How to choose dimension of Keras embedding layer?

Looking for some guidelines to choose dimension of Keras word embedding layer. For example in a simplified movie review classification code:

# NN layer params

MAX_LEN = 100        # Max length of a review text
VOCAB_SIZE = 10000   # Number of words in vocabulary
EMBEDDING_DIMS = 50  # Embedding dimension - number of components in word embedding vector

text_model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_length=MAX_LEN,input_dim=VOCAB_SIZE,
                              output_dim=EMBEDDING_DIMS),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(6, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
text_model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])

Embedding vector has 50 components in this example. Trained on 17500 and tested on 5625 reviews this model reports:

              precision    recall  f1-score   support

           0       0.87      0.86      0.87      2802
           1       0.87      0.88      0.87      2823

    accuracy                           0.87      5625
   macro avg       0.87      0.87      0.87      5625
weighted avg       0.87      0.87      0.87      5625

With 10 and even 2 dimensions I get similar values in classification report!

Then what guiding principle really works when choosing word embedding vector dimension? When select 50, 10,100, 200, … etc. dimensions?