I made the following changes to match sst2.
    # Preprocessing the datasets
    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [name for name in datasets["train"].column_names if name != "label"]
        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
                if sentence2_key == "id" or sentence2_key == "idx":
                    sentence2_key = None
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    print(f"sentence1_key {sentence1_key}")
    print(f"sentence2_key {sentence2_key}")

The print functions returns below:
sentence1_key sentence
sentence2_key None

I think it is the same state as the “sst2” task keys.
However, the same information remains.
[INFO|trainer.py:387] 2021-01-07 13:42:04,003 >> The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: id, sentenc
e.
[INFO|trainer.py:387] 2021-01-07 13:42:04,003 >> The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: id, sente
nce.

The training starts, but I don’t know is it correctly using the data.
Does anyone have idea?
Excuse me for my frequent posting.