91 lines
2.0 KiB
Markdown
91 lines
2.0 KiB
Markdown
# General-QA
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## Overview
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General-QA is a customizable question answering benchmark for evaluating language models on open-ended text generation tasks. It supports flexible data formats and configurable evaluation metrics.
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## Task Description
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- **Task Type**: Open-Ended Question Answering
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- **Input**: Question (with optional system prompt and conversation history)
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- **Output**: Free-form text answer
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- **Flexibility**: Supports custom datasets via local files
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## Key Features
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- Flexible input format (query/answer or messages format)
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- Optional system prompt support
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- BLEU and Rouge evaluation metrics
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- Custom dataset support via local file loading
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- Extensible for various QA use cases
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Default metrics: **BLEU**, **Rouge** (Rouge-L-R as main score)
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- Evaluates on **test** split
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- See [User Guide](https://evalscope.readthedocs.io/en/latest/advanced_guides/custom_dataset/llm.html#qa) for dataset format
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `general_qa` |
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| **Dataset ID** | `general_qa` |
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| **Paper** | N/A |
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| **Tags** | `Custom`, `QA` |
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| **Metrics** | `BLEU`, `Rouge` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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*Statistics not available.*
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## Sample Example
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*Sample example not available.*
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## Prompt Template
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**Prompt Template:**
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```text
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请回答问题
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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 general_qa \
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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=['general_qa'],
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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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