| from transformers import PretrainedConfig | |
| class EmCoderConfig(PretrainedConfig): | |
| model_type = "emcoder" | |
| def __init__( | |
| self, | |
| vocab_size=50368, | |
| d_model=768, | |
| n_head=12, | |
| n_layers=6, | |
| d_ffn=2048, | |
| dropout=0.1, | |
| num_labels=28, | |
| base_encoder_path="", | |
| id2label=None, | |
| label2id=None, | |
| **kwargs, | |
| ): | |
| if id2label is not None: | |
| id2label = {int(k): v for k, v in id2label.items()} | |
| super().__init__(id2label=id2label, label2id=label2id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.d_model = d_model | |
| self.n_head = n_head | |
| self.n_layers = n_layers | |
| self.d_ffn = d_ffn | |
| self.dropout = dropout | |
| self.num_labels = num_labels | |
| self.base_encoder_path = base_encoder_path | |