New Transformer models, Quantized BERT, Distillation from BERT, Neural Taggers, PyTorch backend, CLI, procedures,

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 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