Fix link to TextVectorization layer doc

docs/tutorials/word_embeddings.ipynb

@@ -429,7 +429,7 @@
         "## Create a classification model\n",
         "\n",
         "Use the [Keras Sequential API](https://www.tensorflow.org/guide/keras/sequential_model) to define the sentiment classification model. In this case it is a \"Continuous bag of words\" style model.\n",
-        "* The [`TextVectorization`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/TextVectorization) layer transforms strings into vocabulary indices. You have already initialized `vectorize_layer` as a TextVectorization layer and built its vocabulary by calling `adapt` on `text_ds`. Now vectorize_layer can be used as the first layer of your end-to-end classification model, feeding transformed strings into the Embedding layer.\n",
+        "* The [`TextVectorization`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization) layer transforms strings into vocabulary indices. You have already initialized `vectorize_layer` as a TextVectorization layer and built its vocabulary by calling `adapt` on `text_ds`. Now vectorize_layer can be used as the first layer of your end-to-end classification model, feeding transformed strings into the Embedding layer.\n",
         "* The [`Embedding`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding) layer takes the integer-encoded vocabulary and looks up the embedding vector for each word-index. These vectors are learned as the model trains. The vectors add a dimension to the output array. The resulting dimensions are: `(batch, sequence, embedding)`.\n",
         "\n",
         "* The [`GlobalAveragePooling1D`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D) layer returns a fixed-length output vector for each example by averaging over the sequence dimension. This allows the model to handle input of variable length, in the simplest way possible.\n",