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>
132 lines
3.7 KiB
Markdown
132 lines
3.7 KiB
Markdown
# Sanskriti
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## Overview
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Sanskriti is a multiple-choice trivia benchmark testing knowledge of Indian states' culture, history,
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and geography, sourced from state-specific attributes (art, cuisine, festivals, etc.) with
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Wikipedia-backed answers. From the SANSKRITI paper (arXiv:2506.15355); this adapter loads the
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dataset mirrored to ModelScope as `evalscope/Sanskriti`.
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## Task Description
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- **Task Type**: Multiple-Choice Trivia Question Answering
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- **Input**: A question about a specific Indian state's culture/geography/history, with 4 answer choices
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- **Output**: Correct answer letter
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- **Subsets**: `association` (state-attribute association trivia), `country` (country-level trivia),
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`gk` (general knowledge), `states` (state-identification trivia)
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation (the dataset's only split, named `train` upstream
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despite being evaluation data)
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- Questions and choices are in English
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- The paper acknowledges some questions involve ambiguous cultural elements; a small number of rows
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(~0.6%) whose `answer` doesn't match any of the 4 listed options are skipped at load time
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `sanskriti` |
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| **Dataset ID** | [evalscope/Sanskriti](https://modelscope.cn/datasets/evalscope/Sanskriti/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MCQ` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `train` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 21,726 |
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| Prompt Length (Mean) | 322.93 chars |
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| Prompt Length (Min/Max) | 256 / 636 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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| `association` | 5,453 | 343.41 | 273 | 523 |
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| `country` | 5,563 | 284.48 | 256 | 417 |
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| `gk` | 5,328 | 346.94 | 263 | 547 |
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| `states` | 5,382 | 318.17 | 260 | 636 |
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## Sample Example
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**Subset**: `association`
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```json
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{
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"input": [
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{
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"id": "0629b222",
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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\nWhich of the given regions is home to the Jarawa body painting?\n\nA) Surguja district\nB) South Andaman and Middle Andaman Islands\nC) Buddha Marg, Patna\nD) Telangana"
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}
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],
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"choices": [
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"Surguja district",
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"South Andaman and Middle Andaman Islands",
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"Buddha Marg, Patna",
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"Telangana"
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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": "association",
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"metadata": {
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"state": "Andaman_and_Nicobar",
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"attribute": "Art"
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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 sanskriti \
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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=['sanskriti'],
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dataset_args={
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'sanskriti': {
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# subset_list: ['association', 'country', 'gk'] # 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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