GECToR 2.3.0

Added a parameter to the embedder to avoid downloading weights from Hugging Face if loading the model from an archive.

GECToR 2.2.0

1. Update CPU and GPU recipes.
2. Minor changes to dependencies.

GECToR 2.1.0

1. Update GECToR dependencies to the latest versions including both GECToR code base and predictor class.
2. Restructure GECToR code base to better support installation in a conda environment.
3. Move data/vocabulary to test_fixtures
4. Create conda packages (gector and gector-cpu) in the ETS public channel.

GECToR 2.0.0

1. Write predictor class that can do multiple applications of forward() in model to replicate gector's iterative error correction.
2. Manually loading the model onto a cuda device should not be happening inside gec_model this is handled by allennlp's predictor base class
3. Modify pretrained gec model archive to use parameters for updated token embedder, token indexer, model etc.
4. Gector doesn't provide a model archive, they just provide the weights file. We should definitely make a model archive file.
5. Make model archive file for use with allennlp's Predictor.from_path() method and allennlp predict command
6. A config.json file will need to be written. These specify all the parameters used during training which we will need to extract from hardcoded values in Gec_model
7. Write unit test verifying that modified gec model archive can be used to do error correction on plaintext sentences.
8. Override json_to_instance behavior in predictor class so that gector's bespoke tokenization module is used.
9. Make the output from the predictor match the output from gec_model isn't accounted for.
   • Not correcting short sequences (<4 tokens),
   • Adding gector's start tokens to the input before correcting and stopping the iterations if no corrections were made for the previous iteration.

GECToR 1.0.0

The original work is done by the Grammarly. Our work in this release have been focusing on the following:

1. Create unit tests on different modules.
2. Create regression tests using various public GEC datasets.
3. Create the Python package and provide instructions on README.