diff --git a/src/model.py b/src/model.py
index 94ee44a..3252c1e 100644
--- a/src/model.py
+++ b/src/model.py
@@ -15,39 +15,44 @@ def build_model() -> Model:
     """
     Build the CNN model
     """
-    model = Sequential(
-        [
-            layers.Conv1D(
-                filters=16,
-                kernel_size=5,
-                activation="relu",
-                kernel_regularizer=l2(L2),
-            ),
-            layers.MaxPool1D(pool_size=3, strides=1),
-            layers.Conv1D(
-                filters=16,
-                kernel_size=3,
-                activation="relu",
-                kernel_regularizer=l2(L2),
-            ),
-            layers.MaxPool1D(pool_size=3, strides=1),
-            layers.Flatten(),
-            layers.Dense(
-                units=16,
-                activation="relu",
-                kernel_regularizer=l2(L2),
-            ),
-            layers.Dropout(rate=0.3),
-            layers.Dense(
-                units=16,
-                activation="relu",
-                kernel_regularizer=l2(L2),
-            ),
-            layers.Dropout(rate=0.3),
-            # FIXME Change output size
-            layers.Dense(units=len(BASES), activation="softmax"),
-        ]
+    model = Sequential()
+    model.add(
+        layers.Conv1D(
+            filters=16,
+            kernel_size=5,
+            activation="relu",
+            kernel_regularizer=l2(L2),
+        )
     )
+    model.add(layers.MaxPool1D(pool_size=3, strides=1))
+    model.add(
+        layers.Conv1D(
+            filters=16,
+            kernel_size=3,
+            activation="relu",
+            kernel_regularizer=l2(L2),
+        )
+    )
+    model.add(layers.MaxPool1D(pool_size=3, strides=1))
+    model.add(layers.Flatten())
+    model.add(
+        layers.Dense(
+            units=16,
+            activation="relu",
+            kernel_regularizer=l2(L2),
+        )
+    )
+    model.add(layers.Dropout(rate=0.3))
+    model.add(
+        layers.Dense(
+            units=16,
+            activation="relu",
+            kernel_regularizer=l2(L2),
+        )
+    )
+    model.add(layers.Dropout(rate=0.3))
+    # FIXME Change output size
+    model.add(layers.Dense(units=len(BASES), activation="softmax"))
     model.compile(
         optimizer=Adam(LEARNING_RATE),
         loss=sparse_categorical_crossentropy,