| """Independent label check: deepseek-v4-pro classifies each generated query among the intents; keep only agreeing rows. |
| Also calibrates the verifier on held-out REAL rows so we know its ceiling.""" |
| import argparse, json |
| from concurrent.futures import ThreadPoolExecutor |
| import pandas as pd |
| from llm import chat_json, usage_line |
|
|
| def build_system(intents, examples): |
| listing = "\n".join(f"{k}: {n} | e.g. " + " / ".join(examples[n]) for k, n in enumerate(intents)) |
| return ("You classify messages sent to a retail bank's support chat into exactly one intent. Intents (id: name | examples):\n" |
| f"{listing}\n\nFor each numbered message choose the single best-fitting intent id. Placeholders like {{NAME}} are fake personal details; ignore them. " |
| 'Return JSON {"a": [id, id, ...]} with exactly one id per message, in order.') |
|
|
| def classify(system, texts, model): |
| user = "\n".join(f"{i+1}. {t}" for i, t in enumerate(texts)) |
| a = chat_json(model, system, user, max_tokens=400, temperature=0.0).get("a", []) |
| if len(a) == len(texts): return a |
| if len(texts) == 1: return [-1] |
| h = len(texts) // 2 |
| return classify(system, texts[:h], model) + classify(system, texts[h:], model) |
|
|
| def run(system, df, model, batch, workers): |
| chunks = [df.text.tolist()[i:i + batch] for i in range(0, len(df), batch)] |
| with ThreadPoolExecutor(workers) as ex: res = list(ex.map(lambda c: classify(system, c, model), chunks)) |
| return [x for r in res for x in r] |
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--inp", default="data/synth_raw.jsonl") |
| ap.add_argument("--out", default="data/synth_verified.jsonl") |
| ap.add_argument("--model", default="deepseek-v4-pro") |
| ap.add_argument("--batch", type=int, default=40) |
| ap.add_argument("--workers", type=int, default=8) |
| ap.add_argument("--calibrate", type=int, default=0, help="only score N held-out real rows, then exit") |
| a = ap.parse_args() |
|
|
| pool = pd.read_parquet("data/clean_pool.parquet") |
| intents = sorted(pool.intent.unique()) |
| ex_rows = pool.groupby("intent").sample(2, random_state=0) |
| examples = {i: g.text.tolist() for i, g in ex_rows.groupby("intent")} |
| system = build_system(intents, examples) |
| idx = {n: k for k, n in enumerate(intents)} |
|
|
| if a.calibrate: |
| held = pool.drop(ex_rows.index).sample(a.calibrate, random_state=1) |
| pred = run(system, held, a.model, a.batch, a.workers) |
| acc = (pd.Series(pred).values == held.intent.map(idx).values).mean() |
| print(f"verifier accuracy on {len(held)} held-out REAL rows: {acc:.3f}\n{usage_line()}") |
| return |
|
|
| df = pd.read_json(a.inp, lines=True) |
| df["pred"] = run(system, df, a.model, a.batch, a.workers) |
| df["verified"] = df.pred == df.intent.map(idx) |
| df.to_json(a.out, orient="records", lines=True) |
| print(f"verified {df.verified.mean():.1%} of {len(df)}\n{df.groupby('intent').verified.mean().sort_values().head(8).round(2)}\n{usage_line()}") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
