Research methods of adding tags during training and using them during translation.
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- Dominant language
- Python
- Stars
- 39
- Forks
- 7
- Avg merge
- 1d 9h
- Merged PRs (30d)
- 5
Description
We would like to tag different genres of Scripture texts during training, and include tags with the source text during inferencing. The hope is that this will improve the drafts produced by the model.
We would need a flexible method of tagging verses and including the tags as tokens.
Here are a few ideas we could test. They are listed here to give an idea of the kinds of tagging support that might be useful.
Tag each verse with the book it is from.
Tag each verse with the name of the author.
Tag each verse with a genre.
Tag each verse with the book, author, and genre.
Tag verses with a language family or dialect.
Tags should be optional, training and inferencing should continue for untagged verses.
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start by mapping where training data is prepared and where source text enters inferencing in the Python pipelines. Evaluate optional tags for book, author, genre, language family, and dialect, and define how tagged and untagged verses should behave. Done means a documented approach is selected for testing without disrupting untagged training or inference.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100