Getting a value Error: Unable to create a tensor because the feature 'text' has excessive nesting and it expects it to be 'int' for some reason,

Beginners
1 
dataset = load_dataset('csv', data_files={'train': f"{DATA_PATH}\\train.csv", 'test': f"{DATA_PATH}\\test.csv", 'validation': f"{DATA_PATH}\\validation.csv"}, column_names=['text', 'label'], split=['train', 'test', 'validation'])
dataset = dataset_dict.DatasetDict({'train':dataset[0], 'test':dataset[1], 'validation':dataset[2]})
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')

def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

dataset = dataset.map(tokenize_function, batched=True)
FINAL_DS = dataset

training_stuff = {
    "batch_size": 64, 
    "epochs": 4, 
    "learning_rate": 1e-5,
    "weight_decay": 0.01
    }

training_args = TrainingArguments(
    output_dir="Models/DistilBert",
    per_device_train_batch_size=training_stuff["batch_size"],
    evaluation_strategy="steps",
    num_train_epochs=training_stuff["epochs"],
    fp16=True,
    save_steps=100,
    eval_steps=50,
    logging_steps=10,
    weight_decay=training_stuff["weight_decay"],
    learning_rate=training_stuff["learning_rate"],
    save_total_limit=64,
    remove_unused_columns=False,
    push_to_hub=False,
    report_to='tensorboard',
    load_best_model_at_end=True,
)

model = DistilBertModel.from_pretrained(
    'distilbert-base-uncased',
    num_labels=3,
    id2label={0: 'Biased', 1: 'Non-biased', 2: 'No agreemnt'},
    label2id={'Biased': 0, 'Non-biased': 1, 'No agreement': 2},
    )

trainer = Trainer(
            model=model,
            args=training_args,,
            train_dataset=FINAL_DS['train'],
            eval_dataset=FINAL_DS['validation'],
            tokenizer=tokenizer,
        )


train_results = trainer.train()

When I run this I get the following error

ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`text` in this case) have excessive nesting (inputs type `list` where type `int` is expected).