Nlp-architect

* Improved ID-CNN and LSTM sequence tagger models accuracy
* Added support for BILOU format for tagging tasks
* Updated Transformer token tagging and neural taggers to save best and support BILOU format
* Refactor pseudo/distillation procedures to reproduce results according to our [paper](https://arxiv.org/abs/1910.06294)

General features
* Added PyTorch as a backend
* New `nlp_architect` CLI for training/running/process models and scripts
* New `nlp_architect.procedures` package for creating procedures/scripts for doing scripted work with NLP architect.
* S3 URL caching of pre-trained models
* Many additions/refactors/base classes reflecting new API (WIP)

Transformer models
* Integrated [``pytorch-transformers``](https://github.com/huggingface/pytorch-transformers) library into NLP Architect's model package.
* Created a base transformer class for training transformer-based models
* Added sequence/token classification models

Optimization related models
* Added a base transformer model for training quantized (8bit) BERT models
* Added distillation process from Transformer models into CNN/LSTM based token classification models

NLP related additions
* Added GLUE benchmark tasks
* Added CNN-LSTM and ID-CNN token classification models (pytorch backend)

Documentation
* Updated documentation website to reflect 0.5 release
* Updated website with logo and matching style

Models
- New [Aspect Based Sentiment Analysis](http://nlp_architect.nervanasys.com/absa.html) model and example

Solutions
- New [Aspect Based Sentiment Analysis](http://nlp_architect.nervanasys.com/absa_solution.html) solution

Improvements
- Revised installation process (separate development dependencies)
- Fixed `NERCRF` model bug with TF 1.12
- Revised Documentation