| # `data/preprocessed/sft_dream_py_ast/` — AST SFT caches (many runs) | |
| Sibling to `sft_dream_py/`. Each **subfolder** is one **AST-related preprocessing** pipeline (mask policy, depth, random depth, SVEN subsets, etc.), usually containing `train.pt`, `val.pt`, `meta.json` (verify on disk). | |
| ## Subfolder name patterns (examples) | |
| | Pattern | Meaning (high level) | | |
| |---------|----------------------| | |
| | `ast_depth_masking*`, `ast_depth_masking_both`, `ast_depth_masking_min` | Mask variants by **AST depth** | | |
| | `ast_depth_masking_invert*` | Inverted or ablation depth masks | | |
| | `random_depth_masking*` | **Random depth** masks | | |
| | `ast_node_masking_*` | **Node-level** masks | | |
| | `ast_random_m_*` | Random-AST `m` experiment series | | |
| | `*_sven_1_1024`, `*_sven_bigvul*` | **SVEN / Big-Vul** scale or subset | | |
| | `ast_depth_label_learning` | Preprocessing with **depth label learning** | | |
| We **do not** add a README in every leaf; for reproduction use that folder’s `meta.json` and the YAML `dataset.preprocessed_dir` from the run. | |