConfig | |
| class RNAErnie2Config(PretrainedConfig): | |
| model_type = "rnaernie2" | |
| auto_map = { | |
| "AutoConfig": "configuration_rnaernie2.RNAErnie2Config", | |
| "AutoModel": "modeling_rnaernie2.RNAErnie2Model", | |
| "AutoModelForMaskedLM": "modeling_rnaernie2.RNAErnie2ForMaskedLM", | |
| } | |
| def __init__( | |
| self, | |
| vocab_size: int = 11, | |
| hidden_size: int = 768, | |
| num_hidden_layers: int = 12, | |
| num_attention_heads: int = 12, | |
| intermediate_size: int = 3072, | |
| hidden_act: str = "gelu", | |
| hidden_dropout_prob: float = 0.1, | |
| attention_probs_dropout_prob: float = 0.1, | |
| max_position_embeddings: int = 2048, | |
| type_vocab_size: int = 2, | |
| layer_norm_eps: float = 1e-5, | |
| pad_token_id: int = 0, | |
| **kwargs, | |
| ): | |
| super().__init__(pad_token_id=pad_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| 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.type_vocab_size = type_vocab_size | |
| self.layer_norm_eps = layer_norm_eps | |