FIX errors in loading eval Dataset in `run_squad_pytorch`

run_squad_pytorch.py

@@ -865,10 +865,11 @@ def main():
         all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
         all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
         all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
-        all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
+        #all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
         all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
 
-        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_example_index)
+        #eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_example_index)
+        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
         if args.local_rank == -1:
             eval_sampler = SequentialSampler(eval_data)
         else:
@@ -877,7 +878,8 @@ def main():
 
         model.eval()
         all_results = []
-        for input_ids, input_mask, segment_ids, label_ids, example_index in eval_dataloader:
+        #for input_ids, input_mask, segment_ids, label_ids, example_index in eval_dataloader:
+        for input_ids, input_mask, segment_ids, example_index in eval_dataloader:
             if len(all_results) % 1000 == 0:
                 logger.info("Processing example: %d" % (len(all_results)))