A complete error is:

ValueError: The model did not return a loss from the inputs, only the following keys: logits. For reference, the inputs it received are input_ids,attention_mask.

However, dataset contains label and train_dataset in the argument has label in column_names.

from transformers import Trainer, TrainingArguments
batch_size = 64
logging_steps = len(emotions_encoded["train"])

training_args = TrainingArguments(output_dir = "model_out",
                                  num_train_epochs=2, 
                                  learning_rate = 2e-5, 
                                  per_device_train_batch_size=batch_size, 
                                  per_device_eval_batch_size=batch_size, 
                                  weight_decay=0.01, 
                                  evaluation_strategy="epoch", 
                                  disable_tqdm=False, 
                                  logging_steps=logging_steps,
                                  push_to_hub=False,
                                  log_level="error")

from transformers import Trainer
trainer = Trainer(model=model, args=training_args,
                  compute_metrics=compute_metrics,
                  train_dataset=emotions_encoded["train"],  # column_names shows <class 'list'>: ['text', 'label', 'input_ids', 'attention_mask'] 
                  eval_dataset=emotions_encoded["validation"],
                  tokenizer=tokenizer)

trainer.train()

This is what I found:
train_dataset in the following trainer.py function has column_names value <class 'list'>: ['label', 'input_ids', 'attention_mask']

def get_train_dataloader(self) -> DataLoader:
        return DataLoader(
            train_dataset,
            batch_size=self._train_batch_size,
            sampler=train_sampler,
            collate_fn=data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
            worker_init_fn=seed_worker,
        )

but datasets arrow_dataset.py function:

def _getitem(self, key: Union[int, slice, str, ListLike[int]], **kwargs) -> Union[Dict, List]:
    format_columns = kwargs["format_columns"] if "format_columns" in kwargs else self._format_columns

because kwargs is None, thereafter self._format_columns is used and its value is <class 'list'>: ['input_ids', 'attention_mask']. Here model is lost.

And that finally caused dataloder.py class _BaseDataLoaderIter function:

    def __next__(self) -> Any:
        with torch.autograd.profiler.record_function(self._profile_name):
            if self._sampler_iter is None:
                # TODO(https://github.com/pytorch/pytorch/issues/76750)
                self._reset()  # type: ignore[call-arg]
            data = self._next_data()

data contains only ‘input_ids’and ‘attention_mask’.

I am a beginner with the boot NLP with Transformers