Hugging Face Transformers: v2.0.0 - TF 2.0/PyTorch interoperability, improved tokenizers, improved torchscript support

## Name change: welcome 🤗 Transformers Following the extension to TensorFlow 2.0, `pytorch-transformers` => `transformers` Install with `pip install transformers` Also, note that PyTorch is **no longer in the requirements so don't forget to install TensorFlow 2.0 and/or PyTorch** to be able to use (and load) the models. ## TensorFlow 2.0 - PyTorch All the PyTorch `nn.Module` classes now have their counterpart in TensorFlow 2.0 as `tf.keras.Model` classes. TensorFlow 2.0 classes have the same name as their PyTorch counterparts prefixed with `TF`. The interoperability between TensorFlow and PyTorch is actually **a lot deeper** than what is usually meant when talking about libraries with multiple backends: - each model (not just the static computation graph) can be seamlessly moved from one framework to the other during the lifetime of the model for training/evaluation/usage (`from_pretrained` can load weights saved from models saved in one or the other framework), - an example is given in the quick-tour on TF 2.0 and PyTorch in the readme in which a model is trained using keras.fit before being opened in PyTorch for quick debugging/inspection. ### Remaining unsupported operations in TF 2.0 (to be added later): - resizing input embeddings to add new tokens - pruning model heads ### TPU support Training on TPU using free TPUs provided in the TensorFlow Research Cloud (TFRC) program is possible but requires to implement a custom training loop (not possible with keras.fit at the moment). We will add an example of such a custom training loop soon. ## Improved tokenizers Tokenizers have been improved to provide extended encoding methods `encoding_plus` and additional arguments to `encoding`. Please refer to the doc for detailed usage of the new options. ## Breaking changes ### Positional order of some model keywords inputs changed (better TorchScript support) To be able to better use Torchscript both on CPU and GPUs (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed. If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any breaking change. If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you should double-check the exact order of input arguments. ### Dependency requirements have changed PyTorch is no longer in the requirements so don't forget to install TensorFlow 2.0 and/or PyTorch to be able to use (and load) the models. ### Renamed method The method `add_special_tokens_sentence_pair` has been renamed to the more appropriate name `add_special_tokens_sequence_pair`. The same holds true for the method `add_special_tokens_single_sentence` which has been changed to `add_special_tokens_single_sequence`. ## Community additions/bug-fixes/improvements - new German model (@Timoeller) - new script for MultipleChoice training (SWAG, RocStories...) (@erenup) - better fp16 support (@ziliwang and @bryant1410) - fix evaluation in run_lm_finetuning (@SKRohit) - fiw LM finetuning to prevent crashing on assert len(tokens_b)>=1 (@searchivarius) - Various doc and docstring fixes (@sshleifer, @Maxpa1n, @mattolson93, @t080)