Added two pre-trained models and one new fine-tuning class

This release comprise the following improvements and updates:

• added two new pre-trained models from Google: bert-large-cased and bert-base-multilingual-cased,
• added a model that can be fine-tuned for token-level classification: BertForTokenClassification,
• added tests for every model class, with and without labels,
• fixed tokenizer loading function BertTokenizer.from_pretrained() when loading from a directory containing a pretrained model,
• fixed typos in model docstrings and completed the docstrings,
• improved examples (added do_lower_caseargument).

Small improvements and a few bug fixes.

Improvement:

• Added a cache_dir option to from_pretrained() function to select a specific path to download and cache the pre-trained model weights. Useful for distributed training (see readme) (fix issue #44).

Bug fixes in model training and tokenizer loading:

• Fixed error in CrossEntropyLoss reshaping (issue #55).
• Fixed unicode error in vocabulary loading (issue #52).

Bug fixes in examples:

• Fix weight decay in examples (previously bias and layer norm weights were also decayed due to an erroneous check in training loop).
• Fix fp16 grad norm is None error in examples (issue #43).

Updated readme and docstrings

First release

This is the first release of pytorch_pretrained_bert.