• 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

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

Models

• New Aspect Based Sentiment Analysis model and example

Solutions

• New Aspect Based Sentiment Analysis solution

Improvements

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

Improvements

• Installation process refactored and supports pip install nlp-architect (much more simple)
• Moved server and solutions into main package
• Many Cross-doc coreference model fixes and additions
• Updates and fixed lots of documentation files

Demos

• Improved NLP Architect demos with a new design
• Renamed nlp_architect demo to nlp_architect server