diff --git a/flair/datasets/entity_linking.py b/flair/datasets/entity_linking.py
index 801144ca8..a515e0f3a 100644
--- a/flair/datasets/entity_linking.py
+++ b/flair/datasets/entity_linking.py
@@ -946,7 +946,7 @@ def _text_to_cols(self, sentence: Sentence, links: list, outfile):
             links: array containing information about the starting and ending position of an entity mention, as well as its corresponding wiki tag
             outfile: file, to which the output is written
         """
-        for i in range(0, len(sentence)):
+        for i in range(len(sentence)):
             # If there are annotated entity mentions for given post title or a comment thread
             if links:
                 # Keep track which is the correct corresponding entity link, in cases where there is >1 link in a sentence
diff --git a/flair/datasets/sequence_labeling.py b/flair/datasets/sequence_labeling.py
index 849c7c899..d2e4215c8 100644
--- a/flair/datasets/sequence_labeling.py
+++ b/flair/datasets/sequence_labeling.py
@@ -1178,8 +1178,7 @@ def dataset_document_iterator(cls, file_path: Union[Path, str]) -> Iterator[List
     def sentence_iterator(cls, file_path: Union[Path, str]) -> Iterator:
         """An iterator over the sentences in an individual CONLL formatted file."""
         for document in cls.dataset_document_iterator(file_path):
-            for sentence in document:
-                yield sentence
+            yield from document
 
 
 class CONLL_03(ColumnCorpus):
@@ -2490,7 +2489,7 @@ def _add_IOB_tags(self, data_file: Union[str, Path], encoding: str = "utf8", ner
         """
 
         def add_I_prefix(current_line: List[str], ner: int, tag: str):
-            for i in range(0, len(current_line)):
+            for i in range(len(current_line)):
                 if i == 0:
                     f.write(line_list[i])
                 elif i == ner:
@@ -2508,7 +2507,7 @@ def add_I_prefix(current_line: List[str], ner: int, tag: str):
                 if len(line_list) > 2:  # word with tags
                     ner_tag = line_list[ner_column]
                     if ner_tag in ["0", "O"]:  # no chunk
-                        for i in range(0, len(line_list)):
+                        for i in range(len(line_list)):
                             if i == 0:
                                 f.write(line_list[i])
                             elif i == ner_column:
diff --git a/flair/models/sequence_tagger_utils/viterbi.py b/flair/models/sequence_tagger_utils/viterbi.py
index 050697b18..73c10fb67 100644
--- a/flair/models/sequence_tagger_utils/viterbi.py
+++ b/flair/models/sequence_tagger_utils/viterbi.py
@@ -119,7 +119,7 @@ def _format_targets(self, targets: torch.Tensor, lengths: torch.IntTensor):
 
         matrix_indices = [
             [self.tag_dictionary.get_idx_for_item(START_TAG) + (s[0] * self.tagset_size)]
-            + [s[i] + (s[i + 1] * self.tagset_size) for i in range(0, len(s) - 1)]
+            + [s[i] + (s[i + 1] * self.tagset_size) for i in range(len(s) - 1)]
             for s in targets_per_sentence
         ]
 
diff --git a/flair/trainers/plugins/functional/linear_scheduler.py b/flair/trainers/plugins/functional/linear_scheduler.py
index ad1cb8612..51b295c7d 100644
--- a/flair/trainers/plugins/functional/linear_scheduler.py
+++ b/flair/trainers/plugins/functional/linear_scheduler.py
@@ -52,7 +52,6 @@ def before_training_epoch(self, **kw):
     @TrainerPlugin.hook
     def after_training_batch(self, optimizer_was_run: bool, **kw):
         """Do the scheduler step if one-cycle or linear decay."""
-
         # skip if no optimization has happened.
         if not optimizer_was_run:
             return
diff --git a/flair/trainers/trainer.py b/flair/trainers/trainer.py
index 43e99d6ca..7db6a7d17 100644
--- a/flair/trainers/trainer.py
+++ b/flair/trainers/trainer.py
@@ -348,6 +348,7 @@ def train_custom(
             monitor_train_sample: Set this to evaluate on a sample of the train data at the end of each epoch.
                 If you set an int, it will sample this many sentences to evaluate on. If you set a float, it will sample
                 a percentage of data points from train.
+            max_grad_norm: If not None, gradients are clipped to this value before an optimizer.step is called.
             use_final_model_for_eval: If True, the final model is used for the final evaluation. If False, the
                 model from the best epoch as determined by main_evaluation_metric is used for the final evaluation.
             gold_label_dictionary_for_eval: Set to force evaluation to use a particular label dictionary
@@ -364,6 +365,7 @@ def train_custom(
                 be saved each 5 epochs. Default is 0 which means no model saving.
             create_file_logs: If True, logging output is written to a file
             create_loss_file: If True, a loss file logging output is created
+            use_amp: If True, uses the torch automatic mixed precision
             write_weights: If True, write weights to weights.txt on each batch logging event.
             plugins: Any additional plugins you want to pass to the trainer
             **kwargs: Additional arguments, for instance for the optimizer