Dataset Curation

Filter a dataset before training on it: gate rows on quality with a judge, collapse near-duplicates, and drop rows that overlap your eval set. These are the three filters post-training pipelines are actually judged by. Only the quality gate uses an LLM - dedup and decontamination are deliberately LLM-free, pure-stdlib math, because that is how they run in production and because the numbers they print should be exactly reproducible.

Files

• basic.py - LLM judge quality gate over JSONL. Scores each (instruction, response) row 1-5 on clarity, factual correctness, and self-containedness (temperature-0 judge); keeps rows scoring >= 4 and writes them out with score and reason attached as provenance. Reads the committed fixture data/sample_rows.jsonl. The gate expects {"instruction", "response"} rows; to point input_path at another generator's output, map its fields into that shape first (_20_instruction_generation/ emits instructions without responses, and _21_rejection_sampling/ rows use prompt/reasoning keys).
• dedup.py - no LLM. MinHash near-duplicate detection in pure stdlib: word 3-gram shingles, 64 keyed blake2b hash functions, estimated Jaccard >= 0.7 clustered with union-find, first row per cluster kept. Fully deterministic across runs. Catches verbatim copies, light edits, and close paraphrases; heavy rewording needs embedding-based dedup.
• decontamination.py - no LLM. 13-gram overlap decontamination against data/benchmark_sample.jsonl (an invented fixture, not a real benchmark). Flags a planted verbatim copy of a benchmark question and honestly reports the planted paraphrase it cannot catch - exact n-gram overlap misses paraphrase contamination by construction.

Example rows from basic.py output (kept rows carry their gate provenance):

{"instruction": "Convert 25 degrees Celsius to Fahrenheit and show the formula.", "response": "Using F = C * 9/5 + 32: F = 25 * 9/5 + 32 = 45 + 32 = 77. So 25 degrees Celsius is 77 degrees Fahrenheit.", "score": 5, "reason": "The response is clear, factually correct, and self-contained."}
{"instruction": "Explain what HTTP status code 404 means.", "response": "HTTP 404 Not Found means the server understood the request but could not find the requested resource at that URL. It indicates a client-side addressing problem (bad link or mistyped path), not a server failure; server failures use 5xx codes instead.", "score": 5, "reason": "The response is clear, factually correct, and self-contained."}

When to use

When you have a corpus and need to decide which rows deserve to be trained on. This folder is corpus-level curation: whole rows are kept or dropped. For label-level review - checking and fixing individual annotations - use _18_quality_review/. For the judging primitive itself, see _17_llm_as_judge/.

Typical position in a pipeline: generate candidates with _20_instruction_generation/ or _21_rejection_sampling/, then curate here - quality gate, then dedup, then decontaminate against your eval sets.

Run

python cookbook/data_labeling/_22_dataset_curation/basic.py
python cookbook/data_labeling/_22_dataset_curation/dedup.py
python cookbook/data_labeling/_22_dataset_curation/decontamination.py

Requires GOOGLE_API_KEY (basic.py only; dedup.py and decontamination.py make no API calls).