Create train/val/test splits
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 38/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- data, machine-learning
Research direction
Locate the normalized dataset input and the existing data-processing entry point, then review how commit types, repository identity, and scope frequency are represented. Define and implement reproducible train, validation, and test outputs under data/splits/; done means the three JSONL files and splits_metadata.json exist, satisfy the stated stratification and leakage constraints, and include documented statistics.
Written by the indexing model from the issue text.
Description
Summary
Split the normalized dataset into train, validation, and test sets with proper stratification.
Success Criteria
- Split ratios defined (e.g., 80/10/10)
- Stratification by commit type
- No data leakage (same repo shouldn't span splits)
- Splits saved to data/splits/
- Split statistics documented
- Reproducible with fixed random seed
Stratification Strategy
- Balance commit types across splits
- Consider stratifying by scope frequency
- Ensure test set has good coverage of rare types
Output Files
```
data/splits/
train.jsonl
val.jsonl
test.jsonl
splits_metadata.json
```
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Contributor guide
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.
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