0.6.0a1

Preprocessing of code inputs have changed, and code tokens are no longer treated specially. Instead, they are treated as normal words and preprocessed using BPE. This allows using any Huggingface tokenizer without changes. Tasks can still define certain tokens that should be treated as special tokens. These are either added as new tokens, or further preprocessed into non-ambiguous sequences of words.
Old models (MQAN and baselines) were removed. The GloVe vectors and other non-contextual word embeddings were also removed. Old training options that were ineffective or unused were removed.
The internals of the library have been refactored to simplify development allow using any Huggingface Seq2Seq or MLM model. As a result, the name of the models have changed: Seq2Seq is now TransformerLSTM and Bart is now TransformerSeq2Seq. Command-line flags changed as well.

NOTE: due to the change in model names and commnd-line flags, this release is not backward compatible with models trained with genienlp <= 0.5.0

0.5.0

Paraphrasing and training was made much faster, with improved GPU usage and by removing redundant tokenization in the hot-paraphrase path [#37, #38, #47].
The transformers library was updated to 4.0; PyTorch dependency increased to 1.6 [#44, #59, #62].
New models: BART, mBART, mT5. As part of this work, the model code was refactored to be more consistent with Huggingface models and generation code [#46, #62].
Paraphrasing scripts are now proper subcommands of genienlp [#53].
It is now possible to fine-tune MBart and Marian models for neural machine translation and sentence denoising [#54].
genienlp server can now operate in batch mode, improving GPU utilization [#58].
Misc bug and documentation fixes [#39, #40, #41, #43, #48, #55, #58].

Added the ability to run paraphrasing in FP-16 mixed precision mode.
The dependency on matplotlib and seaborn (used to produce plots for analysis) is now optional [#36].

Please see the development releases below for the full list of features in this release.

Fixed handling of CJK characters and combining characters in BERT and XLM tokenizers [#34].

The paraphrase generation code was extended and can now use BART instead of GPT2. It also now has the ability to run as a translation task as well (using the Marian models) [#26, #27, #29, #31].
Added the ability to override the context and the question used as input to the model [#23].
MultiGPU training was tested and fixed [#25].
Completed support for beam search, including the ability to return multiple results for a given input [#30].
Misc bug fixes [#32].

New option: sentence batching. Multiple sentences with related properties can be batched together in microbatches within a larger minibatch [#14, #11].
Added option to append context and question in a single model input [#18, #20, #22].
Updated Transformers dependency to 2.9, and fixed compatibility with newer versions [#18, #24].

Please see the development releases below for the full list of features in this release.

New features:

Add new tasks for Almond: almond_dialogue_nlu, almond_dialogue_nlg, almond_dialogue_policy
Added a new encoder, "Coattention", which encodes the context and question separately, then coattends and applies a BiLSTM layer.
For Coattention and Identity encoder, it is now possible to specify the context and question embeddings separately.
Embeddings in context, question and answer can now be untied, by suffixing the name with '@' followed by an unique identifier (e.g. bert-base-uncased@0 and bert-base-uncased@1).
Added an option to pretrain the context encoder, using MLM objective.
Added beam search.
New embedding option: XLM-R (XLM trained with Roberta).
New task: paraphrasing with GPT2. This is not fully integrated with the other tasks yet, but it will in the future.
New command "genienlp export" can be used to save a trained model for inference.

Incompatible changes: