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41e2b8e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # 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.
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