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| """Inspect a pinned Hub dataset sample and persist a content-free quality report.""" |
|
|
| from __future__ import annotations |
|
|
| from collections import Counter |
| from datetime import UTC, datetime |
| import hashlib |
| import json |
| import os |
| import re |
| from typing import Any |
|
|
| from datasets import load_dataset |
| from huggingface_hub import HfApi |
|
|
| SECRET = re.compile(r"(?:\bsk-[A-Za-z0-9_-]{12,}|\bhf_[A-Za-z0-9]{12,})") |
|
|
|
|
| def _shape(value: Any) -> str: |
| if value is None: |
| return "null" |
| if isinstance(value, list): |
| return "list" |
| if isinstance(value, dict): |
| return "object" |
| return type(value).__name__ |
|
|
|
|
| def main() -> None: |
| repo = os.environ["SOURCE_REPO"] |
| revision = os.environ["SOURCE_REVISION"] |
| config = os.getenv("SOURCE_CONFIG") or None |
| split = os.getenv("SOURCE_SPLIT", "train") |
| sample_rows = int(os.getenv("SAMPLE_ROWS", "1000")) |
| dataset = load_dataset(repo, config, split=split, revision=revision, streaming=True) |
|
|
| field_presence: Counter[str] = Counter() |
| field_shapes: dict[str, Counter[str]] = {} |
| text_lengths: dict[str, list[int]] = {} |
| suspected_secrets = 0 |
| duplicate_fingerprints = 0 |
| seen: set[str] = set() |
| count = 0 |
| for row in dataset.take(sample_rows): |
| count += 1 |
| canonical = json.dumps(row, sort_keys=True, ensure_ascii=False, default=str) |
| fingerprint = hashlib.sha256(canonical.encode()).hexdigest() |
| duplicate_fingerprints += fingerprint in seen |
| seen.add(fingerprint) |
| suspected_secrets += bool(SECRET.search(canonical)) |
| for key, value in row.items(): |
| field_presence[key] += value is not None |
| field_shapes.setdefault(key, Counter())[_shape(value)] += 1 |
| if isinstance(value, str): |
| text_lengths.setdefault(key, []).append(len(value)) |
|
|
| report = { |
| "schema_version": "1.0.0", |
| "created_at": datetime.now(UTC).isoformat(), |
| "source": {"repo_id": repo, "revision": revision, "config": config, "split": split}, |
| "sample_count": count, |
| "fields": { |
| key: { |
| "present": field_presence[key], |
| "shapes": dict(sorted(field_shapes[key].items())), |
| "text_length": ( |
| { |
| "min": min(text_lengths[key]), |
| "max": max(text_lengths[key]), |
| "mean": sum(text_lengths[key]) / len(text_lengths[key]), |
| } |
| if key in text_lengths |
| else None |
| ), |
| } |
| for key in sorted(field_presence) |
| }, |
| "findings": { |
| "suspected_secret_rows": suspected_secrets, |
| "duplicate_sample_rows": duplicate_fingerprints, |
| }, |
| } |
| payload = json.dumps(report, sort_keys=True, indent=2) + "\n" |
| print(payload) |
| target = os.getenv("REPORT_REPO") |
| if target: |
| HfApi().create_repo(target, repo_type="dataset", private=True, exist_ok=True) |
| HfApi().upload_file( |
| path_or_fileobj=payload.encode(), |
| path_in_repo=os.getenv("REPORT_PATH", "probes/latest.json"), |
| repo_id=target, |
| repo_type="dataset", |
| commit_message=f"Add dataset probe for {repo}@{revision[:12]}", |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
