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