Tensorflow - Text Classification - Shapes (None,) and (None, 250, 100) are incompatible error

I want to classify text with multiple labels. I use TextVectorization layer and CategoricalCrossEntropy function. Here is my model code:

Text Vectorizer:

def custom_standardization(input_data):
  print(input_data[:5])
  lowercase = tf.strings.lower(input_data)
  stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')
  return tf.strings.regex_replace(stripped_html,
                                  '[%s]' % re.escape(string.punctuation),
                                  '')

max_features = 10000
sequence_length = 250

vectorize_layer = layers.TextVectorization(
    standardize=custom_standardization,
    max_tokens=max_features,
    output_mode='int',
    output_sequence_length=sequence_length)

Model generation:

MAX_TOKENS_NUM = 5000  # Maximum vocab size.
MAX_SEQUENCE_LEN = 40  # Sequence length to pad the outputs to.
EMBEDDING_DIMS = 100

model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(1,), dtype=tf.string))
model.add(vectorize_layer)
model.add(tf.keras.layers.Embedding(MAX_TOKENS_NUM + 1, EMBEDDING_DIMS))
model.summary()
model.compile(loss=losses.CategoricalCrossentropy(from_logits=True),
              optimizer='adam',
              metrics=tf.metrics.CategoricalAccuracy())

FIT :

epochs = 10
history = model.fit(
    x_train,
    y=y_train,
    epochs=epochs)

x_train is a list of texts like ['This is a text about science.', 'This is a text about art',...]

y_train also is a list of texts like ['Science','Art',...]

When I try to run fitting code it gives the following error:

ValueError: Shapes (None,) and (None, 250, 100) are incompatible

What am i doing wrong? And also I'd like to learn if it's a good approach/model for classifying test with multiple labels?

EDIT:

I edited my code according to Frightera's answer. Here is my model:

MAX_TOKENS_NUM = 5000  # Maximum vocab size.
MAX_SEQUENCE_LEN = 40  # Sequence length to pad the outputs to.
EMBEDDING_DIMS = 100

model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(1,), dtype=tf.string))
model.add(vectorize_layer)
model.add(tf.keras.layers.Embedding(MAX_TOKENS_NUM + 1, EMBEDDING_DIMS))
model.add(layers.Dropout(0.2))
model.add(layers.GlobalAveragePooling1D())
model.add(layers.Dropout(0.2))
model.add(layers.Dense(len(labels)))
model.summary()
model.compile(loss=losses.SparseCategoricalCrossentropy(from_logits=True),
              optimizer='adam',
              metrics=tf.metrics.SparseCategoricalAccuracy())

And I pass y_train_int instead of y_train by converting categories to indexes with y_train_int = [get_label_index(label) for label in y_train]

epochs = 10
history = model.fit(
    x_train,
    y=y_train_int,
    epochs=epochs)

Now the model fits, but when I check loss function with plt.plot(history.history['loss']) it's an all zero line like below:

Is this model good for classification. Do I need those layers between input layer and final Dense Layer(Embedding etc.)? What am I doing wrong?

EDIT 2:
I have the above model now. I am using SparseCategoricalEntropy and passing to the last Dense layer length of labels which is 78 and now it fits the model.

Now when I use model.predict(x_test), it gives following results:

array([[ 1.3232083 ,  3.4263668 ,  0.3206688 , ..., -1.9279423 ,
        -0.83103067, -5.3442082 ],
       [ 0.11507592, -2.0753977 , -0.07149621, ..., -0.27729607,
        -1.132122  , -2.4074485 ],
       [ 0.87828857, -0.5063573 ,  1.5770453 , ...,  0.72519284,
         0.50958884,  3.7006462 ],
       ...,
       [ 0.35316354, -3.1919005 , -0.25520897, ..., -1.648859  ,
        -2.2707412 , -4.321298  ],
       [ 0.89357865,  1.3001428 ,  0.17324057, ..., -0.8185719 ,
        -1.4108973 , -3.674326  ],
       [ 1.6258209 , -0.59622926,  0.7382731 , ..., -0.8473997 ,
        -0.90670204, -4.043623  ]], dtype=float32)

How can I convert these to labels?

I resolved this according to the comments as follows for text classification:

1. Use Dense layer with number of unique labels in the end of the model.
2. Convert string category labels to indexes and use SparseCategoricalCrossEntropy and SparseCategoricalAccuracy in the model.
3. When converting results to string labels, get the max valued output and get index of it in the labels list.