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ai-labeling: ground-truth v0.3.0 + 脚本扩展 --field 支持 Prompt 1

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- ground-truth.json 新增 4 分类字段骨架（program_format/equipment_domain/scene_tags/tech_tags）
- equipment_domain 类型修正为数组（对齐 v2 快照多选定义）
- run_labeling.py 新增 --field 参数，文件名含 field 标记
- summarize.py 新增 --field 参数 + 老文件向后兼容
- CLAUDE.md 补入 v1-v4 快照精华：象限图规格、6 字段枚举、三层交付策略

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
simonkoson
2026-06-24 17:53:05 +08:00
parent bdc59bf787
commit 30931d4446
4 changed files with 174 additions and 62 deletions
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ai-labeling/scripts/run_labeling.py
+29 -11
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@@ -41,11 +41,16 @@ MODEL_CONFIG = {
ALL_EPISODES = [3, 4, 5, 7, 10, 11, 12, 13, 14, 15]
FIELD_PROMPT_MAP = {
"narrative": "prompt2_narrative.md",
"classification": "prompt1_classification.md",
}
def load_prompt(field):
if field == "narrative":
return (PROMPTS_DIR / "prompt2_narrative.md").read_text(encoding="utf-8")
raise ValueError(f"Unknown field: {field}")
if field not in FIELD_PROMPT_MAP:
raise ValueError(f"Unknown field: {field}, valid: {list(FIELD_PROMPT_MAP.keys())}")
return (PROMPTS_DIR / FIELD_PROMPT_MAP[field]).read_text(encoding="utf-8")
def load_transcript(ep):
@@ -65,8 +70,19 @@ def load_ground_truth(ep):
def parse_prompt(template, transcript):
"""按 ## SYSTEM / ## USER 分隔符拆解 prompt。
自动剥离 ## SYSTEM 标签之前的标题行。
"""
parts = template.split("## USER")
system_prompt = parts[0].replace("# Prompt 2:叙事结构判别", "").strip()
# parts[0] 是 system 部分，可能包含标题行 + ## SYSTEM 标签
system_raw = parts[0]
# 如果有 ## SYSTEM 标签，取它之后的内容；否则去除标题行
if "## SYSTEM" in system_raw:
system_prompt = system_raw.split("## SYSTEM", 1)[1].strip()
else:
# 没有 ## SYSTEM 标签时，去掉第一行（标题行）作为降级处理
lines = system_raw.strip().splitlines()
system_prompt = "\n".join(lines[1:]).strip() if len(lines) > 1 else system_raw.strip()
user_prompt = parts[1].strip().replace("{transcript}", transcript)
return system_prompt, user_prompt
@@ -108,19 +124,19 @@ def call_model(model_key, system_prompt, user_prompt):
return extract_json_from_response(raw)
def run_labeling(ep, model_key):
def run_labeling(ep, model_key, field="narrative"):
transcript, fname = load_transcript(ep)
template = load_prompt("narrative")
template = load_prompt(field)
system_prompt, user_prompt = parse_prompt(template, transcript)
result = call_model(model_key, system_prompt, user_prompt)
gt = load_ground_truth(ep)
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
out = EXPERIMENTS_DIR / f"{ts}_{model_key}_ep{ep:02d}.json"
out = EXPERIMENTS_DIR / f"{ts}_{model_key}_{field}_ep{ep:02d}.json"
out.write_text(
json.dumps({"episode": ep, "filename": fname, "result": result, "ground_truth": gt}, ensure_ascii=False, indent=2),
json.dumps({"episode": ep, "filename": fname, "field": field, "result": result, "ground_truth": gt}, ensure_ascii=False, indent=2),
encoding="utf-8",
)
print(f"完成 ep{ep:02d} -> {out.name}")
print(f"完成 ep{ep:02d} [{field}] -> {out.name}")
return result
@@ -129,12 +145,14 @@ def main():
parser.add_argument("--ep", type=int, help="单期编号")
parser.add_argument("--all", action="store_true", help="跑全部")
parser.add_argument("--model", default="mimo-v2.5-pro", help="模型键名")
parser.add_argument("--field", default="narrative", choices=["narrative", "classification"],
help="打标字段: narrative(叙事结构) / classification(4分类)")
args = parser.parse_args()
if args.all:
for ep in ALL_EPISODES:
run_labeling(ep, args.model)
run_labeling(ep, args.model, args.field)
elif args.ep:
run_labeling(args.ep, args.model)
run_labeling(args.ep, args.model, args.field)
else:
parser.print_help()