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model/config.py

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+from dataclasses import dataclass
+
+
+@dataclass
+class ModelConfig:
+    vocab_size: int = 16000
+    d_model: int = 256
+    num_heads: int = 4
+    d_ff: int = 1024
+    num_encoder_layers: int = 4
+    num_decoder_layers: int = 4
+    max_seq_len: int = 128
+    dropout: float = 0.1
+
+    use_copy: bool = True   # pointer-generator copy mechanism
+
+    # Sentinels appended after the BPE vocab for T5-style span corruption.
+    # Their ids are [vocab_size, vocab_size + num_sentinels). At zero this is
+    # a no-op; at 32 the effective embedding / output projection grows by
+    # 32 rows (~8K extra params at d_model=256).
+    num_sentinels: int = 32
+
+    # special token ids — set after tokenizer is trained
+    pad_id: int = 0
+    unk_id: int = 1
+    bos_id: int = 2
+    eos_id: int = 3
+
+    @property
+    def effective_vocab_size(self) -> int:
+        return self.vocab_size + self.num_sentinels