Fix mlm
The way the texts is being split up into blocks right now

```
tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))

for i in range(0, len(tokenized_text) - block_size + 1, block_size):  # Truncate in block of block_size
self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size]))
```

Results in double [CLS] tokens at the beginning, since special tokens are being added at `tokenizer.convert_tokens_to_ids` And at `tokenizer.build_inputs_with_special_tokens`. Somehow, double [SEP] tokens are not occuring. 

The following eliminates the double [CLS] token. 

```
tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)[1:-2])

for i in range(0, len(tokenized_text) - block_size + 1, block_size):  # Truncate in block of block_size
self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size]))
```
Couldn't we achieve the same result by specifying `add_special_tokens=False` instead of that? This isn't robust to different models, as GPT-2 (which doesn't have special tokens) would get some tokens removed.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>Closing this as `run_language_modeling.py` is now based on the trainer. Thanks for your contribution!!