Load text file for RoBERTa pre-training.  
 I migrate my question from https://github.com/huggingface/transformers/pull/4009#issuecomment-690039444

I tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. 
According to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.
```
from datasets import load_dataset
dataset = load_dataset('text', data_files='test.txt',cache_dir="./")
dataset.set_format(type='torch',columns=["text"])
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
next(iter(dataloader))
```

But dataload cannot yield sample and error is:
```
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-12-388aca337e2f> in <module>
----> 1 next(iter(dataloader))

/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
    361 
    362     def __next__(self):
--> 363         data = self._next_data()
    364         self._num_yielded += 1
    365         if self._dataset_kind == _DatasetKind.Iterable and \

/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
    401     def _next_data(self):
    402         index = self._next_index()  # may raise StopIteration
--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    404         if self._pin_memory:
    405             data = _utils.pin_memory.pin_memory(data)

/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

KeyError: 0
```

`dataset.set_format(type='torch',columns=["text"])` returns a log says:
```
Set __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.
```

I noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.
Each sample can be accessed by `dataset["train"]["text"]` instead of `dataset["text"]`. 

Could you please give me any suggestions on how to modify this code to load the text file?

Versions:
Python version 3.7.3
PyTorch version 1.6.0 
TensorFlow version 2.3.0 
datasets version: 1.0.1 
 Hi @thomwolf , @lhoestq ,

Thanks for your suggestions. With the latest version of this package, I can load text data without Internet. 

But I found the speed of dataset loading is very slow. 

My scrips like this: 
```
    def token_encode(examples):
        tokenizer_out = tokenizer(examples['text'], truncation=True,  padding="max_length", add_special_tokens=True, max_length=args.block_size)
        return tokenizer_out
    
    path = Path(file_path)
    files = sorted(path.glob('*'))
    dataset = load_dataset('./text.py', data_files=files, cache_dir = args.data_cache_dir, split="train")
    dataset = dataset.map(token_encode, batched=True)

    dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])
```

I have 1,123,870,657 lines in my input directory. 
I can find the processing speed as following. It is very slow.  
```
| 13/1123871 [00:02<62:37:39,  4.98ba/s]^M  0%|   
| 14/1123871 [00:03<61:27:31,  5.08ba/s]^M  0%|          
| 15/1123871 [00:03<66:34:19,  4.69ba/s]^M  0%|         
| 16/1123871 [00:03<68:25:01,  4.56ba/s]^M  0%|          
| 17/1123871 [00:03<72:00:03,  4.34ba/s]^M  0%|       
```
Do you have any suggestions to accelerate this loading process?