Upload structa/config.py

structa/config.py

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+from dataclasses import dataclass, field
+from typing import Optional
+
+
+@dataclass
+class StruCTAConfig:
+    """Configuration for StruCTA privacy-preserving transformer."""
+
+    # Model dimensions
+    hidden_dim: int = 768
+    num_encoder_layers: int = 12
+    num_decoder_layers: int = 12
+    num_heads: int = 12
+    ffn_dim: int = 3072
+    dropout: float = 0.1
+    attention_dropout: float = 0.1
+
+    # Graph structural encodings (Graphormer)
+    max_degree: int = 512
+    max_spatial_dist: int = 128
+    max_edge_features: int = 32
+    use_centrality_encoding: bool = True
+    use_spatial_encoding: bool = True
+    use_edge_encoding: bool = True
+
+    # Abstract entity types
+    num_abstract_types: int = 32
+    abstract_type_map: dict = field(default_factory=lambda: {
+        "PERSON": 0,
+        "ORG": 1,
+        "LOC": 2,
+        "GPE": 3,
+        "MONEY": 4,
+        "DATE": 5,
+        "PHONE": 6,
+        "EMAIL": 7,
+        "SSN": 8,
+        "ID": 9,
+        "PRODUCT": 10,
+        "EVENT": 11,
+        "MISC": 12,
+    })
+
+    # Vocabulary
+    vocab_size: int = 50000
+
+    # Privacy
+    use_dp_training: bool = True
+    dp_epsilon: float = 3.0
+    dp_delta: float = 1e-5
+    dp_clip_norm: float = 1.0
+
+    # Decoder cross-modal
+    max_graph_nodes: int = 256
+
+    # Training
+    max_seq_length: int = 512
+    batch_size: int = 32
+    learning_rate: float = 2e-4
+    warmup_steps: int = 60000
+    num_training_steps: int = 1000000
+
+    # Inference
+    use_privacy_verification: bool = True
+    privacy_threshold: float = 0.95