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Releases: IntelLabs/nlp-architect
Releases · IntelLabs/nlp-architect
v0.5.5.1
Updated ABSA Solution + Auto documentation build
v0.5.5 Update build_docs.yml
Updated external links
v0.5.4.1 Updated docs links
ID-CNN/LSTM model improvements
- 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
Misc. library maintenance + fixes
v0.5.3 release 0.5.3
Pseudo-labeling for token classification, new Transformer heads and updated CLI/Docs
v0.5.2 Update setup.py
Bug fixes, tweaks and documentation update
v0.5.1 update version
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
Release v0.4
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
Release v0.3.1
Improvements
- Installation process refactored and supports
pip install nlp-architect
(much more simple) - Moved
server
andsolutions
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
tonlp_architect server