Merge pull request #191 from nhatchan/20190113_py35_finetune

lm_finetuning compatibility with Python 3.5

examples/run_lm_finetuning.py

@@ -139,11 +139,11 @@ class BERTDataset(Dataset):
         # transform sample to features
         cur_features = convert_example_to_features(cur_example, self.seq_len, self.tokenizer)
 
-        cur_tensors = {"input_ids": torch.tensor(cur_features.input_ids),
-                       "input_mask": torch.tensor(cur_features.input_mask),
-                       "segment_ids": torch.tensor(cur_features.segment_ids),
-                       "lm_label_ids": torch.tensor(cur_features.lm_label_ids),
-                       "is_next": torch.tensor(cur_features.is_next)}
+        cur_tensors = (torch.tensor(cur_features.input_ids),
+                       torch.tensor(cur_features.input_mask),
+                       torch.tensor(cur_features.segment_ids),
+                       torch.tensor(cur_features.lm_label_ids),
+                       torch.tensor(cur_features.is_next))
 
         return cur_tensors
 
@@ -592,7 +592,7 @@ def main():
             tr_loss = 0
             nb_tr_examples, nb_tr_steps = 0, 0
             for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
-                batch = tuple(t.to(device) for t in batch.values())
+                batch = tuple(t.to(device) for t in batch)
                 input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
                 loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
                 if n_gpu > 1: