Hugging Face Transformers: CTRL, DistilGPT-2, Pytorch TPU, tokenizer enhancements, guideline requirements

# New model architectures: CTRL, DistilGPT-2 Two new models have been added since release 2.0. - CTRL (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858), by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, Richard Socher. This model has been added to the library by @keskarnitish with the help of @thomwolf. - DistilGPT-2 (from HuggingFace), as the second distilled model after DistilBERT in version 1.2.0. Released alongside the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) # Distillation Several updates have been made to the distillation script, including the possibility to distill GPT-2 and to distill on the SQuAD task. By @VictorSanh. # Pytorch TPU support The `run_glue.py` example script can now run on a Pytorch TPU. # Updates to example scripts Several example scripts have been improved and refactored to use the full potential of the new tokenizer functions: - `run_multiple_choice.py` has been refactored to include `encode_plus` by @julien-c and @erenup - `run_lm_finetuning.py` has been improved with the help of @dennymarcels, @jinoobaek-qz and @LysandreJik - `run_glue.py` has been improved with the help of @brian41005 # QOL enhancements on the tokenizer Enhancements have been made on the tokenizers. Two new methods have been added: `get_special_tokens_mask ` and `truncate_sequences `. The former returns a mask indicating which tokens are special tokens in a token list, and which are tokens from the initial sequences. The latter truncate sequences according to a strategy. Both of those methods are called by the `encode_plus` method, which itself is called by the `encode` method. The `encode_plus` now returns a larger dictionary which holds information about the special tokens, as well as the overflowing tokens. Thanks to @julien-c, @thomwolf, and @LysandreJik for these additions. # New German BERT models - Support for new German BERT models (cased and uncased) from @stefan-it @dbmdz # Breaking changes - The two methods `add_special_tokens_single_sequence` and `add_special_tokens_sequence_pair` have been removed. They have been replaced by the single method `build_inputs_with_special_tokens ` which has a more comprehensible name and manages both sequence singletons and pairs. - The boolean parameter `truncate_first_sequence ` has been removed in tokenizers' `encode` and `encode_plus` methods, being replaced by a strategy in the form of a string: 'longest_first', 'only_second', 'only_first' or 'do_not_truncate' are accepted strategies. - When the `encode` or `encode_plus` methods are called with a specified `max_length`, the sequences will now always be truncated or throw an error if overflowing. # Guidelines and requirements New contributing guidelines have been added, alongside library development requirements by @rlouf, the newest member of the HuggingFace team. # Community additions/bug-fixes/improvements - GLUE Processors have been refactored to handle inputs for all tasks coming from the `tensorflow_datasets`. This work has been done by @agrinh and @philipp-eisen. - The padding_idx is now correctly initialized to 1 in randomly initialized RoBERTa models. @ikuyamada - The documentation CSS has been adapted to work on older browsers. @TimYagan - An addition concerning the management of hidden states has been added to the README by @BramVanroy. - Integration of TF 2.0 models with other Keras modules @thomwolf - Past values can be opted-out @thomwolf