Forward and reverse detokinizing
I am looking for some code examples on how to train with backbone transformers to both forward and reverse or detokenize.The problem being that all my datasets (including validation) are pre-labelled for scoring and I  believe it would help if I made the text more clean and similar to what the my pretrained transformers were trained on (wikipedia text, I believe).   This means removing contractions, among other things.The biggest challenge, of course, is the reverse trip and what techniques can be used to tokenize in such a way that we can appropriately map to and re-generate the original labels.  The text is very messy and requires quite a bit of cleansing.Any pointers would be greatly appreciated!  Cheers
2021-12-26T01:09:30Z
Another approach is using augmentation, eg:What’s in the Dataset object — datasets 1.11.0 documentationThe idea being there is that I train/predict on augmented datasets so that I retain the original.   Is this a typical approach used?Any feedback appreciated