Best selection of verses (golden path) to jump start fine tuning
Open
Nobody has claimed this yet.
needs to be revisited
research
- Dominant language
- Python
- Stars
- 39
- Forks
- 7
- Avg merge
- 1d 9h
- Merged PRs (30d)
- 5
Description
How to figure out the best selection of verses to help jump start the NLLB model for best translations?
- Baseline:
- Use Matthew to train
- Bleu score off of Mark, John, Genesis and Jonah
- Algorithm 1: Random selection of verses
- Make all Bible verses into vectors
- Start with a selection of "really nice" verses that are helpful for people groups
- Extend to a full set equal to the number of verses in Matthew (though vector dissimilarity? What other mechanisms?)
- Train and get Bleu score from the same books
- Algorithm 2: Rapid word collection with XRI's sentence generation:
- What does this entail? How much and what type of corpora is needed?
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are named. Start by defining the Matthew training baseline and the Mark, John, Genesis, and Jonah BLEU evaluation, then compare the proposed verse-selection and XRI sentence-generation approaches. Done means documenting a reproducible selection method and measured comparison.
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
- 20/100