| """ |
| Pass 2: independent re-classification of the guardrail cache. |
| |
| The generator was told which class to write for, so its stated label is a restatement of the |
| instruction, not an observation. Here the same served model classifies each text against the full |
| 12-class taxonomy under a prompt that shares no wording with the generation prompt and never |
| reveals the target. Classes are presented in shuffled order so the answer cannot be read off |
| position. A row is kept only when the independent answer matches. |
| |
| This matters more here than on any earlier corpus. The smoke test produced rows labelled |
| `nonviolent_crime` that were actually a victim asking for help with malware on their own machine, |
| and a `violent_weapons` row that was a request for historical battlefield photographs. Both mention |
| the hazard vocabulary; neither is a harmful request. Only an independent pass catches that. |
| |
| Disagreements are written out, never silently relabelled - they are the ambiguity slice. |
| """ |
| import json |
| import os |
| import random |
| import sys |
| from concurrent.futures import ThreadPoolExecutor |
| from pathlib import Path |
|
|
| from openai import OpenAI |
|
|
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) |
| import guard_common as gc |
|
|
| OUT_DIR = Path(os.environ.get("OUT_DIR", "out_guard")) |
| CACHE = OUT_DIR / "generations.jsonl" |
| VERIFIED = OUT_DIR / "verified.jsonl" |
| WORKERS = int(os.environ.get("WORKERS", 8)) |
|
|
| CLIENT = OpenAI(base_url=os.environ.get("OPENAI_BASE", "http://localhost:8000/v1"), |
| api_key=os.environ.get("OPENAI_KEY", "EMPTY"), |
| timeout=float(os.environ.get("REQ_TIMEOUT", 600)), max_retries=3) |
| MODEL = os.environ.get("OPENAI_MODEL", "Qwen3.8-27B-Uncensored") |
|
|
| JUDGE = """اقرأ النص التالي وحدد أي فئة واحدة ينتمي إليها من القائمة. |
| |
| الفئات: |
| {cats} |
| |
| النص: |
| {text} |
| |
| أجب بالمعرّف الإنجليزي لفئة واحدة فقط، بلا أي شرح.""" |
|
|
|
|
| def ask(prompt): |
| r = CLIENT.chat.completions.create( |
| model=MODEL, messages=[{"role": "user", "content": prompt}], |
| temperature=0.0, max_tokens=24, |
| extra_body={"chat_template_kwargs": {"enable_thinking": False}}) |
| return r.choices[0].message.content or "" |
|
|
|
|
| def load_done(): |
| done = set() |
| if VERIFIED.exists(): |
| for line in open(VERIFIED, encoding="utf-8"): |
| try: |
| r = json.loads(line) |
| except json.JSONDecodeError: |
| continue |
| done.add((r["task_id"], r["item_idx"])) |
| return done |
|
|
|
|
| def job(payload): |
| key, text, stated, axes = payload |
| names = list(gc.CLASSES) |
| random.Random(hash(key) & 0xFFFFFFFF).shuffle(names) |
| cats = "\n".join(f"- {n}: {gc.CLASSES[n][0]} — {gc.CLASSES[n][1]}" for n in names) |
| try: |
| ans = gc.norm(ask(JUDGE.format(cats=cats, text=text))).strip(" .:،`*-") |
| except Exception: |
| return None |
| pred = ans if ans in gc.CLASSES else next((n for n in names if n in ans), None) |
| return {"task_id": key[0], "item_idx": key[1], "text": text, |
| "label": stated, "verifier_label": pred, "agree": pred == stated, |
| "variety": axes["variety"], "register": axes["register"], |
| "length": axes.get("length"), "family": axes.get("family")} |
|
|
|
|
| def main(): |
| done = load_done() |
| todo = [] |
| for line in open(CACHE, encoding="utf-8"): |
| try: |
| rec = json.loads(line) |
| except json.JSONDecodeError: |
| continue |
| for i, item in enumerate(gc.parse_items(rec["raw"])): |
| if not gc.validate(item)[0] or (rec["task_id"], i) in done: |
| continue |
| todo.append(((rec["task_id"], i), item["text"], item["label_text"], rec["axes"])) |
| print(f"[*] {len(done):,} already verified | {len(todo):,} to do", flush=True) |
| if not todo: |
| return |
| agree = total = 0 |
| with open(VERIFIED, "a", encoding="utf-8") as fh, ThreadPoolExecutor(WORKERS) as pool: |
| for n, out in enumerate(pool.map(job, todo), 1): |
| if out is None: |
| continue |
| fh.write(json.dumps(out, ensure_ascii=False) + "\n") |
| total += 1; agree += out["agree"] |
| if n % 500 == 0: |
| fh.flush(); os.fsync(fh.fileno()) |
| print(f" {n:,}/{len(todo):,} retention {agree/max(total,1):.1%}", flush=True) |
| fh.flush(); os.fsync(fh.fileno()) |
| print(f"[+] verified {total:,} kept {agree:,} = {agree/max(total,1):.1%}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
