| # Preview samples |
|
|
| **Quick human-readable look at the dataset in its original schema.** |
|
|
| > Parquet under `data/` is the canonical format for benchmarking and Hugging Face loading. This folder exists purely so reviewers and other researchers can skim a handful of real rows without writing any code or installing `datasets`. |
|
|
| ## Contents |
|
|
| | File | Count | Notes | |
| |---|---|---| |
| | `preview.jsonl` | **20** rows | Stratified sample from `data/canonical.parquet`, in the source-native JSON-per-line schema. Fields are identical to those documented in the root `README.md`. | |
| | `vce_plus_preview.jsonl` | **20** rows | VCE+ 7-dimensional structured visual-evidence extractions **aligned by `instance_id`** to `preview.jsonl`, illustrating the auxiliary schema used by text-only baselines. | |
| | `images/` | 31 files | Every image referenced by `preview.jsonl.images[*].local_path`. Filenames follow `issue_<issue_id>_<idx>.{png,jpg,...}`. | |
| |
| ## Quick inspection |
| |
| ```bash |
| # Pretty-print the first canonical row |
| head -1 preview.jsonl | python3 -m json.tool |
| |
| # All gold files across the 20 samples |
| python3 -c "import json; [print(r['instance_id'], '→', r['edit_files']) \ |
| for r in map(json.loads, open('preview.jsonl'))]" |
| |
| # VCE+ extraction for the same instance — side-by-side schema comparison |
| python3 - <<'PY' |
| import json |
| canon = {json.loads(l)["instance_id"]: json.loads(l) for l in open("preview.jsonl")} |
| vce = {json.loads(l)["instance_id"]: json.loads(l) for l in open("vce_plus_preview.jsonl")} |
| iid = next(iter(canon)) |
| print("instance:", iid) |
| print(" issue title :", canon[iid]["issue_title"]) |
| print(" image category:", canon[iid]["image_category"]) |
| print(" VCE+ record[0]:", json.dumps(vce[iid]["records"][0], indent=2, ensure_ascii=False)) |
| PY |
| |
| # Open the image for the first sample |
| python3 -c "import json; r=json.loads(open('preview.jsonl').readline()); \ |
| print('first image:', r['images'][0]['local_path'])" |
| ``` |
| |
| ## Full dataset |
| |
| To work with all 652 instances, all 1050 images (embedded as bytes), and both evaluation granularities, use the Parquet configs from the root: |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("<hub-id>/MM-IssueLoc-Bench", name="canonical", split="test") |
| ``` |
| |
| See the root `README.md` and `examples/load_dataset.py` for full workflows. |
| |
| |
