128 lines
3.3 KiB
Markdown
128 lines
3.3 KiB
Markdown
# DrivelologyMultilabelClassification
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## Overview
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Drivelology Multi-label Classification evaluates models' ability to categorize "drivelology" text into rhetorical technique categories: inversion, wordplay, switchbait, paradox, and misdirection. Each text may belong to multiple categories.
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## Task Description
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- **Task Type**: Multi-label Text Classification
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- **Input**: Drivelology text sample
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- **Output**: One or more technique categories
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- **Domain**: Linguistic analysis, rhetorical technique detection
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## Key Features
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- Five rhetorical technique categories
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- Multi-label classification (multiple categories per sample)
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- Tests understanding of linguistic creativity mechanisms
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- Requires recognition of humor and irony techniques
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- Detailed category definitions provided
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Metrics: F1 (weighted, micro, macro), Exact Match
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- Aggregation method: F1 weighted
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- Categories: inversion, wordplay, switchbait, paradox, misdirection
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `drivel_multilabel` |
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| **Dataset ID** | [extraordinarylab/drivel-hub](https://modelscope.cn/datasets/extraordinarylab/drivel-hub/summary) |
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| **Paper** | N/A |
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| **Tags** | `MCQ` |
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| **Metrics** | `f1_weighted`, `f1_micro`, `f1_macro`, `exact_match` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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| **Aggregation** | `f1_weighted` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 600 |
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| Prompt Length (Mean) | 1637.18 chars |
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| Prompt Length (Min/Max) | 1580 / 2041 chars |
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## Sample Example
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**Subset**: `multi-label-classification`
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```json
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{
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"input": [
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{
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"id": "0e8acb03",
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"content": [
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{
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"text": "#Instruction#:\nClassify the given text into one or more of the following categories: inversion, wordplay, switchbait, paradox, and misdirection.\n\n#Definitions#:\n- inversion: This technique takes a well-known phrase, cliché, or social script a ... [TRUNCATED] ... e should be of the following format: 'ANSWER: [LETTERS]' (without quotes) where [LETTER]S is one or more of A,B,C,D,E.\n\nText to classify: 後天的努力比什麼都重要，所以今天和明天休息。\n\nA) A. inversion\nB) B. wordplay\nC) C. switchbait\nD) D. paradox\nE) E. misdirection"
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}
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]
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}
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],
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"choices": [
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"A. inversion",
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"B. wordplay",
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"C. switchbait",
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"D. paradox",
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"E. misdirection"
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],
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"target": "AB",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"text": "後天的努力比什麼都重要，所以今天和明天休息。",
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"label": [
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"inversion",
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"wordplay"
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],
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"target_letters": "AB"
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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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{question}
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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 drivel_multilabel \
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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=['drivel_multilabel'],
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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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