Y2

training_args = TrainingArguments(
    output_dir="my_awesome_food_model",
    remove_unused_columns=False,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=16,
    gradient_accumulation_steps=4,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    warmup_ratio=0.1,
    logging_steps=10,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy",
    push_to_hub=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=food["train"],
    eval_dataset=food["test"],
    tokenizer=image_processor,
    compute_metrics=compute_metrics,
)

trainer.train()