diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d07ccd9..d07ffae 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -10,7 +10,7 @@ repos:
   -   id: check-added-large-files
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: 'v0.11.6'
+  rev: 'v0.11.13'
   hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]
diff --git a/explain.py b/explain.py
index 3579282..99880bd 100644
--- a/explain.py
+++ b/explain.py
@@ -1,5 +1,6 @@
 import argparse
 import io
+import os
 import random
 
 import matplotlib.pyplot as plt
@@ -46,7 +47,9 @@ def update_sample(samples, N, sample):
 )
 
 # Load the trained model
-model = tf.keras.models.load_model(os.path.join(args.input_dir, "nn_model_sherlock.keras"))
+model = tf.keras.models.load_model(
+    os.path.join(args.input_dir, "nn_model_sherlock.keras")
+)
 
 # Produce a randomly sample of background from the training data
 background = []
diff --git a/train.py b/train.py
index a6856f7..ca89da7 100644
--- a/train.py
+++ b/train.py
@@ -51,31 +51,31 @@
 regex_model1 = BatchNormalization(axis=1)(regex_model_input)
 regex_model2 = Dense(
     1000,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(regex_model1)
 regex_model3 = Dropout(0.35)(regex_model2)
 regex_model4 = Dense(
     1000,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(regex_model3)
 
 merged_model2 = BatchNormalization(axis=1)(regex_model4)
 merged_model3 = Dense(
     500,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(merged_model2)
 merged_model4 = Dropout(0.35)(merged_model3)
 merged_model5 = Dense(
     500,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(merged_model4)
 merged_model_output = Dense(
     len(le.classes_),
-    activation='softmax',
+    activation="softmax",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(merged_model5)