How to adapt TextVectorization layer on tf.Dataset

I load my dataset like this:

self.train_ds = tf.data.experimental.make_csv_dataset(
            self.config["input_paths"]["data"]["train"],
            batch_size=self.params["batch_size"],
            shuffle=False,
            label_name="tags",
            num_epochs=1,
        )

My TextVectorization layer looks like this:

vectorizer = tf.keras.layers.TextVectorization(
            standardize=code_standaridization,
            split="whitespace",
            output_mode="int",
            output_sequence_length=params["input_dim"],
            max_tokens=100_000,
        )

And I thought this is going to be enough:

vectorizer.adapt(data_provider.train_ds)

But its not, I have this error:

TypeError: Expected string, but got Tensor("IteratorGetNext:0", shape=(None, None), dtype=string) of type 'Tensor'.

Can I somehow adapt my vectorizer on TensorFlow dataset?