| from transformers import PretrainedConfig | |
| class ConfigHybridBiGRUModel(PretrainedConfig): | |
| model_type = "bert-bigru" | |
| def __init__(self, | |
| bert_model_name="bert-base-uncased", | |
| tokenizer_name="bert-base-uncased", | |
| hidden_dim=128, | |
| num_classes=2, | |
| gru_layers=1, | |
| bidirectional=True, | |
| dropout=0.3, | |
| concat_layers=0, | |
| pooling="last", | |
| freeze_bert=False, | |
| freeze_n_layers=0, # jumlah layer yg akan di-freeze | |
| freeze_from_start=False, # freeze dari awal atau akhir | |
| **kwargs): | |
| super().__init__(**kwargs) | |
| self.bert_model_name = bert_model_name | |
| self.tokenizer_name = tokenizer_name | |
| self.hidden_dim = hidden_dim | |
| self.num_classes = num_classes | |
| self.gru_layers = gru_layers | |
| self.bidirectional = bidirectional | |
| self.dropout = dropout | |
| self.concat_layers = concat_layers | |
| self.pooling = pooling | |
| self.freeze_bert = freeze_bert | |
| self.freeze_n_layers = freeze_n_layers | |
| self.freeze_from_start = freeze_from_start |