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modeling_havelock.py

@@ -106,9 +106,9 @@ class HavelockTokenClassifier(PreTrainedModel):
 
         # Accept injected backbone (from_pretrained path) or build from config
         if backbone is not None:
-            self.backbone = backbone
+            self.bert = backbone
         else:
-            self.backbone = AutoModel.from_config(config)
+            self.bert = AutoModel.from_config(config)
 
         self.dropout = nn.Dropout(getattr(config, "hidden_dropout_prob", 0.1))
         self.classifier = nn.Linear(config.hidden_size, config.num_types * 3)
@@ -135,16 +135,13 @@ class HavelockTokenClassifier(PreTrainedModel):
             use_crf=use_crf,
             **backbone_config.to_dict(),
         )
-
         model = cls(config, backbone=backbone)
-
         if use_crf and obi_bias is not None:
             model.crf.emission_bias = obi_bias.reshape(1, 1, 1, 3)
-
         return model
 
     def forward(self, input_ids, attention_mask=None, **kwargs):
-        hidden = self.backbone(
+        hidden = self.bert(
             input_ids=input_ids, attention_mask=attention_mask
         ).last_hidden_state
         hidden = self.dropout(hidden)