config.py

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