13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
129 lines
3.8 KiB
Markdown
129 lines
3.8 KiB
Markdown
# BhashaBench-V1 (Legal)
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## 概述
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BhashaBench-Legal 是 BhashaBench-Multi 法律领域的前身：这是一个领域特定的多项选择题基准测试，用于评估大语言模型对印度法律的知识，涵盖英语和印地语。
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## 任务描述
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- **任务类型**：领域特定的多项选择题问答
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- **输入**：一道包含4个选项的印度法律问题，语言为英语或印地语
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- **输出**：正确答案的字母
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- **语言**：英语、印地语
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## 主要特点
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- 每种语言包含5,600至17,000道题目，仅涵盖英语和印地语
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- 作为 BhashaBench-Multi 的前身：领域相同，但语言覆盖范围更窄
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- 每个领域对应一个独立的代码仓库，英语和印地语分别作为独立的配置项
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## 评估说明
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- 默认配置使用 **0-shot** 评估（仅提供 test 分割）
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- 使用 `subset_list` 可评估单一语言（例如 `['Hindi']`）
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- 需要访问此受限制的数据集——在 ModelScope（默认 Hub）上，请先接受条款并确保已登录；或者将 `dataset_hub` 设置为 `huggingface`，并在 huggingface.co 上接受条款后使用 `HF_TOKEN`
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- 如需同一领域但更广泛的语言覆盖，请参见 `bhasha_bench_multi_legal`（涵盖22种印度语言，无需授权）
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `bhashabenchv1_legal` |
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| **数据集ID** | [bharatgenai/BhashaBench-Legal](https://modelscope.cn/datasets/bharatgenai/BhashaBench-Legal/summary) |
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| **论文** | 无 |
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| **标签** | `Knowledge`, `MCQ`, `MultiLingual` |
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| **指标** | `accuracy` |
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| **默认示例数** | 0-shot |
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| **评估分割** | `test` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 24,365 |
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| 提示词长度（平均） | 513.88 字符 |
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| 提示词长度（最小/最大） | 229 / 4628 字符 |
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**各子集统计数据：**
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| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |
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|--------|---------|-------------|------------|------------|
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| `English` | 17,047 | 539.36 | 233 | 4628 |
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| `Hindi` | 7,318 | 454.52 | 229 | 1748 |
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## 样例示例
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**子集**: `English`
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```json
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{
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"input": [
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{
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"id": "6e1ae42b",
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"content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nPower to amend the issue or frame additional issues prior to passing of a decree vests in a Court by virtue of which provision of the Code of Civil Procedure, 1908?\n\nA) Order XIV Rule 1\nB) Order XIV Rule 5\nC) Order XIV Rule 6\nD) Section 151"
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}
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],
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"choices": [
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"Order XIV Rule 1",
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"Order XIV Rule 5",
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"Order XIV Rule 6",
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"Section 151"
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],
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"target": "B",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"language": "English",
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"topic": "Procedural Law"
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}
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}
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```
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## 提示模板
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**提示模板：**
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```text
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Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.
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{question}
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{choices}
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```
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## 使用方法
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### 使用 CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets bhashabenchv1_legal \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['bhashabenchv1_legal'],
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dataset_args={
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'bhashabenchv1_legal': {
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# subset_list: ['English', 'Hindi'] # 可选，用于评估特定子集
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}
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},
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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```
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