GitLab

Add example for fine tuning BERT language model

add exemplary training data. update to nvidia apex. refactor 'item -> line in doc' mapping. add warning for unknown word.

raises value error for bert tokenizer for long sequences

Use entry-points instead of scripts

Add license to source distribution

The recommended approach to create launch scripts is to use entry_points
and console_scripts.

xref: https://packaging.python.org/guides/distributing-packages-using-setuptools/#scripts

Adds an example for loading a pre-trained BERT model and fine tune it as a language model (masked tokens & nextSentence) on your target corpus.

Minor README fix 

Fourth release

Change to use apex for better fp16 and multi-gpu support

fix compatibility with python 3.5.2

BertForMultipleChoice and Swag dataset example.