| from transformers import PretrainedConfig | |
| class transformerConfig(PretrainedConfig): | |
| model_type = "IQsignal_transformer" | |
| def __init__( | |
| self, | |
| vocab_size : int = 32, | |
| key_size : int = 32, | |
| query_size : int = 32, | |
| value_size : int = 32, | |
| num_hiddens : int = 32, | |
| norm_shape : int = 32, | |
| ffn_num_input : int = 32, | |
| ffn_num_hiddens : int = 64, | |
| num_heads : int = 4, | |
| num_layers : int = 2, | |
| dropout : int = 0.1, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.key_size = key_size | |
| self.query_size = query_size | |
| self.value_size = value_size | |
| self.num_hiddens = num_hiddens | |
| self.norm_shape = norm_shape | |
| self.ffn_num_input = ffn_num_input | |
| self.ffn_num_hiddens = ffn_num_hiddens | |
| self.num_heads = num_heads | |
| self.num_layers = num_layers | |
| self.dropout = dropout | |
| super().__init__(**kwargs) |