Download eval/prompts/_text.py from VirtualLUO/Chronicles-OCR: direct link, hf CLI and curl.
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https://huggingface.co/datasets/VirtualLUO/Chronicles-OCR/resolve/main/eval/prompts/_text.py
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curl -L -o _text.py https://huggingface.co/datasets/VirtualLUO/Chronicles-OCR/resolve/main/eval/prompts/_text.py
4.03 kB
| """Prompt / Extract 共用文本工具。 | |
| - ``strip_thinking``: 剥离模型输出里的 ``<think>...</think>`` / ``<answer>...</answer>`` | |
| - ``extract_by_prefix``: 按多前缀("字体分类:" / "字体:" / ...)从纯文本中抽取冒号后的内容 | |
| - ``clean_value``: 清理首尾空白、代码块围栏、常见标点引号 | |
| """ | |
| from __future__ import annotations | |
| import re | |
| _OTHER_PREFIX_RE = re.compile(r"^(?:字体分类|字体|分类|提取文本|提取结果|识别结果|识别文本|文本)\s*[::]") | |
| def strip_thinking(text: str) -> str: | |
| """剥离 thinking 段,只保留最终答案。 | |
| 支持 ``<think>...</think><answer>...</answer>`` / 仅 ``</think>`` / 仅 ``<think>`` 等多种形态。 | |
| """ | |
| if not text: | |
| return "" | |
| s = text | |
| ans_m = re.search(r"<\s*answer\s*>([\s\S]*?)<\s*/\s*answer\s*>", s, flags=re.IGNORECASE) | |
| if ans_m: | |
| return ans_m.group(1).strip() | |
| close_matches = list(re.finditer(r"<\s*/\s*(?:think|thinking|reasoning)\s*>", s, flags=re.IGNORECASE)) | |
| if close_matches: | |
| last = close_matches[-1] | |
| tail = s[last.end() :].strip() | |
| tail = re.sub(r"^\s*<\s*answer\s*>\s*", "", tail, flags=re.IGNORECASE) | |
| tail = re.sub(r"\s*<\s*/?\s*answer\s*>\s*$", "", tail, flags=re.IGNORECASE) | |
| return tail.strip() | |
| open_m = re.search(r"<\s*(?:think|thinking|reasoning)\s*>", s, flags=re.IGNORECASE) | |
| if open_m: | |
| head = s[: open_m.start()].strip() | |
| return head if head else "" | |
| return s.strip() | |
| def strip_code_fence(s: str) -> str: | |
| if s is None: | |
| return "" | |
| s = s.strip() | |
| m = re.match(r"^`{3,}[^\n`]*\n?(.*?)\n?`{3,}\s*$", s, re.DOTALL) | |
| if m: | |
| return m.group(1).strip() | |
| return s.strip("`").strip() | |
| def clean_value(s: str) -> str: | |
| if not s: | |
| return "" | |
| s = strip_code_fence(s) | |
| s = s.strip().strip("。.;;,,\"'“”‘’") | |
| return s.strip() | |
| def extract_by_prefix(text: str, prefixes: list[str], merge_trailing_lines: bool = False) -> str: | |
| """按 ``前缀:`` 抽取冒号之后的答案,命中多次取最后一次。 | |
| ``merge_trailing_lines=True`` 时,"同行答案 + 后续多行非空"一并合并, | |
| 用于"字符提取"任务(模型常把多行文本写在前缀之后)。 | |
| """ | |
| if not text: | |
| return "" | |
| prefix_pattern = "|".join(re.escape(p) for p in prefixes) | |
| head_pattern = re.compile(rf"(?:{prefix_pattern})\s*[::]") | |
| matches = list(head_pattern.finditer(text)) | |
| if not matches: | |
| return "" | |
| last = matches[-1] | |
| tail = text[last.end() :] | |
| first_line, _nl, rest = tail.partition("\n") | |
| first_line_stripped = first_line.strip() | |
| starts_with_fence = bool(re.match(r"^`{3,}", first_line_stripped)) | |
| first_line_no_fence = re.sub(r"^`{3,}[^\n`]*", "", first_line_stripped).strip("` ").strip() | |
| if not starts_with_fence and first_line_no_fence: | |
| if not merge_trailing_lines: | |
| return clean_value(first_line_no_fence) | |
| follow_lines: list[str] = [] | |
| for ln in rest.splitlines(): | |
| ln_s = ln.strip().strip("`").strip() | |
| if not ln_s: | |
| continue | |
| if _OTHER_PREFIX_RE.search(ln_s): | |
| break | |
| follow_lines.append(ln_s) | |
| head_clean = clean_value(first_line_no_fence) | |
| if not follow_lines: | |
| return head_clean | |
| cleaned_follow = [(clean_value(ln) or ln) for ln in follow_lines] | |
| return "\n".join([head_clean, *cleaned_follow]).strip() | |
| multiline_src = rest | |
| if starts_with_fence: | |
| close_m = re.search(r"\n?`{3,}\s*(\n|$)", rest) | |
| if close_m: | |
| multiline_src = rest[: close_m.start()] | |
| lines = [ln.strip().strip("`").strip() for ln in multiline_src.splitlines()] | |
| lines = [ln for ln in lines if ln] | |
| if not lines: | |
| return "" | |
| if len(lines) == 1: | |
| return clean_value(lines[0]) | |
| cleaned = [(clean_value(ln) or ln) for ln in lines] | |
| return "\n".join(cleaned).strip() | |