The input training data files (multiple files in glob format).
Very often Corpus comes in [split files (book-large-p1.txt, book-large-p2.txt)](https://huggingface.co/datasets/bookcorpus); Also splitting large files to smaller files can prevent [language_modeling's tokenizer going out of memory](https://github.com/huggingface/transformers/blob/6303b5a7185fba43830db0cbb06c61861f57ddff/src/transformers/data/datasets/language_modeling.py#L67) in environment like Colab that does not have swap memory and limited to Standard (12Gb) or High RAM (25Gb) instances.

To avoid making any assumption and prematurely truncate the file to avoid such error, we add support to concatenate training data on Dataset level. User can split files to multiple 512Mb, in that case language_modeling's tokenizer would be less to go out of memory.

In addition, it would even further enhance the memory limitation if we keep [pytorch tensor in memory](https://github.com/huggingface/transformers/blob/6303b5a7185fba43830db0cbb06c61861f57ddff/src/transformers/data/datasets/language_modeling.py#L88) instead of python list. We leave this in future work.

@LysandreJik @sgugger 