CrossEntropyLoss bugfix.

modules.py

@@ -1,6 +1,7 @@
 import logging
 from typing import NamedTuple, Callable, Union
 
+import torch
 import torch.nn as nn
 from pytorch_pretrained_bert.modeling import ACT2FN, BertLayerNorm, BertModel, BertSelfOutput
 
@@ -92,6 +93,8 @@ class ClassificationModel(nn.Module):
 
         if labels is not None:
             loss_function = nn.CrossEntropyLoss()
+            if len(labels.shape) > 1:
+                labels = torch.argmax(labels, dim=1)
             loss = loss_function(logits, labels)
             return loss, logits
         else: