LEL-A / LEL-A/EuroInstructProject
DPR Dataset: should we use all negative contexts?
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Description
The number of positive responses and the number of negative responses are exactly balanced at the moment.
Is that useful - or should we use all negative contexts to generate data?
Contributor guide
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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 identified. Start by reviewing how the DPR dataset currently generates and balances positive and negative responses, then determine which sampling strategy should be adopted and how its effect on the resulting data would be evaluated.
Written by the indexing model from the issue text.
Assessment
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100