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 installingdatasets.
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
# 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:
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.