Add transformers-compatible wrapper (AutoModel/AutoTokenizer via trust_remote_code)
0c48771 verified | """transformers-compatible config for the RadGraph-IT DyGIE++ joint NER + relation model. | |
| Mirrors the hyperparameters of `training_v2/src/dygie/model.py`'s `DyGIEModel` plus the | |
| label vocabulary (`training_v2/src/dygie/vocab.py`'s `Vocabulary`), so a `RadgraphModel` can | |
| be reconstructed from `config.json` alone, matching the checkpoint trained by | |
| `run_medbit_full_cv.sh` (single train run on the full split, not actual cross-validation -- | |
| see that script's own header comment). | |
| """ | |
| from transformers import PretrainedConfig | |
| class RadgraphConfig(PretrainedConfig): | |
| model_type = "radgraph_it" | |
| def __init__( | |
| self, | |
| encoder_name: str = "IVN-RIN/medBIT-r3-plus", | |
| max_length: int = 512, | |
| max_span_width: int = 12, | |
| feature_size: int = 20, | |
| feedforward_params: dict = None, | |
| loss_weights: dict = None, | |
| relation_spans_per_word: float = 0.5, | |
| train_encoder: bool = True, | |
| span_pooling: bool = False, | |
| transformer_params: dict = None, | |
| relation_context: bool = False, | |
| relation_feedforward_params: dict = None, | |
| dataset: str = "radgraph-it", | |
| ner_labels: dict = None, | |
| relation_labels: dict = None, | |
| **kwargs, | |
| ): | |
| self.encoder_name = encoder_name | |
| self.max_length = max_length | |
| self.max_span_width = max_span_width | |
| self.feature_size = feature_size | |
| self.feedforward_params = feedforward_params or {"hidden_dims": [150, 150], "dropout": 0.4} | |
| self.loss_weights = loss_weights or {"ner": 0.2, "relation": 1.0} | |
| self.relation_spans_per_word = relation_spans_per_word | |
| self.train_encoder = train_encoder | |
| self.span_pooling = span_pooling | |
| self.transformer_params = transformer_params | |
| self.relation_context = relation_context | |
| self.relation_feedforward_params = relation_feedforward_params | |
| self.dataset = dataset | |
| # Namespace-unqualified label -> index maps, null label "" pinned to 0. Namespaced as | |
| # f"{dataset}__ner_labels" / f"{dataset}__relation_labels" when rebuilt into a Vocabulary | |
| # (see modeling_radgraph.py), matching training_v2/src/dygie/vocab.py exactly. | |
| self.ner_labels = ner_labels or { | |
| "": 0, | |
| "Anatomy::definitely present": 1, | |
| "Observation::definitely present": 2, | |
| "Observation::definitely absent": 3, | |
| "Observation::uncertain": 4, | |
| "Anatomy::definitely absent": 5, | |
| "Anatomy::uncertain": 6, | |
| } | |
| self.relation_labels = relation_labels or { | |
| "": 0, | |
| "modify": 1, | |
| "located_at": 2, | |
| "suggestive_of": 3, | |
| } | |
| super().__init__(**kwargs) | |