| """HF-Hub-compatible config for Stoicheia-meter (macronization + metrical scansion). | |
| Same backbone hyperparameters as CharBertConfig (this wraps a Stoicheia backbone | |
| fine-tuned with two extra per-letter heads), plus the two fields that change the | |
| model's *shape* (use_cap, scalar_mix) -- head_dropout/w_mac/w_scan/class weights are | |
| training-only and irrelevant to inference, so they aren't part of this config. | |
| """ | |
| from transformers import PretrainedConfig | |
| class CharBertMeterConfig(PretrainedConfig): | |
| model_type = "char_bert_meter" | |
| def __init__( | |
| self, | |
| n_alpha: int = 24, | |
| mask_id: int = 24, | |
| blank_id: int = 25, | |
| pad_id: int = 26, | |
| n_char_ids: int = 27, | |
| n_boundary: int = 4, | |
| n_dia: int = 49, | |
| n_punct: int = 7, | |
| d_model: int = 1024, | |
| n_heads: int = 16, | |
| depth: int = 32, | |
| char_window: int = 256, | |
| attn_impl: str = "sdpa", | |
| qk_norm: bool = True, | |
| use_cap: bool = True, | |
| scalar_mix: bool = True, | |
| **kwargs, | |
| ): | |
| self.n_alpha = n_alpha | |
| self.mask_id = mask_id | |
| self.blank_id = blank_id | |
| self.pad_id = pad_id | |
| self.n_char_ids = n_char_ids | |
| self.n_boundary = n_boundary | |
| self.n_dia = n_dia | |
| self.n_punct = n_punct | |
| self.d_model = d_model | |
| self.n_heads = n_heads | |
| self.depth = depth | |
| self.char_window = char_window | |
| self.attn_impl = attn_impl | |
| self.qk_norm = qk_norm | |
| self.use_cap = use_cap | |
| self.scalar_mix = scalar_mix | |
| super().__init__(**kwargs) | |