diff --git a/examples/pytorch/question-answering/run_qa.py b/examples/pytorch/question-answering/run_qa.py
index 3f785f441f..9cf433af34 100755
--- a/examples/pytorch/question-answering/run_qa.py
+++ b/examples/pytorch/question-answering/run_qa.py
@@ -339,6 +339,11 @@ def main():
 
     # Training preprocessing
     def prepare_train_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
@@ -433,6 +438,11 @@ def main():
 
     # Validation preprocessing
     def prepare_validation_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
diff --git a/examples/pytorch/question-answering/run_qa_beam_search.py b/examples/pytorch/question-answering/run_qa_beam_search.py
index 97236e2074..6baab3ea67 100755
--- a/examples/pytorch/question-answering/run_qa_beam_search.py
+++ b/examples/pytorch/question-answering/run_qa_beam_search.py
@@ -327,6 +327,11 @@ def main():
 
     # Training preprocessing
     def prepare_train_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
diff --git a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py b/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
index fb0d4628a9..6843f3a140 100644
--- a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
+++ b/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
@@ -315,6 +315,11 @@ def main():
 
     # Training preprocessing
     def prepare_train_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
@@ -430,6 +435,11 @@ def main():
 
     # Validation preprocessing
     def prepare_validation_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
diff --git a/examples/pytorch/question-answering/run_qa_no_trainer.py b/examples/pytorch/question-answering/run_qa_no_trainer.py
index 9e7d4cd939..7d79733c1c 100755
--- a/examples/pytorch/question-answering/run_qa_no_trainer.py
+++ b/examples/pytorch/question-answering/run_qa_no_trainer.py
@@ -367,6 +367,11 @@ def main():
 
     # Training preprocessing
     def prepare_train_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
@@ -459,6 +464,11 @@ def main():
 
     # Validation preprocessing
     def prepare_validation_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
diff --git a/examples/tensorflow/question-answering/run_qa.py b/examples/tensorflow/question-answering/run_qa.py
index 9badc8ca06..2700d6fa77 100755
--- a/examples/tensorflow/question-answering/run_qa.py
+++ b/examples/tensorflow/question-answering/run_qa.py
@@ -393,6 +393,11 @@ def main():
 
     # Training preprocessing
     def prepare_train_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.
@@ -487,6 +492,11 @@ def main():
 
     # Validation preprocessing
     def prepare_validation_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
         # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
         # in one example possible giving several features when a context is long, each of those features having a
         # context that overlaps a bit the context of the previous feature.