| """Render adjudication rows' states through Qwen3-14B (vLLM) into diverse |
| natural-language registers while preserving all decision-relevant facts. |
| |
| Validation: every numeric literal and identifier-ish token in the source |
| state must appear in the rendered text (case-insensitive). Failures are |
| dropped — labels are programmatic so a faithful render is all we need. |
| """ |
| import json, re, sys, argparse, random |
|
|
| PROMPT = """Rewrite the following internal business document into a different but equally realistic register. Vary the structure freely — e.g. interleaved email thread, policy handbook pages, ticket + attachment layout, memo chain, multi-document case file, table-heavy annex format. You may rename headings, reorder sections, change formatting, and adjust phrasing. |
| |
| HARD REQUIREMENTS: |
| - Preserve EVERY number, date, clause/section identifier, entity name, version string, and quoted note — they carry the decision logic. |
| - Keep all logical relationships intact (which rule overrides which, dates relative to each other, thresholds, definitions). |
| - Keep the embedded agent/intake notes expressing the same (possibly wrong) recommendation. |
| - Do NOT state or hint at the correct answer. |
| - Output ONLY the document text — no preamble, no commentary. |
| |
| DOCUMENT: |
| %s""" |
|
|
| NUM = re.compile(r"\d[\d,.:%/\-]*") |
| ID = re.compile(r"\b[A-Z]{1,4}-?[A-Z0-9]{2,}(?:-[A-Z0-9]{2,})*\b") |
|
|
| def required_tokens(text): |
| toks = set(NUM.findall(text)) | set(ID.findall(text)) |
| |
| return {t.strip(",.:-") for t in toks if len(t.strip(",.:-")) >= 1} |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--in", dest="inp", required=True) |
| ap.add_argument("--out", required=True) |
| ap.add_argument("--n", type=int, default=8000) |
| ap.add_argument("--model", default="/ephemeral/models/qwen3-14b") |
| args = ap.parse_args() |
| rows = [json.loads(l) for l in open(args.inp, encoding="utf-8")] |
| rows = [r for r in rows if isinstance(r["state"], str)] |
| random.seed(3); random.shuffle(rows) |
| rows = rows[: args.n] |
| from vllm import LLM, SamplingParams |
| from transformers import AutoTokenizer |
| llm = LLM(model=args.model, dtype="bfloat16", max_model_len=8192, |
| gpu_memory_utilization=0.88, enforce_eager=False) |
| ctok = AutoTokenizer.from_pretrained(args.model) |
| sp = SamplingParams(temperature=0.85, top_p=0.95, max_tokens=3500) |
| prompts = [] |
| for r in rows: |
| m = [{"role": "user", "content": PROMPT % r["state"]}] |
| try: |
| prompts.append(ctok.apply_chat_template( |
| m, tokenize=False, add_generation_prompt=True, |
| enable_thinking=False)) |
| except TypeError: |
| prompts.append(ctok.apply_chat_template( |
| m, tokenize=False, add_generation_prompt=True) |
| + "/no_think\n") |
| outs = llm.generate(prompts, sp) |
| kept = 0 |
| with open(args.out, "w") as f: |
| for r, o in zip(rows, outs): |
| txt = o.outputs[0].text.strip() |
| if not txt or len(txt) < 200: |
| continue |
| need = required_tokens(r["state"]) |
| low = txt.lower() |
| missing = [t for t in need if t.lower() not in low] |
| if len(missing) > max(1, len(need) // 12): |
| continue |
| r2 = dict(r) |
| r2["id"] = r["id"] + "-r" |
| r2["group_id"] = r["group_id"] + "-r" |
| r2["state"] = txt |
| r2["source"] = r["source"] + "_render" |
| f.write(json.dumps(r2, ensure_ascii=False) + "\n") |
| kept += 1 |
| print(f"rendered+validated {kept}/{len(rows)}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
