Jeffery (jiantw83)andClaude Sonnet 5.5
ea6b9ca7ef
verify_batches.py 逐批比對 id；make_relabel.py 挑出需重標的 none； merge_labels.py 支援多個目錄覆蓋與 confidence 縮寫；worry 加入情緒白名單。 Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
108 lines
4.7 KiB
Python
108 lines
4.7 KiB
Python
#!/usr/bin/env python3
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"""合併標註結果、檢查標籤白名單，並和規則判定比對。
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用法：merge_labels.py <dialogues.jsonl> <標註輸出目錄> <合併後 jsonl> [--review-dir DIR]
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- 標註的 speaker 只有 confidence=high 才採用；unknown、none（非說出口的話）與其他信心都視為未判定。
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- 標籤不在 tags.json 白名單內的整筆丟棄並列出。
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- 以規則判定為高信心的句子，和標註比對一致率，用來估計兩邊的可靠度。
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- --review-dir：每個角色輸出 20 句抽查清單（markdown），供人工檢查歸屬。
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"""
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import argparse
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import json
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import random
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from collections import Counter
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from pathlib import Path
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TAGS = json.loads(Path(__file__).with_name("tags.json").read_text(encoding="utf-8"))
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KEYS = {"id", "speaker", "confidence", "function", "emotion", "evidence"}
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def read_jsonl(path):
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return [json.loads(line) for line in Path(path).read_text(encoding="utf-8").splitlines() if line.strip()]
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# 有的批次輸出把 confidence 縮寫成一個字母；明確對應才轉換，其他值照常丟棄。
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CONFIDENCE_ALIASES = {"h": "high", "m": "medium", "l": "low"}
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def normalize(label):
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if label.get("confidence") in CONFIDENCE_ALIASES:
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return {**label, "confidence": CONFIDENCE_ALIASES[label["confidence"]]}
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return label
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def check(label):
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errs = []
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if set(label) != KEYS:
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errs.append(f"欄位不符：{sorted(set(label) ^ KEYS)}")
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if label.get("function") not in TAGS["functions"]:
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errs.append(f"function 不在白名單：{label.get('function')}")
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if label.get("emotion") not in TAGS["emotions"]:
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errs.append(f"emotion 不在白名單：{label.get('emotion')}")
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if label.get("confidence") not in ("high", "medium", "low"):
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errs.append(f"confidence 無效：{label.get('confidence')}")
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return errs
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("dialogues")
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ap.add_argument("labels_dir", help="標註輸出目錄；可用逗號接多個，後面的覆蓋前面的（用於重標）")
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ap.add_argument("out")
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ap.add_argument("--review-dir")
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a = ap.parse_args()
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recs = {r["id"]: r for r in read_jsonl(a.dialogues)}
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labels, rejected = {}, []
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files = [f for d in a.labels_dir.split(",") for f in sorted(Path(d).glob("*.jsonl"))]
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for f in files:
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for lb in read_jsonl(f):
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lb = normalize(lb)
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errs = check(lb)
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if lb.get("id") not in recs:
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errs.append("id 不存在")
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if errs:
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rejected.append((lb.get("id"), errs))
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else:
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labels[lb["id"]] = lb
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merged = []
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for rid, lb in labels.items():
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r = recs[rid]
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final = lb["speaker"] if lb["confidence"] == "high" and lb["speaker"] not in ("unknown", "none") else None
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merged.append({**r, "label": lb, "final_speaker": final})
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Path(a.out).write_text("".join(json.dumps(m, ensure_ascii=False) + "\n" for m in merged), encoding="utf-8")
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agree = Counter()
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for m in merged:
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r = m
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if r["confidence"] in ("high", "medium"):
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agree[(r["confidence"], r["speaker"] == m["label"]["speaker"])] += 1
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print(f"標註 {len(labels)} 句，丟棄 {len(rejected)} 筆")
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for rid, errs in rejected[:10]:
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print(" 丟棄", rid, errs)
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print("標註採用（high 且非 unknown）：", sum(1 for m in merged if m["final_speaker"]), "/", len(merged))
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print("標註信心：", dict(Counter(m["label"]["confidence"] for m in merged)))
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print("非說出口的「」（none）：", sum(1 for m in merged if m["label"]["speaker"] == "none"))
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for conf in ("high", "medium"):
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ok, bad = agree[(conf, True)], agree[(conf, False)]
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if ok + bad:
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print(f"規則 {conf} 與標註一致：{ok}/{ok + bad}")
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if a.review_dir:
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out = Path(a.review_dir)
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out.mkdir(parents=True, exist_ok=True)
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rng = random.Random(11)
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for who in ("asuna", "suguha"):
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pool = [m for m in merged if m["final_speaker"] == who]
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rows = rng.sample(pool, min(20, len(pool)))
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lines = [f"# 抽查：{who}（{len(pool)} 句採用，抽 {len(rows)}）", "", "每句請看：說話者是否正確？不對的請在行尾標記。", ""]
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for m in rows:
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ctx = (m["before"] or m["prev_par"])[-60:]
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lines += [f"- `{m['id']}`「{m['text'][:60]}」", f" - 前文：{ctx}", f" - 標註：{m['label']['function']}／{m['label']['emotion']}；依據：{m['label']['evidence']}"]
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(out / f"{who}.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
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print("抽查清單 ->", out)
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if __name__ == "__main__":
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main()
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