645 lines
21 KiB
Python
Executable File
645 lines
21 KiB
Python
Executable File
#!/usr/bin/env python3
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"""检查中文成稿的硬禁令与常见模型化形状。只报警，不自动改文。
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来源：https://github.com/KKKKhazix/human-writing （MIT，版权归 Human Writing Skill
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contributors）。本工坊原样引入未改检测规则；禁令清单见 references/human-voice.md
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第七节与 review-playbook.md「模型腔硬禁清单」（增删禁词须同步改本脚本）。
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"""
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from __future__ import annotations
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import argparse
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import collections
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import re
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import sys
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from dataclasses import dataclass
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from pathlib import Path
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HARD_STOPS = (
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"说白了",
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"说穿了",
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"先说结论",
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)
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HARD_JARGON = (
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"赋能",
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"抓手",
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"商业闭环",
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"价值闭环",
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"能力沉淀",
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"拉通",
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"底层逻辑",
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"顶层设计",
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"认知跃迁",
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"价值释放",
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"能力建设",
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"降本增效",
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"内容矩阵",
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"全链路",
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"组合拳",
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"打开想象空间",
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"结构性机会",
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"关键命题",
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"深层逻辑",
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"技术底座",
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"公共底座",
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"技术主权",
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"单点风险",
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"主脊柱",
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"材料锚点",
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"认知增量",
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"迭代闭环",
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)
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CONTEXT_JARGON = (
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"沉淀",
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"颗粒度",
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"对齐",
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"协同",
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"链路",
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"生态位",
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"心智",
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"范式",
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"方法论",
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"核心变量",
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"打法",
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"想象空间",
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"闭环",
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"不丢",
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)
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LYRIC_WORDS = (
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"安放",
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"抵达",
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"微光",
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"褶皱",
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"丰盈",
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"滚烫",
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"轻盈",
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"赤裸",
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"剥开",
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)
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ROAD_SIGNS = (
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"更微妙的是",
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"还有一层",
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"只说对了一半",
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"值得注意的是",
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"需要指出的是",
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"从某种意义上说",
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)
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ROAD_STRIP_CHARS = "。！？!? \n"
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FORBIDDEN_PUNCTUATION = {
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"：": "中文冒号",
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":": "英文冒号",
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"—": "破折号",
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"–": "连接号式破折号",
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}
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PIVOT_PATTERNS = (
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re.compile(r"(?:并)?不是[^。！？\n]{0,90}而是"),
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re.compile(r"并非[^。！？\n]{0,90}而是"),
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re.compile(r"不在于[^。！？\n]{0,90}而在于"),
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re.compile(r"与其说[^。！？\n]{0,90}(?:不如|毋宁|倒不如)"),
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re.compile(r"[。！？!?]\s*而是"),
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re.compile(r"表面(?:上)?[^。！？\n]{0,90}(?:其实|实际|实则)"),
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re.compile(r"看似[^。！？\n]{0,90}(?:其实|实际|实则)"),
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)
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SEMANTIC_PIVOT_PATTERNS = (
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re.compile(r"(?:总|一直|曾|都)?以为[^！？\n]{2,60}?(?:其实|才发现|才明白|才知道|后来才)"),
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re.compile(r"(?:总|都|一直)以为[^！？\n]{2,60}?[。，](?:可|但|其实)"),
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re.compile(r"回头(?:看|一看)?才(?:发现|明白|知道)"),
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re.compile(r"(?:并)?不是[^。！？\n]{1,40}，(?:更|才)?是[^，。！？\n]"),
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re.compile(r"从来(?:都)?(?:不是|与[^。！？，\n]{1,12}无关)"),
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re.compile(r"答案(?:是否定的|恰恰相反)|恰恰相反"),
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re.compile(r"表面(?:上)?[^！？\n]{0,60}。[^！？\n]{0,12}(?:其实|实际|实则)"),
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re.compile(r"看似[^！？\n]{0,60}。[^！？\n]{0,12}(?:其实|实际|实则)"),
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re.compile(r"[^，。！？\n]{1,12}不重要，(?:重要|要紧)的是"),
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re.compile(r"真正[^，。！？\n]{0,16}的(?:，)?是"),
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re.compile(r"不只(?:是)?[^。！？\n]{0,90}(?:还|也)"),
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)
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NOMINALIZATION_PATTERNS = (
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re.compile(r"进行(?:了|一次|一场|着)?[^。，！？\n]{0,10}(?:调整|优化|升级|分析|讨论|沟通|梳理|复盘|迭代|探索|尝试|思考|规划|布局)"),
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re.compile(r"实现了?[^。，！？\n]{0,14}的?[^。，！？\n]{0,6}(?:提升|增长|突破|转变|跃升|落地)"),
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re.compile(r"完成了?对[^。，！？\n]{0,16}的"),
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re.compile(r"起到了?[^。，！？\n]{0,12}的?作用"),
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re.compile(r"具有[^。，！？\n]{0,10}(?:意义|价值)"),
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)
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CONJUNCTIONS = (
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"因为",
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"所以",
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"但是",
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"然而",
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"同时",
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"此外",
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"而且",
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"并且",
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"因此",
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"不仅",
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)
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ROAD_SIGN_PATTERNS = (
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re.compile(
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rf"(?:^|[。！？!?]\s*){re.escape(ROAD_SIGNS[0])}[^。！？!?\n]{{0,24}}",
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re.MULTILINE,
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),
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re.compile(
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rf"(?:^|[。！？!?]\s*){re.escape(ROAD_SIGNS[1])}(?=(?:更|原因|问题|意思|考虑|变化|逻辑|价值|作用|风险|影响|值得|很少|不容易|常被|往往))[^。！？!?\n]{{0,24}}",
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re.MULTILINE,
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),
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*(
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re.compile(
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rf"(?:^|[。！？!?]\s*){re.escape(phrase)}[^。！？!?\n]{{0,24}}",
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re.MULTILINE,
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)
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for phrase in ROAD_SIGNS[2:]
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),
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)
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SOFT_MARKERS = (
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"真正",
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"本质上",
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"更深层次",
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"归根结底",
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"换句话说",
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"不可否认",
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"核心是",
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"关键在于",
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"这意味着",
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)
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REPEATED_OPENERS = (
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"其实",
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"不过",
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"当然",
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"所以",
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"但是",
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"后来",
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"当时",
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"很多人",
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"问题是",
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"更重要的是",
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"说到这里",
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)
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LEFT_BRANCH_PATTERNS = (
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re.compile(r"(?:^|[。！？]\s*)在[^，。！？\n]{12,70}(?:以后|之后|之前|以前|过程中|情况下|背景下)，"),
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re.compile(r"(?:^|[。！？]\s*)那些[^，。！？\n]{10,60}的[^，。！？\n]{2,30}[，。]"),
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re.compile(r"(?:^|[。！？]\s*)(?:真正|最终|最后)让[^，。！？\n]{8,70}的，是"),
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)
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METAPHOR_FIELDS = {
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"温度": ("降温", "升温", "冷却", "余温", "温度最高"),
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"生死战争": ("杀死", "死因", "枪响", "开火", "战场", "引爆", "弹药"),
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"建筑灾害": ("坍塌", "崩塌", "地基", "砖头", "支柱", "废墟"),
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"仓储租赁": ("仓库", "库房", "租金", "取货", "入库", "库存"),
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"道路竞赛": ("赛道", "跑道", "岔路", "十字路口", "终点线", "门票"),
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"机器器官": ("齿轮", "引擎", "发动机", "血管", "骨架", "肌肉"),
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"海洋航行": ("蓝海", "浪潮", "潮水", "航船", "灯塔", "彼岸"),
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}
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@dataclass
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class Paragraph:
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position: int
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text: str
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han: int
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sentences: int
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def han_count(text: str) -> int:
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return len(re.findall(r"[\u4e00-\u9fff]", text))
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def line_number(text: str, position: int) -> int:
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return text.count("\n", 0, position) + 1
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def excerpt(value: str, width: int = 72) -> str:
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value = re.sub(r"\s+", " ", value).strip()
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return value if len(value) <= width else value[: width - 1] + "…"
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def mask_non_prose(text: str) -> str:
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"""屏蔽代码、网址和机器元数据，同时保留字符位置与换行。"""
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def mask(match: re.Match[str]) -> str:
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return "".join("\n" if char == "\n" else " " for char in match.group())
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patterns = (
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re.compile(r"\A---\s*\n.*?\n---\s*(?:\n|\Z)", re.DOTALL),
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re.compile(r"```.*?```", re.DOTALL),
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re.compile(r"`[^`\n]*`"),
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re.compile(r"\]\([^\n)]*\)"),
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re.compile(r"https?://[^\s)>]+"),
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re.compile(r"<[^>\n]+>"),
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)
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masked = text
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for pattern in patterns:
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masked = pattern.sub(mask, masked)
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return masked
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def non_overlapping_terms(text: str, terms: tuple[str, ...]):
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matches = []
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occupied = []
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for term in sorted(terms, key=len, reverse=True):
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for match in re.finditer(re.escape(term), text):
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start, end = match.span()
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if any(start < old_end and end > old_start for old_start, old_end in occupied):
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continue
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matches.append((start, term))
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occupied.append((start, end))
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return sorted(matches)
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def all_matches(text: str, patterns: tuple[re.Pattern[str], ...]):
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matches = []
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for pattern in patterns:
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matches.extend(pattern.finditer(text))
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return sorted(matches, key=lambda match: match.start())
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def heavy_de_sentences(text: str):
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"""找出主干可能被多个“的”压到后面的长句。"""
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matches = []
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pattern = re.compile(r"[^。！？!?\n]+(?:[。！？!?]|$)")
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for match in pattern.finditer(text):
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value = match.group()
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if han_count(value) >= 38 and value.count("的") >= 4:
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matches.append(match)
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return matches
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def anaphora_runs(text: str, minimum: int = 3):
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"""找出同一句里三个以上小句用同一个开头的排比。"""
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matches = []
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for sentence in re.finditer(r"[^。！？!?\n]+(?:[。！？!?]|$)", text):
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clauses = [
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clause.strip()
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for clause in re.split(r"[，、；,;]", sentence.group())
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if han_count(clause) >= 3
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]
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if len(clauses) < minimum:
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continue
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run = 1
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for previous, current in zip(clauses, clauses[1:]):
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if previous[:2] == current[:2] and re.match(r"[一-鿿]{2}", current):
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run += 1
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if run >= minimum:
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matches.append(sentence)
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break
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else:
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run = 1
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return matches
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def sentence_length_cv(text: str):
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"""句长变异系数。人写的长短句差距大，模型的句长彼此接近。"""
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lengths = [
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han_count(match.group())
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for match in re.finditer(r"[^。！？!?\n]+[。！？!?]", text)
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if han_count(match.group()) >= 4
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]
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if len(lengths) < 12:
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return None
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mean = sum(lengths) / len(lengths)
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if mean == 0:
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return None
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variance = sum((value - mean) ** 2 for value in lengths) / len(lengths)
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return (variance ** 0.5) / mean, len(lengths)
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def bracket_highlights(text: str):
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"""「」括起来的短语。太密说明在批量造金句。"""
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return list(re.finditer(r"[「『][^」』\n]{1,6}[」』]", text))
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def prose_paragraphs(text: str) -> list[Paragraph]:
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paragraphs = []
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cursor = 0
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for block in re.split(r"\n\s*\n", text):
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position = text.find(block, cursor)
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cursor = max(position + len(block), cursor)
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clean = re.sub(r"[>*_`]", "", block).strip()
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if not clean or clean.startswith(("#", "http", "![", "```")):
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continue
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if re.match(r"^(?:[-+*]|\d+[.、])\s", clean):
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continue
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count = han_count(clean)
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if count < 4:
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continue
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sentences = max(1, len(re.findall(r"[。！？!?]", clean)))
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paragraphs.append(Paragraph(position, clean, count, sentences))
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return paragraphs
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def metaphor_cluster(text: str, distance: int = 800):
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hits = []
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for field, words in METAPHOR_FIELDS.items():
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for word in words:
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for match in re.finditer(re.escape(word), text):
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hits.append((match.start(), field, word))
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hits.sort()
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for index, (start, _, _) in enumerate(hits):
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window = [hit for hit in hits[index:] if hit[0] - start <= distance]
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fields = {hit[1] for hit in window}
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if len(fields) >= 3:
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return window, fields
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return None
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def short_streak(paragraphs: list[Paragraph], limit: int = 4):
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streak = []
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for paragraph in paragraphs:
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if paragraph.han <= 24 and paragraph.sentences <= 1:
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streak.append(paragraph)
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if len(streak) >= limit:
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return streak
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else:
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streak = []
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return None
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def opener_counts(paragraphs: list[Paragraph]):
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counts = collections.Counter()
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examples = {}
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for paragraph in paragraphs:
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value = paragraph.text.lstrip("“‘\"（(")
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for opener in REPEATED_OPENERS:
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if value.startswith(opener):
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counts[opener] += 1
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examples.setdefault(opener, paragraph.position)
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break
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return counts, examples
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def read_text(path: str) -> str:
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if path == "-":
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return sys.stdin.read()
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return Path(path).read_text(encoding="utf-8")
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def main() -> int:
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parser = argparse.ArgumentParser(description="检查中文成稿的硬禁令与模型化形状")
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parser.add_argument("path", help="Markdown 或文本文件路径。使用 - 从标准输入读取")
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args = parser.parse_args()
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try:
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text = read_text(args.path)
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except (OSError, UnicodeError) as error:
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print(f"无法读取稿件。{error}", file=sys.stderr)
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return 2
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prose = mask_non_prose(text)
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total_han = han_count(prose)
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if total_han == 0:
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print("没有检测到汉字。", file=sys.stderr)
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return 2
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failures = []
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warnings = []
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quote_colons = []
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for symbol, label in FORBIDDEN_PUNCTUATION.items():
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matches = list(re.finditer(re.escape(symbol), prose))
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if symbol in ("：", ":"):
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hard = []
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for match in matches:
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tail = prose[match.end() : match.end() + 2].lstrip()
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if tail[:1] in ("「", "『", "“", "‘", '"'):
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quote_colons.append(match)
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else:
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hard.append(match)
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matches = hard
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if matches:
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lines = "、".join(str(line_number(text, match.start())) for match in matches[:8])
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failures.append(f"{label}共 {len(matches)} 处，出现在第 {lines} 行。")
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if quote_colons:
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lines = "、".join(
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str(line_number(text, match.start())) for match in quote_colons[:8]
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)
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warnings.append(
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f"引出原话的冒号 {len(quote_colons)} 处，第 {lines} 行。确认引号里确实是原话，且不是提示性用法。"
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)
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stop_matches = non_overlapping_terms(prose, HARD_STOPS)
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for position, phrase in stop_matches:
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failures.append(f"硬停词，第 {line_number(text, position)} 行，{phrase}")
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jargon_matches = non_overlapping_terms(prose, HARD_JARGON)
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for position, phrase in jargon_matches:
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failures.append(f"黑话，第 {line_number(text, position)} 行，{phrase}")
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|
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context_jargon_matches = non_overlapping_terms(prose, CONTEXT_JARGON)
|
||
hard_spans = [
|
||
(position, position + len(phrase)) for position, phrase in jargon_matches
|
||
]
|
||
context_jargon_matches = [
|
||
(position, phrase)
|
||
for position, phrase in context_jargon_matches
|
||
if not any(
|
||
position < end and position + len(phrase) > start
|
||
for start, end in hard_spans
|
||
)
|
||
]
|
||
if context_jargon_matches:
|
||
samples = "、".join(
|
||
dict.fromkeys(phrase for _, phrase in context_jargon_matches)
|
||
)
|
||
lines = "、".join(
|
||
dict.fromkeys(
|
||
str(line_number(text, position))
|
||
for position, _ in context_jargon_matches[:8]
|
||
)
|
||
)
|
||
warnings.append(
|
||
f"有 {len(context_jargon_matches)} 处词语需要结合语境判断。"
|
||
f"第 {lines} 行出现 {samples}。本义准确时保留，用来抬价时改写。"
|
||
)
|
||
|
||
road_signs = all_matches(prose, ROAD_SIGN_PATTERNS)
|
||
for match in road_signs:
|
||
failures.append(
|
||
f"模型路标，第 {line_number(text, match.start())} 行，"
|
||
f"“{excerpt(match.group().lstrip(ROAD_STRIP_CHARS))}”"
|
||
)
|
||
|
||
pivots = all_matches(prose, PIVOT_PATTERNS)
|
||
for match in pivots:
|
||
failures.append(
|
||
f"禁用翻案句，第 {line_number(text, match.start())} 行，"
|
||
f"“{excerpt(match.group())}”"
|
||
)
|
||
|
||
occupied_spans = [match.span() for match in pivots]
|
||
semantic_pivots = []
|
||
for match in all_matches(prose, SEMANTIC_PIVOT_PATTERNS):
|
||
if any(
|
||
match.start() < end and match.end() > start
|
||
for start, end in occupied_spans
|
||
):
|
||
continue
|
||
semantic_pivots.append(match)
|
||
occupied_spans.append(match.span())
|
||
for match in semantic_pivots:
|
||
warnings.append(
|
||
f"疑似翻案腔变形，第 {line_number(text, match.start())} 行，"
|
||
f"“{excerpt(match.group(), 44)}”。先立误解再推翻就改成正面陈述，正常用法保留。"
|
||
)
|
||
|
||
anaphoras = anaphora_runs(prose)
|
||
for match in anaphoras[:4]:
|
||
warnings.append(
|
||
f"三连以上同构排比，第 {line_number(text, match.start())} 行，"
|
||
f"“{excerpt(match.group(), 44)}”。留两项，第三项换说法或删掉。"
|
||
)
|
||
|
||
lyric_matches = non_overlapping_terms(prose, LYRIC_WORDS)
|
||
if len(lyric_matches) >= 2:
|
||
samples = "、".join(dict.fromkeys(term for _, term in lyric_matches))
|
||
warnings.append(
|
||
f"模型偏爱的抒情词 {len(lyric_matches)} 处。{samples}。"
|
||
"写具体事物时保留，给抽象概念穿衣服时删掉。"
|
||
)
|
||
|
||
nominalizations = all_matches(prose, NOMINALIZATION_PATTERNS)
|
||
for match in nominalizations[:4]:
|
||
warnings.append(
|
||
f"名词化句式，第 {line_number(text, match.start())} 行，"
|
||
f"“{excerpt(match.group(), 36)}”。还原成直接的动词。"
|
||
)
|
||
|
||
conjunction_hits = non_overlapping_terms(prose, CONJUNCTIONS)
|
||
if total_han >= 600 and len(conjunction_hits) * 1000 / total_han > 7:
|
||
samples = "、".join(
|
||
f"{term} {count} 次"
|
||
for term, count in collections.Counter(
|
||
term for _, term in conjunction_hits
|
||
).most_common(4)
|
||
)
|
||
warnings.append(
|
||
f"连词密度偏高，每千字 {len(conjunction_hits) * 1000 // total_han} 个。{samples}。"
|
||
"中文小句靠语序和事理相接，删掉一半试试。"
|
||
)
|
||
|
||
highlights = bracket_highlights(prose)
|
||
highlight_limit = max(3, total_han // 700)
|
||
if len(highlights) > highlight_limit:
|
||
samples = "、".join(dict.fromkeys(match.group() for match in highlights[:6]))
|
||
warnings.append(
|
||
f"「」括起的短语共 {len(highlights)} 处。{samples}。太密说明在批量造金句。"
|
||
)
|
||
|
||
cv_result = sentence_length_cv(prose)
|
||
if cv_result and cv_result[0] < 0.42:
|
||
warnings.append(
|
||
f"全文 {cv_result[1]} 个句子长度过于接近（变异系数 {cv_result[0]:.2f}）。"
|
||
"人写的段落里十个字的句子会挨着四十个字的句子，放开几句，压短几句。"
|
||
)
|
||
|
||
marker_matches = non_overlapping_terms(prose, SOFT_MARKERS)
|
||
marker_limit = max(2, total_han // 900)
|
||
if len(marker_matches) > marker_limit:
|
||
samples = "、".join(dict.fromkeys(term for _, term in marker_matches))
|
||
warnings.append(
|
||
f"洞察路标共 {len(marker_matches)} 处，当前提醒线为 {marker_limit} 处。"
|
||
f"重点检查 {samples}。"
|
||
)
|
||
|
||
left_branches = all_matches(prose, LEFT_BRANCH_PATTERNS)
|
||
left_limit = max(2, total_han // 1200)
|
||
if len(left_branches) > left_limit:
|
||
samples = "；".join(
|
||
f"第 {line_number(text, match.start())} 行“{excerpt(match.group(), 44)}”"
|
||
for match in left_branches[:4]
|
||
)
|
||
warnings.append(
|
||
f"长前置成分共 {len(left_branches)} 处，可能让主干来得太晚。{samples}"
|
||
)
|
||
|
||
dense_de = heavy_de_sentences(prose)
|
||
dense_de_limit = max(1, total_han // 1500)
|
||
if len(dense_de) > dense_de_limit:
|
||
samples = "；".join(
|
||
f"第 {line_number(text, match.start())} 行“{excerpt(match.group(), 44)}”"
|
||
for match in dense_de[:4]
|
||
)
|
||
warnings.append(
|
||
f"有 {len(dense_de)} 个长句包含四个以上的“的”，可能要先交代人和动作。{samples}"
|
||
)
|
||
|
||
paragraphs = prose_paragraphs(prose)
|
||
if len(paragraphs) >= 10:
|
||
one_sentence = sum(paragraph.sentences <= 1 for paragraph in paragraphs)
|
||
ratio = one_sentence / len(paragraphs)
|
||
if ratio >= 0.75:
|
||
warnings.append(
|
||
f"可识别段落中有 {ratio:.0%} 只有一句话，可能形成统一的短段鼓点。"
|
||
)
|
||
|
||
streak = short_streak(paragraphs)
|
||
if streak:
|
||
first = streak[0]
|
||
warnings.append(
|
||
f"从第 {line_number(text, first.position)} 行起连续出现 {len(streak)} 个短促单句段，"
|
||
"检查是否在排队喊结论。"
|
||
)
|
||
|
||
counts, opener_examples = opener_counts(paragraphs)
|
||
repeated = [(opener, count) for opener, count in counts.items() if count >= 4]
|
||
if repeated:
|
||
details = "、".join(f"{opener} {count} 次" for opener, count in repeated)
|
||
first_position = min(opener_examples[opener] for opener, _ in repeated)
|
||
warnings.append(
|
||
f"段落开场重复，从第 {line_number(text, first_position)} 行附近开始。{details}。"
|
||
)
|
||
|
||
metaphors = metaphor_cluster(prose)
|
||
if metaphors:
|
||
window, fields = metaphors
|
||
samples = "、".join(dict.fromkeys(hit[2] for hit in window))
|
||
warnings.append(
|
||
f"八百字内出现 {len(fields)} 套借喻。{'、'.join(sorted(fields))}。"
|
||
f"例词有 {samples}。"
|
||
)
|
||
|
||
print(f"汉字数 {total_han}")
|
||
print(
|
||
f"翻案句 {len(pivots)}，翻案腔变形 {len(semantic_pivots)}，"
|
||
f"同构排比 {len(anaphoras)}，名词化 {len(nominalizations)}，"
|
||
f"黑话 {len(jargon_matches)}，硬停词 {len(stop_matches)}，"
|
||
f"模型路标 {len(road_signs)}，需辨语境词 {len(context_jargon_matches)}，"
|
||
f"抒情词 {len(lyric_matches)}，洞察路标 {len(marker_matches)}，"
|
||
f"长前置成分 {len(left_branches)}，重定语句 {len(dense_de)}"
|
||
)
|
||
|
||
if failures:
|
||
print("\n需要修改")
|
||
for item in failures:
|
||
print(f"- {item}")
|
||
|
||
if warnings:
|
||
print("\n需要人工判断")
|
||
for item in warnings:
|
||
print(f"- {item}")
|
||
|
||
if not failures and not warnings:
|
||
print("\n未发现这份检查器覆盖的问题。")
|
||
|
||
return 1 if failures else 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
raise SystemExit(main())
|