sora 13274243a0 Bump vendored EvalScope and add K3-ready DPV4 configs.

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>

2026-09-02 07:30:48 +00:00

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BhashaBench-V1 (Ayurveda)

Overview

BhashaBench-Ayur is the predecessor of BhashaBench-Multi's ayur domain: a domain-specific multiple-choice benchmark evaluating LLM knowledge of Ayurvedic medicine, covering English and Hindi.

Task Description

Task Type: Domain-Specific Multiple-Choice Question Answering
Input: An Ayurvedic medicine question with 4 answer choices, in English or Hindi
Output: Correct answer letter
Languages: English, Hindi

Key Features

5,600–17,000 questions per language, covering English and Hindi only
Predecessor of BhashaBench-Multi: same domains, narrower language coverage
Each domain is a separate repository, with English and Hindi as separate configs

Evaluation Notes

Default configuration uses 0-shot evaluation (test split, the only split available)
Use subset_list to evaluate a single language (e.g., ['Hindi'])
Requires access to this gated dataset - on ModelScope (the default hub), accept the terms and ensure you're logged in; alternatively, set dataset_hub to huggingface and use HF_TOKEN after accepting the terms on huggingface.co
For broader language coverage of the same domain, see bhasha_bench_multi_ayur (22 Indic languages, not gated)

Properties

Property	Value
Benchmark Name	`bhashabenchv1_ayur`
Dataset ID	bharatgenai/BhashaBench-Ayur
Paper	N/A
Tags	`Knowledge`, `MCQ`, `MultiLingual`
Metrics	`accuracy`
Default Shots	0-shot
Evaluation Split	`test`

Data Statistics

Metric	Value
Total Samples	14,963
Prompt Length (Mean)	307.52 chars
Prompt Length (Min/Max)	222 / 1060 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`English`	9,348	310.35	227	1060
`Hindi`	5,615	302.81	222	938

Sample Example

Subset: English

{
  "input": [
    {
      "id": "6a6db8b7",
      "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"
    }
  ],
  "choices": [
    "Hypersensitivity",
    "auto-immune diseases",
    "immunodeficiency",
    "all of these"
  ],
  "target": "D",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "language": "English",
    "topic": "Kayachikitsa"
  }
}

Prompt Template

Prompt Template:

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}.

{question}

{choices}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets bhashabenchv1_ayur \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['bhashabenchv1_ayur'],
    dataset_args={
        'bhashabenchv1_ayur': {
            # subset_list: ['English', 'Hindi']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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