144 lines
3.6 KiB
Markdown
144 lines
3.6 KiB
Markdown
# ChartQA
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## Overview
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ChartQA is a benchmark designed to evaluate question-answering capabilities over charts and data visualizations. It tests both visual reasoning and logical understanding of various chart types including bar charts, line graphs, and pie charts.
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## Task Description
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- **Task Type**: Chart Question Answering
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- **Input**: Chart image + natural language question
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- **Output**: Single word or numerical answer
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- **Domains**: Data visualization, visual reasoning, numerical reasoning
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## Key Features
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- Covers diverse chart types (bar, line, pie, scatter plots)
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- Includes both human-written and augmented test questions
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- Requires understanding of chart structure and data relationships
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- Tests both visual extraction and logical reasoning abilities
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- Questions range from simple data lookup to complex reasoning
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## Evaluation Notes
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- Default evaluation uses the **test** split with two subsets:
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- `human_test`: Human-written questions
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- `augmented_test`: Automatically generated questions
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- Primary metric: **Relaxed Accuracy** (allows minor variations in answers)
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- Answers should be in format "ANSWER: [ANSWER]"
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- Numerical answers may have tolerance for rounding differences
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `chartqa` |
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| **Dataset ID** | [lmms-lab/ChartQA](https://modelscope.cn/datasets/lmms-lab/ChartQA/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MultiModal`, `QA` |
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| **Metrics** | `relaxed_acc` |
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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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| Metric | Value |
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|--------|-------|
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| Total Samples | 2,500 |
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| Prompt Length (Mean) | 224.33 chars |
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| Prompt Length (Min/Max) | 178 / 352 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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| `human_test` | 1,250 | 220.17 | 178 | 352 |
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| `augmented_test` | 1,250 | 228.49 | 186 | 293 |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 2,500 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 184x326 - 800x1796 |
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| Formats | png |
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## Sample Example
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**Subset**: `human_test`
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```json
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{
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"input": [
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{
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"id": "c75439e0",
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"content": [
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{
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"text": "\nHow many food item is shown in the bar graph?\n\nThe last line of your response should be of the form \"ANSWER: [ANSWER]\" (without quotes) where [ANSWER] is the a single word answer or number to the problem.\n"
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},
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{
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"image": "[BASE64_IMAGE: png, ~42.9KB]"
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}
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]
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}
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],
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"target": "14",
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"id": 0,
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"group_id": 0,
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"subset_key": "human_test"
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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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{question}
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The last line of your response should be of the form "ANSWER: [ANSWER]" (without quotes) where [ANSWER] is the a single word answer or number to the problem.
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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 chartqa \
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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=['chartqa'],
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dataset_args={
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'chartqa': {
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# subset_list: ['human_test', 'augmented_test'] # 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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