BhashaBench-V1 (Krishi)

Overview

BhashaBench-Krishi is the predecessor of BhashaBench-Multi’s krishi domain: a domain-specific multiple-choice benchmark evaluating LLM knowledge of agriculture (Krishi), covering English and Hindi.

Task Description

• Task Type: Domain-Specific Multiple-Choice Question Answering

• Input: An agriculture (Krishi) 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_krishi (22 Indic languages, not gated)

Properties

Property	Value
Benchmark Name	bhashabenchv1_krishi
Dataset ID	bharatgenai/BhashaBench-Krishi
Paper	N/A
Tags	Knowledge, MCQ, MultiLingual
Metrics	accuracy
Default Shots	0-shot
Evaluation Split	test

Data Statistics

Metric	Value
Total Samples	15,405
Prompt Length (Mean)	409.45 chars
Prompt Length (Min/Max)	223 / 1841 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
English	12,648	429.18	223	1841
Hindi	2,757	318.93	233	678

Sample Example

Subset: English

{
  "input": [
    {
      "id": "afa2a6e0",
      "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\nIt is state or condition of atmosphere at given place and given time.?\n\nA) Climate\nB) Weather\nC) Environment\nD) Atmosphere"
    }
  ],
  "choices": [
    "Climate",
    "Weather",
    "Environment",
    "Atmosphere"
  ],
  "target": "B",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "language": "English",
    "topic": ""
  }
}

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_krishi \
    --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_krishi'],
    dataset_args={
        'bhashabenchv1_krishi': {
            # subset_list: ['English', 'Hindi']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)