diff --git a/flair/data.py b/flair/data.py
index 04e10a8dd7..839f750067 100644
--- a/flair/data.py
+++ b/flair/data.py
@@ -1675,7 +1675,7 @@ def __str__(self) -> str:
             f"{len(self.dev) if self.dev else 0} dev + "
             f"{len(self.test) if self.test else 0} test sentences\n - "
         )
-        output += "\n - ".join([f"{type(corpus).__name__} {str(corpus)} - {corpus.name}" for corpus in self.corpora])
+        output += "\n - ".join([f"{type(corpus).__name__} {corpus!s} - {corpus.name}" for corpus in self.corpora])
         return output
 
 
diff --git a/flair/datasets/sequence_labeling.py b/flair/datasets/sequence_labeling.py
index 5525ae45a2..6ed657d9cc 100644
--- a/flair/datasets/sequence_labeling.py
+++ b/flair/datasets/sequence_labeling.py
@@ -560,9 +560,11 @@ def _identify_span_columns(self, column_name_map, skip_first_line):
             # check the first 5 sentences
             probe = []
             for _i in range(5):
-                sentence = self._convert_lines_to_sentence(
-                    self._read_next_sentence(file), word_level_tag_columns=column_name_map
-                )
+                next_sentence = self._read_next_sentence(file)
+                if len(next_sentence) == 0:
+                    break
+
+                sentence = self._convert_lines_to_sentence(next_sentence, word_level_tag_columns=column_name_map)
                 if sentence:
                     probe.append(sentence)
                 else:
diff --git a/flair/inference_utils.py b/flair/inference_utils.py
index e96e16da17..035025c005 100644
--- a/flair/inference_utils.py
+++ b/flair/inference_utils.py
@@ -177,7 +177,7 @@ def __init__(self, embedding, verbose) -> None:
                 self.k = len(result[0]) - 1
                 return
             except sqlite3.Error as err:
-                logger.exception(f"Fail to open sqlite database {self.store_path!s}: {str(err)}")
+                logger.exception(f"Fail to open sqlite database {self.store_path!s}: {err!s}")
         # otherwise, push embedding to database
         if hasattr(embedding, "precomputed_word_embeddings"):
             self.db = sqlite3.connect(str(self.store_path))
@@ -239,7 +239,7 @@ def __init__(self, embedding, verbose) -> None:
                             cursor.close()
                         return
                 except lmdb.Error as err:
-                    logger.exception(f"Fail to open lmdb database {self.store_path!s}: {str(err)}")
+                    logger.exception(f"Fail to open lmdb database {self.store_path!s}: {err!s}")
             # create and load the database in write mode
             if hasattr(embedding, "precomputed_word_embeddings"):
                 pwe = embedding.precomputed_word_embeddings