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>
134 lines
3.7 KiB
Markdown
134 lines
3.7 KiB
Markdown
# BhashaBench-V1 (Ayurveda)
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## Overview
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BhashaBench-Ayur is the predecessor of BhashaBench-Multi's ayur domain: a domain-specific
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multiple-choice benchmark evaluating LLM knowledge of Ayurvedic medicine, covering English and Hindi.
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## Task Description
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- **Task Type**: Domain-Specific Multiple-Choice Question Answering
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- **Input**: An Ayurvedic medicine question with 4 answer choices, in English or Hindi
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- **Output**: Correct answer letter
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- **Languages**: English, Hindi
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## Key Features
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- 5,600–17,000 questions per language, covering English and Hindi only
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- Predecessor of BhashaBench-Multi: same domains, narrower language coverage
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- Each domain is a separate repository, with English and Hindi as separate configs
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation (test split, the only split available)
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- Use `subset_list` to evaluate a single language (e.g., `['Hindi']`)
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- Requires access to this gated dataset - on ModelScope (the default hub), accept the terms and
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ensure you're logged in; alternatively, set `dataset_hub` to `huggingface` and use `HF_TOKEN`
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after accepting the terms on huggingface.co
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- For broader language coverage of the same domain, see `bhasha_bench_multi_ayur`
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(22 Indic languages, not gated)
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `bhashabenchv1_ayur` |
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| **Dataset ID** | [bharatgenai/BhashaBench-Ayur](https://modelscope.cn/datasets/bharatgenai/BhashaBench-Ayur/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MCQ`, `MultiLingual` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 14,963 |
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| Prompt Length (Mean) | 307.52 chars |
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| Prompt Length (Min/Max) | 222 / 1060 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `English` | 9,348 | 310.35 | 227 | 1060 |
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| `Hindi` | 5,615 | 302.81 | 222 | 938 |
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## Sample Example
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**Subset**: `English`
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```json
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{
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"input": [
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{
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"id": "6a6db8b7",
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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\nImmune disorders include .....,\n\nA) Hypersensitivity\nB) auto-immune diseases\nC) immunodeficiency\nD) all of these"
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}
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],
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"choices": [
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"Hypersensitivity",
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"auto-immune diseases",
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"immunodeficiency",
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"all of these"
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],
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"target": "D",
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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": "Kayachikitsa"
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}
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}
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```
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## Prompt Template
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**Prompt Template:**
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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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## Usage
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### Using 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_ayur \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using 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_ayur'],
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dataset_args={
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'bhashabenchv1_ayur': {
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# subset_list: ['English', 'Hindi'] # optional, evaluate specific subsets
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}
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},
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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