diff --git a/retvec/tf/layers/tokenizer.py b/retvec/tf/layers/tokenizer.py
index e48a1a1..6360576 100644
--- a/retvec/tf/layers/tokenizer.py
+++ b/retvec/tf/layers/tokenizer.py
@@ -57,7 +57,7 @@ class RETVecTokenizer(tf.keras.layers.Layer):
     def __init__(
         self,
         sequence_length: int = 128,
-        model: Optional[Union[str, Path]] = 'retvec-v1',
+        model: Optional[Union[str, Path]] = "retvec-v1",
         trainable: bool = False,
         sep: str = "",
         standardize: Optional[str] = None,
@@ -79,7 +79,7 @@ def __init__(
                 `sequence_length` words.
 
             model: Path to saved pretrained RETVec model, str or pathlib.Path
-                object. 'retvec-v1' to use V1 of the pre-trained RETVec word
+                object. "retvec-v1" to use V1 of the pre-trained RETVec word
                 embedding model, None to use the default RETVec character
                 encoding.
 
diff --git a/retvec/tf/models/positional_embeddings.py b/retvec/tf/models/positional_embeddings.py
index 58ab0f3..f455cf6 100644
--- a/retvec/tf/models/positional_embeddings.py
+++ b/retvec/tf/models/positional_embeddings.py
@@ -132,7 +132,7 @@ def __init__(
         self._hidden_size = hidden_size
         self._min_timescale = min_timescale
         self._max_timescale = max_timescale
-        self._init_scale = 1 / self._hidden_size**0.5
+        self._init_scale = 1 / self._hidden_size ** 0.5
 
         self._scale = self.add_weight(
             name="sin_scale",
@@ -217,7 +217,7 @@ def rope(x: Tensor, axis: Union[List[int], int]) -> Tensor:
 
     half_size = shape[-1] // 2
     freq_seq = tf.cast(tf.range(half_size), tf.float32) / float(half_size)
-    inv_freq = 10000**-freq_seq
+    inv_freq = 10000 ** -freq_seq
     sinusoid = tf.einsum("...,d->...d", position, inv_freq)
     sin = tf.cast(tf.sin(sinusoid), dtype=x.dtype)
     cos = tf.cast(tf.cos(sinusoid), dtype=x.dtype)