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modeling_havelock.py

@@ -14,6 +14,8 @@ class MultiLabelCRF(nn.Module):
         self.transitions = nn.Parameter(torch.empty(num_types, 3, 3))
         self.start_transitions = nn.Parameter(torch.empty(num_types, 3))
         self.end_transitions = nn.Parameter(torch.empty(num_types, 3))
+        # Placeholder — will be overwritten by loaded weights if present
+        self.register_buffer("emission_bias", None)
         self._reset_parameters()
 
     def _reset_parameters(self) -> None:
@@ -24,6 +26,11 @@ class MultiLabelCRF(nn.Module):
             self.transitions.data[:, 0, 2] = -10000.0
             self.start_transitions.data[:, 2] = -10000.0
 
+    def _apply_emission_bias(self, emissions: torch.Tensor) -> torch.Tensor:
+        if self.emission_bias is not None:
+            return emissions + self.emission_bias
+        return emissions
+
     def decode(self, emissions: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
         """Viterbi decoding.
 
@@ -33,6 +40,9 @@ class MultiLabelCRF(nn.Module):
 
         Returns: (batch, seq, num_types) best tag sequences
         """
+        # Apply emission bias before decoding
+        emissions = self._apply_emission_bias(emissions)
+
         batch, seq, num_types, _ = emissions.shape
 
         # Reshape to (batch*num_types, seq, 3)