diff --git a/flair/data.py b/flair/data.py
index b5be47e61..704b5c17d 100644
--- a/flair/data.py
+++ b/flair/data.py
@@ -623,7 +623,7 @@ def _print_statistics_for(sentences, name):
 
         size_dict = {}
         for l, c in classes_to_count.items():
-            size_dict = { l: c }
+            size_dict[l] = c
         size_dict['total'] = len(sentences)
 
         stats = {
diff --git a/flair/embeddings.py b/flair/embeddings.py
index 690388444..0fc16c035 100644
--- a/flair/embeddings.py
+++ b/flair/embeddings.py
@@ -540,7 +540,7 @@ class DocumentLSTMEmbeddings(DocumentEmbeddings):
 
     def __init__(self, token_embeddings: List[TokenEmbeddings], hidden_states=128, num_layers=1,
                  reproject_words: bool = True, reproject_words_dimension: int = None, bidirectional: bool = False,
-                 use_first_representation: bool = False, use_word_dropout: bool = True):
+                 use_first_representation: bool = False, use_word_dropout: bool = False, use_locked_dropout: bool = False):
         """The constructor takes a list of embeddings to be combined.
         :param token_embeddings: a list of token embeddings
         :param hidden_states: the number of hidden states in the lstm
@@ -553,6 +553,7 @@ def __init__(self, token_embeddings: List[TokenEmbeddings], hidden_states=128, n
         :param use_first_representation: boolean value, indicating whether to concatenate the first and last
         representation of the lstm to be used as final document embedding.
         :param use_word_dropout: boolean value, indicating whether to use word dropout or not.
+        :param use_locked_dropout: boolean value, indicating whether to use locked dropout or not.
         """
         super().__init__()
 
@@ -586,7 +587,10 @@ def __init__(self, token_embeddings: List[TokenEmbeddings], hidden_states=128, n
                                  bidirectional=self.bidirectional)
 
         # dropouts
-        self.dropout: torch.nn.Module = LockedDropout(0.5)
+        if use_locked_dropout:
+            self.dropout: torch.nn.Module = LockedDropout(0.5)
+        else:
+            self.dropout = torch.nn.Dropout(0.5)
 
         self.use_word_dropout: bool = use_word_dropout
         if self.use_word_dropout:
diff --git a/flair/trainers/text_classification_trainer.py b/flair/trainers/text_classification_trainer.py
index 55e7f2ea0..3e739dae6 100644
--- a/flair/trainers/text_classification_trainer.py
+++ b/flair/trainers/text_classification_trainer.py
@@ -122,7 +122,8 @@ def train(self,
                 log.info("EPOCH {0}: lr {1:.4f} - bad epochs {2}".format(epoch + 1, learning_rate, scheduler.num_bad_epochs))
 
                 dev_metric = train_metric = None
-                dev_loss = train_loss = '_'
+                dev_loss = '_'
+                train_loss = current_loss
 
                 if eval_on_train:
                     train_metric, train_loss = self._calculate_evaluation_results_for(
@@ -208,19 +209,23 @@ def evaluate(self, sentences: List[Sentence], eval_class_metrics: bool = False,
                    range(0, len(sentences), mini_batch_size)]
 
         y_pred = []
-        y_true = convert_labels_to_one_hot([sentence.get_label_names() for sentence in sentences], self.label_dict)
+        y_true = []
 
         for batch in batches:
             scores = self.model.forward(batch)
             labels = self.model.obtain_labels(scores)
             loss = self.model.calculate_loss(scores, batch)
 
+            eval_loss += loss
+
+            y_true.extend([sentence.get_label_names() for sentence in batch])
+            y_pred.extend([[label.value for label in sent_labels] for sent_labels in labels])
+
             if not embeddings_in_memory:
                 clear_embeddings(batch)
 
-            eval_loss += loss
-
-            y_pred.extend(convert_labels_to_one_hot([[label.value for label in sent_labels] for sent_labels in labels], self.label_dict))
+        y_true = convert_labels_to_one_hot(y_true, self.label_dict)
+        y_pred = convert_labels_to_one_hot(y_pred, self.label_dict)
 
         metrics = [calculate_micro_avg_metric(y_true, y_pred, self.label_dict)]
         if eval_class_metrics: