chunk_content:  train_dataset=train_ds,
      eval_dataset=val_ds,
      tokenizer=image_processor,
      compute_metrics=compute_metrics,
      data_collator=collate_fn,
  )
  train_results = trainer.train()
  In just a few minutes, the fine-tuned model shows 96% validation accuracy even on this small
  subset of the training dataset.
  
  
  	Copied
  trainer.evaluate(val_ds)
  {
      "eval_loss": 0.14475855231285095,
      "eval_accuracy": 0.96,
      "eval_runtime": 3.5725,
      "eval_s