dataset-synthesizer-revisor

To audit a JSONL dataset before fine-tuning, analyze the file line by line to detect placeholders, AI apology traces, JSON syntax errors, and content imbalance. This process outputs a Markdown report with anomaly descriptions and remediation recommendations.

Common data quality issues in synthesized fine-tuning datasets include unresolved placeholder tokens like [Preencher...], machine-error remnants or AI-splaining phrases, invalid JSON structures, and content imbalance that can degrade model behavior during training.

Validating JSON syntax errors in a JSONL file involves verifying each line is a standalone valid JSON record with required root keys and correct escaping. This structural validation prevents ingestion failures during ML data curation pipelines.

You can generate a line-by-line audit report for a JSONL training dataset to receive total line counts, problematic line numbers, anomaly descriptions, and precise remediation recommendations. The report is saved as a Markdown file in the original dataset directory.

Dataset auditing works for both synthesized datasets and manual exports used for fine-tuning large language models. It detects generator artifacts in synthesized data and structural errors in manual exports, ensuring both meet production training quality standards.

You should remove AI apology traces from fine-tuning datasets because these machine-error remnants and AI-splaining phrases should not appear in production training data. Leaving them in will degrade model behavior and cause the trained model to replicate unwanted patterns.