i am getting this error while trying to run the example script for finetuning t5 on squad for question answering 
Author: preethip02
Pull request: no

### System Info



System Information:
- transformers version: 4.38.0.dev0
- Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.20.3
- Safetensors version: 0.4.2
- Accelerate version: 0.27.2
- Accelerate config:    not found
- PyTorch version (GPU?): 1.12.1+cu116 (True)
- Tensorflow version (GPU?): 2.9.2 (True)
- Flax version (CPU?/GPU?/TPU?): 0.6.3 (gpu)
- Jax version: 0.4.1
- JaxLib version: 0.4.1
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No



### Who can help?

@ArthurZucker  @muellerzr 

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Error: Traceback (most recent call last):
  File "/notebooks/transformers/examples/pytorch/question-answering/run_seq2seq_qa.py", line 758, in <module>
    main()
  File "/notebooks/transformers/examples/pytorch/question-answering/run_seq2seq_qa.py", line 694, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 1606, in train
    return inner_training_loop(
  File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 1635, in _inner_training_loop
    train_dataloader = self.get_train_dataloader()
  File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 845, in get_train_dataloader
    return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
  File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py", line 236, in _init_
    raise ValueError('prefetch_factor option could only be specified in multiprocessing.'
ValueError: prefetch_factor option could only be specified in multiprocessing.let num_workers > 0 to enable multiprocessing.

### Expected behavior

The expected output of running the finetuning script would be a message indicating that the model has been succesfully trained and saved to the checkpoint.

Comments:
Hi @preethip02, thanks for raising this issue! 

Could you provide a minimal code example to reproduce this error? Specifically, how are you launching the script? 

I followed the steps as given in the README file at huggingface/examples

The code i executed was 

git clone https://github.com/huggingface/transformers
cd transformers
pip install .
cd examples/pytorch/question-answering
pip install -r requirements.txt
python run_seq2seq_qa.py \
  --model_name_or_path t5-small \
  --dataset_name squad_v2 \
  --context_column context \
  --question_column question \
  --answer_column answers \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /tmp/debug_seq2seq_squad/

Hi @preethip02, thanks for sharing the script. 

I'm unsure what's causing this issue, but I'm unable to replicate it. I can successfully run the script and run again so it resumes from the last checkpoint on main. 

Could you share your versions of accelerate, torch and evaluate? 

Are the steps I have followed correct? Am I missing something? I ran this script on paperspace, could that be the reason behind the error?

Name: accelerate
Version: 0.27.2
Name: datasets
Version: 2.17.0
Name: torch
Version: 1.12.1+cu116
Name: evaluate
Version: 0.4.1

Hi @preethip02, 

I don't know if paperspace could be affecting this. I was running with:
* accelerate 0.27.0
* datasets 2.17.0
* torch 2.20
* evaluate 0.4.1

I am able to run it successfully in google cloud gpu, except for some caching errors. 

@preethip02 Great, I'm glad to hear it runs now. Is it OK to mark this issue as closed?


Yes