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>
136 lines
4.3 KiB
Markdown
136 lines
4.3 KiB
Markdown
# BoolQ-Indic
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## Overview
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BoolQ-Indic is a translation of the BoolQ yes/no reading-comprehension benchmark into 10 Indic
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languages plus English, for evaluating multilingual passage understanding.
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## Task Description
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- **Task Type**: Multilingual Yes/No Reading Comprehension
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- **Input**: Passage + yes/no question in one of 11 languages
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- **Output**: `Yes` or `No`
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- **Languages**: Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation (validation split)
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- Use `subset_list` to evaluate specific languages (e.g., `['hi', 'ta']`), or `limit` to cap sample
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count — the full default run is 35,970 samples across all 11 languages
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- Set `few_shot_num` > 0 to enable few-shot prompting; examples are drawn from the `train` split
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- All languages ship in a single dataset config; this adapter reformats by the `language` field
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `indic_boolq` |
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| **Dataset ID** | [sarvamai/boolq-indic](https://modelscope.cn/datasets/sarvamai/boolq-indic/summary) |
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| **Paper** | N/A |
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| **Tags** | `MCQ`, `MultiLingual`, `ReadingComprehension` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `validation` |
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| **Train Split** | `train` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 35,970 |
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| Prompt Length (Mean) | 822.66 chars |
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| Prompt Length (Min/Max) | 275 / 5035 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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| `bn` | 3,270 | 801.95 | 294 | 2308 |
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| `en` | 3,270 | 814.26 | 292 | 5035 |
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| `gu` | 3,270 | 793.37 | 283 | 2105 |
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| `hi` | 3,270 | 818.47 | 297 | 3078 |
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| `kn` | 3,270 | 833.99 | 275 | 2920 |
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| `ml` | 3,270 | 869.99 | 294 | 3558 |
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| `mr` | 3,270 | 806.68 | 289 | 2593 |
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| `or` | 3,270 | 787.68 | 306 | 1482 |
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| `pa` | 3,270 | 804.93 | 295 | 1975 |
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| `ta` | 3,270 | 904.01 | 297 | 3570 |
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| `te` | 3,270 | 813.95 | 284 | 3312 |
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## Sample Example
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**Subset**: `bn`
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```json
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{
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"input": [
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{
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"id": "0f04f0f7",
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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.\n\nসকল জৈববস্তুই কমপক্ষে এই ধাপগুলোর মধ্য দিয়ে যায়: এগুলো চা ... [TRUNCATED 1099 chars] ... বার্কলেতে ছয়টি পৃথক গবেষণা বিশ্লেষণ করার পর, একটি গবেষণায় উপসংহারে আসা গেছে যে, ভুট্টা থেকে ইথানল উৎপাদনে পেট্রোলিয়ামের ব্যবহার গ্যাসোলিন উৎপাদনের তুলনায় অনেক কম।\n\nQuestion: ইথানল উৎপাদনের চেয়ে তৈরিতে কি বেশি শক্তি লাগে??\n\nA) Yes\nB) No"
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}
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],
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"choices": [
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"Yes",
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"No"
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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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"subset_key": "bn",
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"metadata": {
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"language": "Bengali"
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}
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}
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```
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*Note: Some content was truncated for display.*
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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 indic_boolq \
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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=['indic_boolq'],
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dataset_args={
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'indic_boolq': {
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# subset_list: ['bn', 'en', 'gu'] # 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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