[Refector] visualization

docs/en/get_started/supported_dataset/llm.md

@@ -3,7 +3,7 @@
 Below is the list of supported LLM benchmarks. Click on a benchmark name to jump to details.
 
 | Benchmark Name | Pretty Name | Task Categories |
-|----------------|-------------|----------------|
+|------------|----------|----------|
 | `aime24` | [AIME-2024](#aime-2024) | `Mathematics` |
 | `aime25` | [AIME-2025](#aime-2025) | `Mathematics` |
 | `alpaca_eval` | [AlpacaEval2.0](#alpacaeval20) | `Instruction-Following`, `Reasoning` |

docs/generate_dataset_md.py

@@ -1,8 +1,107 @@
 import json
 from tqdm import tqdm
+from typing import Any, Dict
 
 from evalscope.benchmarks import DataAdapter
 
+# Language dictionaries for dataset markdown generation
+DATASET_DETAIL_LOCALE = {
+    'back_to_top': {
+        'zh': '返回目录',
+        'en': 'Back to Top'
+    },
+    'toc_title': {
+        'zh': 'LLM评测集',
+        'en': 'LLM Benchmarks'
+    },
+    'dataset_name': {
+        'zh': '数据集名称',
+        'en': 'Dataset Name'
+    },
+    'dataset_id': {
+        'zh': '数据集ID',
+        'en': 'Dataset ID'
+    },
+    'description': {
+        'zh': '数据集描述',
+        'en': 'Description'
+    },
+    'task_categories': {
+        'zh': '任务类别',
+        'en': 'Task Categories'
+    },
+    'evaluation_metrics': {
+        'zh': '评估指标',
+        'en': 'Evaluation Metrics'
+    },
+    'requires_llm_judge': {
+        'zh': '需要LLM Judge',
+        'en': 'Requires LLM Judge'
+    },
+    'default_shots': {
+        'zh': '默认提示方式',
+        'en': 'Default Shots'
+    },
+    'subsets': {
+        'zh': '数据集子集',
+        'en': 'Subsets'
+    },
+    'supported_output_formats': {
+        'zh': '支持输出格式',
+        'en': 'Supported Output Formats'
+    },
+    'extra_parameters': {
+        'zh': '额外参数',
+        'en': 'Extra Parameters'
+    },
+    'system_prompt': {
+        'zh': '系统提示词',
+        'en': 'System Prompt'
+    },
+    'prompt_template': {
+        'zh': '提示模板',
+        'en': 'Prompt Template'
+    },
+    'yes': {
+        'zh': '是',
+        'en': 'Yes'
+    },
+    'no': {
+        'zh': '否',
+        'en': 'No'
+    },
+    'no_description': {
+        'zh': '暂无详细描述',
+        'en': 'No detailed description available'
+    }
+}
+
+DOCUMENT_LOCALE = {
+    'title': {
+        'zh': 'LLM评测集',
+        'en': 'LLM Benchmarks'
+    },
+    'intro': {
+        'zh': '以下是支持的LLM评测集列表，点击数据集标准名称可跳转详细信息。',
+        'en': 'Below is the list of supported LLM benchmarks. Click on a benchmark name to jump to details.'
+    },
+    'dataset_name': {
+        'zh': '数据集名称',
+        'en': 'Benchmark Name'
+    },
+    'pretty_name': {
+        'zh': '标准名称',
+        'en': 'Pretty Name'
+    },
+    'task_categories': {
+        'zh': '任务类别',
+        'en': 'Task Categories'
+    },
+    'details_title': {
+        'zh': '数据集详情',
+        'en': 'Benchmark Details'
+    }
+}
 
 def wrap_key_words(keywords: list[str]) -> str:
     """
@@ -26,129 +125,33 @@ def process_dictionary(data: dict) -> str:
     """
     return json.dumps(data, ensure_ascii=False, indent=4)
 
+def get_dataset_detail_locale(lang: str) -> Dict[str, str]:
+    """Get localized strings for dataset details"""
+    return {k: v[lang] for k, v in DATASET_DETAIL_LOCALE.items()}
 
-def generate_dataset_markdown(data_adapter: DataAdapter) -> str:
-    """
-    根据DataAdapter实例生成美观的Markdown数据集介绍
-
-    Args:
-        data_adapter (DataAdapter): 数据集适配器实例
-
-    Returns:
-        str: 格式化的Markdown字符串
-    """
-    # 获取基础信息
-    name = data_adapter.name
-    pretty_name = data_adapter.pretty_name or name
-    dataset_id = data_adapter.dataset_id
-    description = data_adapter.description or '暂无详细描述'
-
-
-    # 处理数据集ID的链接格式
-    if dataset_id.startswith(('http://', 'https://')):
-        dataset_id_md = f'[{dataset_id}]({dataset_id})'
-    elif '/' in dataset_id:  # ModelScope格式的ID
-        dataset_id_md = f'[{dataset_id}](https://modelscope.cn/datasets/{dataset_id}/summary)'
-    else:
-        dataset_id_md = dataset_id
-
-    # 构建详情部分
-    details = [
-        f'### {pretty_name}',
-        '',
-        f'[返回目录](#llm评测集)',
-        f'- **数据集名称**: `{name}`',
-        f'- **数据集ID**: {dataset_id_md}',
-        f'- **数据集描述**:  \n  > {description}',
-        f'- **任务类别**: {wrap_key_words(data_adapter.tags)}',
-        f'- **评估指标**: {wrap_key_words(data_adapter.metric_list)}',
-        f"- **需要LLM Judge**: {'是' if data_adapter.llm_as_a_judge else '否'}",
-        f'- **默认提示方式**: {data_adapter.few_shot_num}-shot'
-    ]
-
-    # 添加数据集子集信息
-    if data_adapter.subset_list:
-        details.append(f'- **数据集子集**: {wrap_key_words(data_adapter.subset_list)}')
-
-    # 添加其他技术信息
-    technical_info = [
-        f'- **支持输出格式**: {wrap_key_words(data_adapter.output_types)}',
-    ]
-
-    # 添加额外参数信息
-    extra_params = data_adapter.config_kwargs.get('extra_params', {})
-    if extra_params:
-        technical_info.append(f'- **额外参数**: \n```json\n{process_dictionary(extra_params)}\n```')
-
-    # 添加提示模板
-    if data_adapter.system_prompt:
-        technical_info.append(f'- **系统提示词**: \n```text\n{data_adapter.system_prompt}\n```')
-    if data_adapter.prompt_template:
-        technical_info.append(f'- **提示模板**: \n```text\n{data_adapter.prompt_template}\n```')
+def get_document_locale(lang: str) -> Dict[str, str]:
+    """Get localized strings for document structure"""
+    return {k: v[lang] for k, v in DOCUMENT_LOCALE.items()}
 
-    return '\n'.join(details + [''] + technical_info + [''])
-
-
-def generate_full_documentation(adapters: list[DataAdapter]) -> str:
-    """
-    生成完整的Markdown文档，包含索引和所有数据集详情
-
-    Args:
-        adapters (list[DataAdapter]): DataAdapter实例列表
-
-    Returns:
-        str: 完整的Markdown文档
-    """
-    # 生成索引
-    index = [
-        '# LLM评测集',
-        '',
-        '以下是支持的LLM评测集列表，点击数据集标准名称可跳转详细信息。',
-        '',
-        '| 数据集名称 | 标准名称 | 任务类别 |',
-        '|------------|----------|----------|',
-    ]
-
-    for adapter in adapters:
-        name = adapter.name
-        pretty_name = adapter.pretty_name or name
-        link_name = pretty_name.lower().replace(' ', '-').replace('.', '')
-        tags = wrap_key_words(adapter.tags)
-        index.append(f'| `{name}` | [{pretty_name}](#{link_name}) | {tags} |')
-
-    # 生成详情部分
-    details = [
-        '',
-        '---',
-        '',
-        '## 数据集详情',
-        ''
-    ]
-
-    for i, adapter in enumerate(adapters):
-        details.append(generate_dataset_markdown(adapter))
-        if i < len(adapters) - 1:
-            details.append('---')
-            details.append('')
-
-    return '\n'.join(index + details)
-
-
-def generate_dataset_markdown_en(data_adapter: DataAdapter) -> str:
+def generate_dataset_markdown(data_adapter: DataAdapter, lang: str = 'zh') -> str:
     """
     Generate a well-formatted Markdown benchmark introduction based on a DataAdapter instance
 
     Args:
         data_adapter (DataAdapter): Dataset adapter instance
+        lang (str): Language code ('zh' for Chinese, 'en' for English)
 
     Returns:
         str: Formatted Markdown string
     """
+    # Get localized text
+    text = get_dataset_detail_locale(lang)
+
     # Get basic information
     name = data_adapter.name
     pretty_name = data_adapter.pretty_name or name
     dataset_id = data_adapter.dataset_id
-    description = data_adapter.description or 'No detailed description available'
+    description = data_adapter.description or text['no_description']
 
     # Format dataset ID links
     if dataset_id.startswith(('http://', 'https://')):
@@ -162,57 +165,60 @@ def generate_dataset_markdown_en(data_adapter: DataAdapter) -> str:
     details = [
         f'### {pretty_name}',
         '',
-        f'[Back to Top](#llm-benchmarks)',
-        f'- **Dataset Name**: `{name}`',
-        f'- **Dataset ID**: {dataset_id_md}',
-        f'- **Description**:  \n  > {description}',
-        f'- **Task Categories**: {wrap_key_words(data_adapter.tags)}',
-        f'- **Evaluation Metrics**: {wrap_key_words(data_adapter.metric_list)}',
-        f"- **Requires LLM Judge**: {'Yes' if data_adapter.llm_as_a_judge else 'No'}",
-        f'- **Default Shots**: {data_adapter.few_shot_num}-shot'
+        f'[{text["back_to_top"]}](#{text["toc_title"].lower().replace(" ", "-")})',
+        f'- **{text["dataset_name"]}**: `{name}`',
+        f'- **{text["dataset_id"]}**: {dataset_id_md}',
+        f'- **{text["description"]}**:  \n  > {description}',
+        f'- **{text["task_categories"]}**: {wrap_key_words(data_adapter.tags)}',
+        f'- **{text["evaluation_metrics"]}**: {wrap_key_words(data_adapter.metric_list)}',
+        f'- **{text["requires_llm_judge"]}**: {text["yes"] if data_adapter.llm_as_a_judge else text["no"]}',
+        f'- **{text["default_shots"]}**: {data_adapter.few_shot_num}-shot'
     ]
 
     # Add dataset subsets
     if data_adapter.subset_list:
-        details.append(f'- **Subsets**: {wrap_key_words(data_adapter.subset_list)}')
+        details.append(f'- **{text["subsets"]}**: {wrap_key_words(data_adapter.subset_list)}')
 
     # Add technical information
     technical_info = [
-        f'- **Supported Output Formats**: {wrap_key_words(data_adapter.output_types)}',
+        f'- **{text["supported_output_formats"]}**: {wrap_key_words(data_adapter.output_types)}',
     ]
 
     # Add extra parameters
     extra_params = data_adapter.config_kwargs.get('extra_params', {})
     if extra_params:
-        technical_info.append(f'- **Extra Parameters**: \n```json\n{process_dictionary(extra_params)}\n```')
+        technical_info.append(f'- **{text["extra_parameters"]}**: \n```json\n{process_dictionary(extra_params)}\n```')
 
     # Add prompt templates
     if data_adapter.system_prompt:
-        technical_info.append(f'- **System Prompt**: \n```text\n{data_adapter.system_prompt}\n```')
+        technical_info.append(f'- **{text["system_prompt"]}**: \n```text\n{data_adapter.system_prompt}\n```')
     if data_adapter.prompt_template:
-        technical_info.append(f'- **Prompt Template**: \n```text\n{data_adapter.prompt_template}\n```')
+        technical_info.append(f'- **{text["prompt_template"]}**: \n```text\n{data_adapter.prompt_template}\n```')
 
     return '\n'.join(details + [''] + technical_info + [''])
 
-
-def generate_full_documentation_en(adapters: list[DataAdapter]) -> str:
+def generate_full_documentation(adapters: list[DataAdapter], lang: str = 'zh') -> str:
     """
     Generate complete Markdown documentation with index and all benchmark details
 
     Args:
         adapters (list[DataAdapter]): List of DataAdapter instances
+        lang (str): Language code ('zh' for Chinese, 'en' for English)
 
     Returns:
         str: Complete Markdown document
     """
+    # Get localized text
+    text = get_document_locale(lang)
+
     # Generate index
     index = [
-        '# LLM Benchmarks',
+        f'# {text["title"]}',
         '',
-        'Below is the list of supported LLM benchmarks. Click on a benchmark name to jump to details.',
+        f'{text["intro"]}',
         '',
-        '| Benchmark Name | Pretty Name | Task Categories |',
-        '|----------------|-------------|----------------|',
+        f'| {text["dataset_name"]} | {text["pretty_name"]} | {text["task_categories"]} |',
+        '|------------|----------|----------|',
     ]
 
     for adapter in adapters:
@@ -227,12 +233,12 @@ def generate_full_documentation_en(adapters: list[DataAdapter]) -> str:
         '',
         '---',
         '',
-        '## Benchmark Details',
+        f'## {text["details_title"]}',
         ''
     ]
 
     for i, adapter in enumerate(adapters):
-        details.append(generate_dataset_markdown_en(adapter))
+        details.append(generate_dataset_markdown(adapter, lang))
         if i < len(adapters) - 1:
             details.append('---')
             details.append('')
@@ -253,8 +259,8 @@ def generate_full_documentation_en(adapters: list[DataAdapter]) -> str:
     adapters.sort(key=lambda x: x.name)  # 按名称排序
 
     # 生成完整文档
-    markdown_doc = generate_full_documentation(adapters)
-    markdown_doc_en = generate_full_documentation_en(adapters)
+    markdown_doc = generate_full_documentation(adapters, 'zh')
+    markdown_doc_en = generate_full_documentation(adapters, 'en')
 
     # 输出到文件
     with open('docs/zh/get_started/supported_dataset/llm.md', 'w', encoding='utf-8') as f: