LLM JSONL 数据集验证器

Validate JSONL datasets for LLM evaluation, fine-tuning, preference, extraction, or chat tasks. Use to detect malformed lines, schema drift, duplicates, leakage, role errors, empty content, label imbalance, or train/test contamination.

LLM JSONL Dataset Validator

Inspect data locally when possible. Do not upload private datasets to third parties without authorization.

Workflow

Identify the intended record schema and task type before judging fields.
Parse every non-empty line independently and report line-numbered JSON errors.
Validate required keys, types, allowed roles, content, labels, IDs, and metadata.
Detect exact and normalized duplicates plus conflicting labels for the same input.
Check empty, extremely long, malformed Unicode, control characters, and suspicious secret/PII patterns.
Measure label, language, length, domain, and source distributions.
Compare split fingerprints to detect train/test leakage and near duplicates.

Output

Return counts, severity-ranked issues, representative line numbers, distribution tables, leakage risks, and safe repair recommendations. Never rewrite the source dataset unless explicitly requested; preserve an audit trail when fixing.