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license: mit
tags: [code, assertion-completion, repository-level]
---
# code2lora-data-smartcap — smart-capped GRU training QnAs
Smart-capped, file/function-balanced per-commit QnA dataset used to train
**Code2LoRA-GRU** in the paper. The capping reduces the heavy-tailed
training distribution by:
1. Dropping trivial targets (`len(target.strip()) < 4`, bare `")"`, etc.).
2. Round-robin sampling within each commit across (test_file, test_function)
groups so file/function diversity is preserved.
3. Capping each commit at `max_per_commit = 8` and each
(commit, test_file) at `max_per_file = 4`.
**Only `in_repo_split == "train"` rows are capped.** Val and test rows
pass through verbatim, so evaluation suites remain comparable to the
uncapped dataset.
## Counts
| File | Rows | Notes |
|---|---:|---|
| `qna/cr_train.parquet` | 1,187,359 | 215,129 capped train + 437,227 val + 535,003 test (val/test untouched) |
| `qna/cr_val.parquet` | 641,848 | full uncapped |
| `qna/cr_test.parquet` | 476,455 | full uncapped |
## Split semantics
Two independent split axes:
* `cross_repo_split ∈ {train, cr_val, cr_test}` -- which **repositories** the model has seen.
* `in_repo_split ∈ {train, val, test}` -- chronological 80/10/10 split of each repo's **commits**.
The four evaluation suites used in the paper map onto these axes as follows:
| Paper suite | File | Selection |
|---|---|---|
| Train (loss) | `qna/cr_train.parquet` | `in_repo_split == "train"` |
| IR val | `qna/cr_train.parquet` | `in_repo_split == "val"` |
| IR test | `qna/cr_train.parquet` | `in_repo_split == "test"` |
| CR val | `qna/cr_val.parquet` | all rows |
| CR test | `qna/cr_test.parquet` | all rows |
All evaluation suites use the **full uncapped** assertion set. Only the in-repo train loss path is smart-capped (see `code2lora/code2lora-data-smartcap`).
## Companion datasets
* `code2lora/code2lora-data-commits` per-commit metadata + diff embeddings
* `code2lora/code2lora-data-snapshots` direct-projection (static) variant
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