124 lines
3.2 KiB
Markdown
124 lines
3.2 KiB
Markdown
# DrivelologyBinaryClassification
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## Overview
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Drivelology Binary Classification evaluates models' ability to identify "drivelology" - a unique linguistic phenomenon characterized as "nonsense with depth." These are utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive.
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## Task Description
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- **Task Type**: Binary Text Classification (Yes/No)
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- **Input**: Text sample to classify
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- **Output**: "Yes" if drivelology, "No" otherwise
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- **Domain**: Linguistic analysis, humor detection, pragmatics
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## Key Features
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- Tests understanding of layered linguistic meanings
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- Distinguishes nonsense-with-depth from pure nonsense and normal text
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- Requires contextual understanding and emotional insight
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- Covers humor, irony, sarcasm detection
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- Multiple difficulty levels available
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Metrics: Accuracy, Precision, Recall, F1-Score
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- Subsets: binary-english-easy, binary-english-hard, binary-chinese-easy, binary-chinese-hard
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `drivel_binary` |
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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** | `Yes/No` |
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| **Metrics** | `accuracy`, `precision`, `recall`, `f1_score`, `yes_ratio` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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| **Aggregation** | `f1` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 1,200 |
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| Prompt Length (Mean) | 1056.08 chars |
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| Prompt Length (Min/Max) | 984 / 1449 chars |
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## Sample Example
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**Subset**: `binary-classification`
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```json
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{
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"input": [
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{
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"id": "ddbda8da",
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"content": [
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{
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"text": "#Instruction#:\nClassify whether the given text is a Drivelology sample or not.\n\n#Definition#:\n- Drivelology: Statements that appear logically coherent but contain deeper, often paradoxical meanings.\nThese challenge conventional interpretation ... [TRUNCATED] ... ology.\n\n#Output Format#:\nYou should try your best to answer \"Yes\" if the given input text is Drivelology, otherwise specify \"No\".\nThe answer you give MUST be \"Yes\" or \"No\"\".\n\n#Input Text#: A: Name? B: Henry. A: Age? B: E-N-R-Y.\n#Your Answer#:"
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}
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]
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}
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],
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"target": "YES",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"answer": "YES"
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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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<details>
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<summary>Few-shot Template</summary>
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```text
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{question}
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```
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</details>
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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_binary \
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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_binary'],
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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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