Datasets:
Tasks:
Question Answering
Modalities:
Image
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
License:
| #!/usr/bin/env python3 | |
| """Validate the local general science release before upload.""" | |
| from __future__ import annotations | |
| import json | |
| import re | |
| import sys | |
| from collections import Counter | |
| from pathlib import Path | |
| from typing import Any | |
| RELEASE_DIR = Path(__file__).resolve().parents[1] | |
| TRAIN_PATH = RELEASE_DIR / "train.jsonl" | |
| TEST_PATH = RELEASE_DIR / "test.jsonl" | |
| def normalize_for_key(value: Any) -> str: | |
| text = "" if value is None else str(value).strip().lower() | |
| text = re.sub(r"\s+", " ", text) | |
| text = re.sub(r"[^\w\s]+", "", text) | |
| return text.strip() | |
| def dedup_key(example: dict[str, Any]) -> str: | |
| choices = sorted(normalize_for_key(choice["text"]) for choice in example["choices"]) | |
| return " || ".join([normalize_for_key(example["question"]), *choices, normalize_for_key(example["answer_text"])]) | |
| def load_jsonl(path: Path) -> list[dict[str, Any]]: | |
| rows: list[dict[str, Any]] = [] | |
| with path.open(encoding="utf-8") as handle: | |
| for line_number, line in enumerate(handle, 1): | |
| try: | |
| rows.append(json.loads(line)) | |
| except json.JSONDecodeError as exc: | |
| raise ValueError(f"{path}:{line_number} invalid JSON: {exc}") from exc | |
| return rows | |
| def validate_example(example: dict[str, Any], expected_split: str) -> None: | |
| required = { | |
| "id", | |
| "dataset", | |
| "subset", | |
| "split", | |
| "task_type", | |
| "modality", | |
| "question", | |
| "image", | |
| "choices", | |
| "answer_label", | |
| "answer_text", | |
| "support", | |
| "source_meta", | |
| } | |
| missing = required - set(example) | |
| if missing: | |
| raise ValueError(f"{example.get('id')} missing required fields: {sorted(missing)}") | |
| if example["split"] != expected_split: | |
| raise ValueError(f"{example['id']} split={example['split']} expected {expected_split}") | |
| if example["task_type"] != "multiple_choice_science_qa": | |
| raise ValueError(f"{example['id']} unexpected task_type") | |
| if not isinstance(example["choices"], list) or len(example["choices"]) < 4: | |
| raise ValueError(f"{example['id']} has fewer than four choices") | |
| labels = [choice.get("label") for choice in example["choices"]] | |
| if labels != [chr(ord("A") + idx) for idx in range(len(labels))]: | |
| raise ValueError(f"{example['id']} labels are not contiguous from A") | |
| if example["answer_label"] not in set(labels): | |
| raise ValueError(f"{example['id']} answer label not in choices") | |
| if example["modality"] == "image_text": | |
| image = example.get("image") | |
| if not image or not image.get("path"): | |
| raise ValueError(f"{example['id']} image_text example has no image path") | |
| image_path = RELEASE_DIR / image["path"] | |
| if not image_path.exists(): | |
| raise ValueError(f"{example['id']} image path missing: {image_path}") | |
| elif example["modality"] == "text": | |
| if example.get("image") is not None: | |
| raise ValueError(f"{example['id']} text example should have image=null") | |
| else: | |
| raise ValueError(f"{example['id']} unexpected modality {example['modality']}") | |
| def main() -> int: | |
| train = load_jsonl(TRAIN_PATH) | |
| test = load_jsonl(TEST_PATH) | |
| ids = [example["id"] for example in train + test] | |
| duplicated_ids = [item for item, count in Counter(ids).items() if count > 1] | |
| if duplicated_ids: | |
| raise ValueError(f"Duplicated ids: {duplicated_ids[:10]}") | |
| for example in train: | |
| validate_example(example, "train") | |
| for example in test: | |
| validate_example(example, "test") | |
| train_keys = {dedup_key(example) for example in train} | |
| test_keys = {dedup_key(example) for example in test} | |
| overlap = train_keys & test_keys | |
| if overlap: | |
| raise ValueError(f"Train/test normalized overlap found: {len(overlap)}") | |
| all_examples = train + test | |
| dataset_counts = Counter((example["dataset"], example["subset"]) for example in all_examples) | |
| modality_counts = Counter(example["modality"] for example in all_examples) | |
| image_count = sum(1 for example in all_examples if example.get("image")) | |
| print(f"train={len(train)}") | |
| print(f"test={len(test)}") | |
| print(f"total={len(all_examples)}") | |
| print(f"datasets={dict(dataset_counts)}") | |
| print(f"modalities={dict(modality_counts)}") | |
| print(f"image_examples={image_count}") | |
| print("validation=ok") | |
| return 0 | |
| if __name__ == "__main__": | |
| try: | |
| raise SystemExit(main()) | |
| except Exception as exc: | |
| print(f"Error: {exc}", file=sys.stderr) | |
| raise SystemExit(1) from exc | |