diff --git a/examples/literals.py b/examples/literals.py
index 1ea8e6a0..510753fb 100644
--- a/examples/literals.py
+++ b/examples/literals.py
@@ -71,7 +71,7 @@
 # Fit a Support Vector Machine on train embeddings and pick the best
 # C-parameters (regularization strength).
 clf = GridSearchCV(
-    SVC(random_state=RANDOM_STATE), {"C": [10 ** i for i in range(-3, 4)]}
+    SVC(random_state=RANDOM_STATE), {"C": [10**i for i in range(-3, 4)]}
 )
 clf.fit(train_embeddings, train_labels)
 
@@ -81,7 +81,7 @@
     f"Predicted {len(test_entities)} entities with an accuracy of "
     + f"{accuracy_score(test_labels, predictions) * 100 :.4f}%"
 )
-print(f"Confusion Matrix ([[TN, FP], [FN, TP]]):")
+print("Confusion Matrix ([[TN, FP], [FN, TP]]):")
 print(confusion_matrix(test_labels, predictions))
 
 print("\nUsing literals:")
@@ -141,7 +141,7 @@
 
 # fit a Support Vector Machine on train embeddings.
 clf = GridSearchCV(
-    SVC(random_state=RANDOM_STATE), {"C": [10 ** i for i in range(-3, 4)]}
+    SVC(random_state=RANDOM_STATE), {"C": [10**i for i in range(-3, 4)]}
 )
 clf.fit(train_embeddings2, train_labels)
 
@@ -151,7 +151,7 @@
     f"Predicted {len(test_entities)} entities with an accuracy of "
     + f"{accuracy_score(test_labels, predictions2) * 100 :.4f}%"
 )
-print(f"Confusion Matrix ([[TN, FP], [FN, TP]]):")
+print("Confusion Matrix ([[TN, FP], [FN, TP]]):")
 print(confusion_matrix(test_labels, predictions2))
 
 f, ax = plt.subplots(1, 2, figsize=(15, 6))
diff --git a/examples/mutag.py b/examples/mutag.py
index 46055df7..eb2bad14 100644
--- a/examples/mutag.py
+++ b/examples/mutag.py
@@ -68,7 +68,7 @@
 # Fit a Support Vector Machine on train embeddings and pick the best
 # C-parameters (regularization strength).
 clf = GridSearchCV(
-    SVC(random_state=RANDOM_STATE), {"C": [10 ** i for i in range(-3, 4)]}
+    SVC(random_state=RANDOM_STATE), {"C": [10**i for i in range(-3, 4)]}
 )
 clf.fit(train_embeddings, train_labels)
 
@@ -78,7 +78,7 @@
     f"Predicted {len(test_entities)} entities with an accuracy of "
     + f"{accuracy_score(test_labels, predictions) * 100 :.4f}%"
 )
-print(f"Confusion Matrix ([[TN, FP], [FN, TP]]):")
+print("Confusion Matrix ([[TN, FP], [FN, TP]]):")
 print(confusion_matrix(test_labels, predictions))
 
 # Reduce the dimensions of entity embeddings to represent them in a 2D plane.
diff --git a/examples/online-learning.py b/examples/online-learning.py
index 25a0402b..7feb8fe9 100644
--- a/examples/online-learning.py
+++ b/examples/online-learning.py
@@ -51,7 +51,7 @@
 # Fit a Support Vector Machine on train embeddings and pick the best
 # C-parameters (regularization strength).
 clf = GridSearchCV(
-    SVC(random_state=RANDOM_STATE), {"C": [10 ** i for i in range(-3, 4)]}
+    SVC(random_state=RANDOM_STATE), {"C": [10**i for i in range(-3, 4)]}
 )
 clf.fit(train_embeddings, train_labels)
 
@@ -61,7 +61,7 @@
     f"Predicted {len(test_entities)} entities with an accuracy of "
     + f"{accuracy_score(test_labels, predictions) * 100 :.4f}%"
 )
-print(f"Confusion Matrix ([[TN, FP], [FN, TP]]):")
+print("Confusion Matrix ([[TN, FP], [FN, TP]]):")
 print(confusion_matrix(test_labels, predictions))
 
 print("\nAdding 20 mores entities.")
@@ -89,7 +89,7 @@
 test_embeddings = embeddings[len(train_entities) :][: -len(new_entities)]
 
 clf = GridSearchCV(
-    SVC(random_state=RANDOM_STATE), {"C": [10 ** i for i in range(-3, 4)]}
+    SVC(random_state=RANDOM_STATE), {"C": [10**i for i in range(-3, 4)]}
 )
 clf.fit(train_embeddings + new_embeddings, train_labels + new_labels)
 
@@ -98,7 +98,7 @@
     f"Predicted {len(test_entities)} entities with an accuracy of "
     + f"{accuracy_score(test_labels, predictions) * 100 :.4f}%"
 )
-print(f"Confusion Matrix ([[TN, FP], [FN, TP]]):")
+print("Confusion Matrix ([[TN, FP], [FN, TP]]):")
 print(confusion_matrix(test_labels, predictions))
 
 print("\nTrain all the entities.")
@@ -116,7 +116,7 @@
 test_embeddings = embeddings[len(train_entities) + len(new_entities) :]
 
 clf = GridSearchCV(
-    SVC(random_state=RANDOM_STATE), {"C": [10 ** i for i in range(-3, 4)]}
+    SVC(random_state=RANDOM_STATE), {"C": [10**i for i in range(-3, 4)]}
 )
 clf.fit(train_embeddings, train_labels + new_labels)
 
@@ -125,7 +125,7 @@
     f"Predicted {len(test_entities)} entities with an accuracy of "
     + f"{accuracy_score(test_labels, predictions) * 100 :.4f}%"
 )
-print(f"Confusion Matrix ([[TN, FP], [FN, TP]]):")
+print("Confusion Matrix ([[TN, FP], [FN, TP]]):")
 print(confusion_matrix(test_labels, predictions))
 
 os.remove("mutag")
diff --git a/examples/samplers.py b/examples/samplers.py
index 2dfe667f..ef3ac8d0 100644
--- a/examples/samplers.py
+++ b/examples/samplers.py
@@ -77,7 +77,7 @@
     # Fit a Support Vector Machine on train embeddings and pick the best
     # C-parameters (regularization strength).
     clf = GridSearchCV(
-        SVC(random_state=RANDOM_STATE), {"C": [10 ** i for i in range(-3, 4)]}
+        SVC(random_state=RANDOM_STATE), {"C": [10**i for i in range(-3, 4)]}
     )
     clf.fit(train_embeddings, train_labels)
 
diff --git a/pyrdf2vec/utils/validation.py b/pyrdf2vec/utils/validation.py
index f72b9581..e5a355b9 100644
--- a/pyrdf2vec/utils/validation.py
+++ b/pyrdf2vec/utils/validation.py
@@ -92,7 +92,7 @@ def is_valid_url(url: str) -> bool:
     """
     try:
         query = "ASK {}"
-        res_code = requests.head(url, params={'query': query}).status_code
+        res_code = requests.head(url, params={"query": query}).status_code
         return res_code == requests.codes.ok
     except Exception:
         return False