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 
 Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).
But finally got it working. This is what I did after looking into the documentation.

1. split the whole dataset file into smaller files
```bash
mkdir ./shards
split -a 4 -l 256000 -d full_raw_corpus.txt ./shards/shard_
````
2. Pass paths of small data files to `load_dataset`
```python
files = glob.glob('shards/*')
from datasets import load_dataset
dataset = load_dataset('text', data_files=files, split='train')
```
(On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)

3. Tokenization
```python
def encode(examples):
  return tokenizer(examples['text'], truncation=True, padding='max_length')
dataset = dataset.map(encode, batched=True)
dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])
```
 Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`
```python
dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)
next(iter(dataloader))
```
Hope this helps