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This is the transforna package which contains the following modules:
- `train` is the entry point where data preparation, training and results logging is executed.
- `processing` contains all classes used for data augmentation, tokenization and splitting.
- `model` contains the skorch model `skorchWrapper` that wraps the torch model described in model components
- `callbacks` contains the learning rate scheduler, loss function and the metrics used to evaluate the model.
- `score` compute the balanced accuracy of the classification task -major or sub-class- for each of the splits with known labels(train/valid/test).
- `novelty_prediction` contains two novelty metrics; entropy based(obsolete) and Normalized Levenstein Distance, NLD based (current).
- `inference` contains all inference functionalities. check `transforna/scripts/test_inference_api.py` for how-to-use.
A schematic of the TransfoRNA Architecture:
Model evauation image [source](https://medium.com/@sachinsoni600517/model-evaluation-techniques-in-machine-learning-47ae9fb0ad33)
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