Adaptnlp

Breaking Changes:

- New versions of AdaptNLP will require a minimum torch version of 1.7, and flair of 0.9 (currently we install via git until 0.9/0.81 is released)

New Features

- Complete conversion to the [nbdev](nbdev.fast.ai) library format and actions

- Complete revamp of the [documentation](novetta.github.io/adaptnlp)

- Inference API entirely relies on [fastai_minima](https://github.com/muellerzr/fastai_minima) and is now built on [fastai](https://github.com/fastai/fastai)'s [Callback System](https://docs.fast.ai/callback.core#Callback)

- Integration with [fastcore](fastcore.fast.ai) to simplify logic

- [HuggingFace](https://novetta.github.io/adaptnlp/model_hub.html#HFModelHub) and [Flair](https://novetta.github.io/adaptnlp/model_hub.html#FlairModelHub) [ModelHubs](https://novetta.github.io/adaptnlp/model_hub.html), an easier API to interact, search, and download HF and Flair models. Uses [huggingface_hub](https://github.com/huggingface/huggingface_hub) as a backend. Has logged every single Flair model, including those not in the HuggingFace API

Bugs Squashed

- Fix accessing bart-large-cnn ([110](https://github.com/Novetta/adaptnlp/issues/110))
- Fix SAVE_STATE_WARNING ([114](https://github.com/Novetta/adaptnlp/issues/114))

Official AdaptNLP Docker Images updated
- Using NVIDIA NGC Container Registry Cuda base images 101
- All images should be deployable via. Kubeflow Jupyter Servers
- Cleaner python virtualvenv setup 101
- Official readme can be found at https://github.com/Novetta/adaptnlp/blob/master/docker/README.md

Minor Bug Fixes
- Fix token tagging REST application type check 92
- Semantic fixes in readme 94
- Standalone microservice REST application images 93
- Python 3.7+ is now an official requirement 97

Updated to nlp 0.4 -> datasets 1.0+ and multi-label training for sequence classification fixes.

`EasySequenceClassifier.train()` Updates
- Integrates `datasets.Dataset` now
- Swapped order of formatting and label column renaming due to labels not showing up from torch data batches 87

Tutorials and Documentation
- Documentation and sequence classification tutorials have been updated to address nlp->datasets name change
- Broken links also updated


ODSC Europe Workshop 2020: Notebooks and Colab
- ODSC Europe 2020 workshop materials now available in repository "/tutorials/Workshop"
- Easy to run notebooks and colab links aligned with the tutorials are available

Updated to transformers 3+, nlp 0.4+, flair 0.6+, pandas 1+

New Features!

New and "easier" training framework with easy modules: `EasySequenceClassifier.train()` and `EasySequenceClassifier.evaluate()`
- Integrates `nlp.Dataset` and `transformers.Trainer` for a streamlined training workflow
- Tutorials, notebooks, and colab links available
- Sequence Classification task has been implemented, other NLP tasks are in the works
- `SequenceClassifierTrainer` is still available, but will be transitioned into the `EasySequenceClassifier` and deprecated

New and "easier" `LMFineTuner`
- Integrates `transformers.Trainer` for a streamlined training workflow
- Older `LMFineTuner` is still available as `LMFineTunerManual`, but will be deprecated in later releases
- Tutorials, notebooks, and colab links available

`EasyTextGenerator`
- New module for text generation. GPT models are currently supported, other models may work but still experimental
- Tutorials, notebooks, and colab links available

Tutorials and Documentation
- Documentation has been edited and updated to include additional features like the change in training frameworks and fine-tuning
- The sequence classification tutorial is a good indicator of the direction we are going with the training and fine-tuning framework


Notebooks and Colab
- Easy to run notebooks and colab links aligned with the tutorials are available

Bug fixes
- Minor bug and implementation error fixes from flair upgrades

Split dev requirements 29 66
Pinned torch 70

Updated to Transformers 2.8.0 which now includes the ELECTRA language model

`EasySummarizer` and `EasyTranslator` Bug Fix 63
- Address mini batch output format issue for language model heads for the summarization and translation task

Tutorials and Workshop 64
- Add the ODSC Timeline Generator notebooks along with colab links
- Small touch ups in tutorial notebooks

Documentation
- Address missing `model_name_or_path` param in some easy modules