I am trying to follow the tutorial here 1, but I want to use my own dataset.
I have stored the texts and the labels in a pandas dataset named train. Which has only two columns text, and labels.
I have tried the following code
train_tokenized = tokenizer(list(train.text)), padding="max_length", truncation=True, return_tensors="tf")
train_features = {x: train_tokenized[x] for x in tokenizer.model_input_names}
train_tf_data = tf.data.Dataset.from_tensor_slices((train_features, train.labels))
train_tf_data = train_tf_data.batch(8)

model.compile(
    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=tf.metrics.SparseCategoricalAccuracy(),
)

model.fit(train_tf_data, epochs=3)

But it gives me ValueError: Unsupported type BatchEncoding returned by IteratorSpec._serialize