KeyError when training with a dictionary as a dataset. What should the dataset look like?
from transformers import TrainingArguments, Trainer
import numpy as np
import evaluate

training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
metric = evaluate.load("accuracy")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)
  
def train(model, train, eval, **kwargs):
  print('Training model...')
  trainer = Trainer(
      model=model,
      train_dataset=train, #Dataset to train it with
      eval_dataset=eval, #Dataset to test it with
      compute_metrics=compute_metrics,
      **kwargs
  ) 
  trainer.train()
  trainer.save_model('adkai')
  print('Trained!')
model.train(True) #Puts it in training mode.

train.train(model, {
  '#print Hello World':'stdout.write("Hello World\n")',
  '#print hello World':'stdout.write("hello World\n")',
  '# print Hello world':'stdout.write("Hello world\n")',
  '#print hello world':'stdout.write("hello world\n")',
  '#print Hello World!':'stdout.write("Hello World!\n")',
  '# print hello World!':'stdout.write("hello World!\n")',
  '#print goodbye World!':'stdout.write("goodbye World!\n")',
  '# write Hello World':'stdout.write("Hello World\n")',
  '#write hello World':'stdout.write("hello World\n")',
  '# write Hello world':'stdout.write("Hello world\n")',
  '#write hello world':'stdout.write("hello world\n")',
  '# write Hello World!':'stdout.write("Hello World!\n")',
  'set x = 5\n#print x':'stdout.write(x, "\n")',
  'set x = "Go home"\n#output x':'stdout.write(x, "\n")',
  'set xyz = "Hello"# output xyz':'stdout.write(xyz, "\n")', 
  'set Whatever = "nothing"\n#output Whatever':'stdout.write(Whatever, "\n")',
  '#output Whatever':'stdout.write("Whatever\n")',
  '':'',
  '':''
}, {
  '#write Hello world!':'stdout.write("Hello world!\n")',
  '':'',
  '# output Hello World!':'stdout.write("Hello World!\n")',
})This is not the full code, but this is most of it and its really all you need to understand my problem. I need help, here’s the error:Full code can be found here:https://replit.com/@hg0428/AdkAI#main.py/home/runner/AdkAI/venv/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
***** Running training *****
  Num examples = 18
  Num Epochs = 3
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 8
  Gradient Accumulation steps = 1
  Total optimization steps = 9
  0%|                                          | 0/9 [00:00<?, ?it/s]Traceback (most recent call last):
  File "main.py", line 18, in <module>
    train.train(model, {
  File "/home/runner/AdkAI/train.py", line 23, in train
    trainer.train()
  File "/home/runner/AdkAI/venv/lib/python3.8/site-packages/transformers/trainer.py", line 1500, in train
    return inner_training_loop(
  File "/home/runner/AdkAI/venv/lib/python3.8/site-packages/transformers/trainer.py", line 1716, in _inner_training_loop
    for step, inputs in enumerate(epoch_iterator):
  File "/home/runner/AdkAI/venv/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
    data = self._next_data()
  File "/home/runner/AdkAI/venv/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 721, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/runner/AdkAI/venv/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/runner/AdkAI/venv/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
KeyError: 2I really need help here.What should the training and testing data look like? Did I get the format right?I was doing input:output