diff --git a/keras/layers/preprocessing/text_vectorization.py b/keras/layers/preprocessing/text_vectorization.py
index 3e33386bcb8..d1c8cff8d81 100644
--- a/keras/layers/preprocessing/text_vectorization.py
+++ b/keras/layers/preprocessing/text_vectorization.py
@@ -389,7 +389,7 @@ def adapt(self, data, batch_size=None, steps=None):
     During `adapt()`, the layer will build a vocabulary of all string tokens
     seen in the dataset, sorted by occurance count, with ties broken by sort
     order of the tokens (high to low). At the end of `adapt()`, if `max_tokens`
-    is set, the voculary wil be truncated to `max_tokens` size. For example,
+    is set, the vocabulary wil be truncated to `max_tokens` size. For example,
     adapting a layer with `max_tokens=1000` will compute the 1000 most frequent
     tokens occurring in the input dataset. If `output_mode='tf-idf'`, `adapt()`
     will also learn the document frequencies of each token in the input dataset.