As a follow-up from my previous question 5, I am trying to fine-tune a model, but I am getting an error: IndexError: tuple index out of range.
I am trying to classify individual sentences with a binary classification.  I am using transformers version 4.2.1 and datasets version 1.2.1
The dataset(s) are .csv files with two columns: “sentence” and “label”.  The following is the code that led to the error - if anyone can help identify my error, please let me know 
import numpy as np
from transformers import TrainingArguments, Trainer
from transformers import BertTokenizer, BertForSequenceClassification
from datasets import load_dataset, load_metric

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

dataset = load_dataset('csv', data_files={'train': "train_data.csv",
                                          'test':  "test_data.csv"})

metric = load_metric('f1', 'accuracy')

encoded_dataset = dataset.map(lambda x: tokenizer(x['sentence'], padding=True, truncation=True), batched=True,load_from_cache_file=False)

batch_size = 16
args = TrainingArguments(
    "test_20210201_1200",
    evaluation_strategy = "epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    num_train_epochs=5,
    weight_decay=0.01,
    seed=18,
    label_names='label',
    load_best_model_at_end=True,
    metric_for_best_model='f1',
)

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return metric.compute(predictions=predictions, references=labels)

trainer = Trainer(
    model,
    args,
    train_dataset=encoded_dataset['train'],
    eval_dataset=encoded_dataset['test'],
    tokenizer=tokenizer,
    compute_metrics=compute_metrics
)

All of that runs with no problem.  However, I get the following error next:
trainer.train()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-2-3435b262f1ae> in <module>
----> 1 trainer.train()

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer.py in train(self, model_path, trial)
    933 
    934             self.control = self.callback_handler.on_epoch_end(self.args, self.state, self.control)
--> 935             self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)
    936 
    937             if self.args.tpu_metrics_debug or self.args.debug:

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer.py in _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch)
   1002         metrics = None
   1003         if self.control.should_evaluate:
-> 1004             metrics = self.evaluate()
   1005             self._report_to_hp_search(trial, epoch, metrics)
   1006 

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer.py in evaluate(self, eval_dataset, ignore_keys, metric_key_prefix)
   1440         start_time = time.time()
   1441 
-> 1442         output = self.prediction_loop(
   1443             eval_dataloader,
   1444             description="Evaluation",

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer.py in prediction_loop(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)
   1569                 losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
   1570             if logits is not None:
-> 1571                 preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
   1572             if labels is not None:
   1573                 labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer_pt_utils.py in nested_concat(tensors, new_tensors, padding_index)
     83     ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
     84     if isinstance(tensors, (list, tuple)):
---> 85         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
     86     elif isinstance(tensors, torch.Tensor):
     87         return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer_pt_utils.py in <genexpr>(.0)
     83     ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
     84     if isinstance(tensors, (list, tuple)):
---> 85         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
     86     elif isinstance(tensors, torch.Tensor):
     87         return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer_pt_utils.py in nested_concat(tensors, new_tensors, padding_index)
     85         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
     86     elif isinstance(tensors, torch.Tensor):
---> 87         return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
     88     elif isinstance(tensors, np.ndarray):
     89         return numpy_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)

/usr/local/bin/miniconda3/envs/tfhub/lib/python3.8/site-packages/transformers/trainer_pt_utils.py in torch_pad_and_concatenate(tensor1, tensor2, padding_index)
     46 def torch_pad_and_concatenate(tensor1, tensor2, padding_index=-100):
     47     """Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary."""
---> 48     if len(tensor1.shape) == 1 or tensor1.shape[1] == tensor2.shape[1]:
     49         return torch.cat((tensor1, tensor2), dim=0)
     50 

IndexError: tuple index out of range

Thanks in advance!