Problem with Ellipsis and "train" error when training custom datasets
Author: pow9791
Pull request: no

### System Info

Describe the bug
There is a problem with ellipsis when loading a custom dataset with math symbols. I tried to fix the ellipsis problem, now I have another issue with training. I'm using mistral 7b for the model.

Hardware details
GPU-rtx A4000 cloud gpu with CUDA

Software version
autogpt-q on Ubuntu


### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```python
tokenized_data = ... # Tokenize your data using your tokenizer

    # Assuming `examples` is a single element list containing the text
    text = examples[0]

    # Skip if the text is an ellipsis object
    if text is Ellipsis:
        return None  # Or any other placeholder for skipped examples

    tokenizer.truncation_side = "left"
    tokenized_inputs = tokenizer(
        text,
        return_tensors="np",
        truncation=True,
        max_length=512
    )

    return tokenized_inputs



tokenized_data = []
for example in data:
    inputs = tokenize_function([example])  # Pass a list with the current example
    if inputs is not None:  # Only append if not skipped
        tokenized_data.append(inputs)


# Assuming `data` is a list of strings containing text examples
if tokenized_data is not Ellipsis:
    # Proceed with filtering if it's not an ellipsis object
    filtered_tokenized_data = filter_tokenized_data(tokenized_data)
else:
    print("Warning: tokenized_data is an ellipsis object. Skipping filtering.")




# Use the filtered data for training
tokenized_train_dataset = filtered_tokenized_data["train"]
tokenized_eval_dataset = filtered_tokenized_data.get("validation")  # Use get for optional validation split

trainer = transformers.Trainer(
    model=model,
    train_dataset=tokenized_train_dataset,
    eval_dataset=tokenized_eval_dataset,
    args=training_args,
    data_collator=data_collator
)



# train model
model.config.use_cache = False  # silence the warnings. Please re-enable for inference!
trainer.train()
KeyError                                  Traceback (most recent call last)
Cell In[49], [line 2](vscode-notebook-cell:?execution_count=49&line=2)
      [1](vscode-notebook-cell:?execution_count=49&line=1) # Use the filtered data for training
----> [2](vscode-notebook-cell:?execution_count=49&line=2) tokenized_train_dataset = filtered_tokenized_data["train"]
      [3](vscode-notebook-cell:?execution_count=49&line=3) tokenized_eval_dataset = filtered_tokenized_data.get("validation")  # Use get for optional validation split
      [5](vscode-notebook-cell:?execution_count=49&line=5) trainer = transformers.Trainer(
      [6](vscode-notebook-cell:?execution_count=49&line=6)     model=model,
      [7](vscode-notebook-cell:?execution_count=49&line=7)     train_dataset=tokenized_train_dataset,
   (...)
     [10](vscode-notebook-cell:?execution_count=49&line=10)     data_collator=data_collator
     [11](vscode-notebook-cell:?execution_count=49&line=11) )

KeyError: 'train' 
# re-enable warnings
model.config.use_cache = True 
```

**Expected behavior**
A clear and concise description of what you expected to happen.

**Screenshots**
If applicable, add screenshots to help explain your problem.

**Additional context**
Add any other context about the problem here.

### Expected behavior

It should train the model!

Comments:
Hey 🤗  thanks for opening an issue! We try to keep the github issues for bugs/feature requests.
Could you ask your question on the [forum](https://discuss.huggingface.co/) instead? I'm sure the community will be of help!

Thanks!

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