| CODE DATASET |
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| 5,233 unique mechanical parts, with isometric images, text instructions, target drawing programs, and reference DXF drawings. |
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| SPLITS |
| sft_train.jsonl: 3,929 training samples. |
| sft_val.jsonl: all 256 original validation samples. |
| sft_val_effective254.jsonl: the existing formal 254-sample validation subset, not additional data. |
| test.jsonl: all 1,048 original test samples; 1,045 are eligible under the frozen GT-only mask. |
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| ASSETS |
| Extract images.zip and reference_dxf.zip into this CODE directory. Each ZIP contains its named subdirectory; JSONL image paths then resolve without edits. |
| images/: 5,233 PNG input images. reference_dxf/: 5,233 existing DXF ground-truth drawings. |
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| METADATA |
| sample_index.csv gives actual SFT splits, original source splits, categories, historical tiers, asset paths and exclusion flags. |
| labels.csv is preserved byte-for-byte from the source and labels the combined 4,185 train/validation pool as train. |
| evaluation_exclusions.json preserves the established validation/test masks; no original samples have been removed. |
| asset_manifest.jsonl gives byte counts and SHA-256 for every archived asset. SHA256SUMS verifies uploaded files. |
| dataset_manifest.json records counts, source versions and unchanged source-file hashes. |
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| SCOPE |
| The original dataset files contain image/text inputs and assistant Python/ezdxf programs as supervision targets, with no construction/training scripts, model weights, credentials, or experiment logs. A separate evaluation toolkit is now provided in evaluation/. |
| Training/validation target programs use the final compact representation; test targets retain their original representation, matching the existing experiment data. |
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| EVALUATION TOOLKIT |
| See evaluation/README.txt for setup, reference data and commands. The toolkit scores existing DXF files using the frozen v5.2-rc5k-final metrics, and includes fixed test1045, paper1041 and validation254 populations. It does not execute generated Python or call models. |
| The dataset manifest and top-level SHA256SUMS describe the data files; the evaluation toolkit has its own PROVENANCE.json and SHA256SUMS. Dataset records and assets are unchanged. |
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