BhashaBench-V1 (Finance)#

Overview#

BhashaBench-Finance is the predecessor of BhashaBench-Multi’s finance domain: a domain-specific multiple-choice benchmark evaluating LLM knowledge of finance, covering English and Hindi.

Task Description#

Task Type: Domain-Specific Multiple-Choice Question Answering
Input: A finance 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_finance (22 Indic languages, not gated)

Properties#

Property	Value
Benchmark Name	`bhashabenchv1_finance`
Dataset ID	bharatgenai/BhashaBench-Finance
Paper	N/A
Tags	`Knowledge`, `MCQ`, `MultiLingual`
Metrics	`accuracy`
Default Shots	0-shot
Evaluation Split	`test`

Data Statistics#

Metric	Value
Total Samples	19,433
Prompt Length (Mean)	612.82 chars
Prompt Length (Min/Max)	221 / 6665 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`English`	13,451	663.98	223	6665
`Hindi`	5,982	497.79	221	3304

Sample Example#

Subset: English

{
  "input": [
    {
      "id": "befc8699",
      "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\nIn the following number series. One number is wrong. Find the wrong number of the series? 3, 4, 12, 38, 103, 228\n\nA) 103\nB) 12\nC) 38\nD) 228"
    }
  ],
  "choices": [
    "103",
    "12",
    "38",
    "228"
  ],
  "target": "C",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "language": "English",
    "topic": "Quantitative Aptitude"
  }
}

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_finance \
    --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_finance'],
    dataset_args={
        'bhashabenchv1_finance': {
            # subset_list: ['English', 'Hindi']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)