---
license: cc-by-4.0
task_categories:
  - question-answering
  - visual-question-answering
language:
  - zh
  - en
tags:
  - finance
  - multimodal
  - benchmark
  - vqa
  - video
pretty_name: UniFinEval
size_categories:
  - 1K<n<10K
---

# UniFinEval

**UniFinEval** is a unified multimodal benchmark for high-information-density financial environments, covering text, images, and videos. It evaluates multimodal large language models (MLLMs) on fine-grained financial reasoning tasks grounded in real-world financial systems.

> Paper: *UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos*  
> Code: [github.com/aifinlab/UniFinEval](https://github.com/aifinlab/UniFinEval)

## Dataset Summary

| Item | Value |
|------|-------|
| Total samples | 2,515 |
| Total image files | 6,568 (referenced) |
| Languages | Chinese (2,154), English (361) |
| Question types | Single-round QA (1,341), Multi-round QA (1,174) |
| Multi-image samples | 311 |

### Financial Scenarios

| Scenario | Count |
|----------|------:|
| 金融风险感知 (Financial Risk Sensing) | 738 |
| 资产配置分析 (Asset Allocation Analysis) | 494 |
| 公司基本面推导 (Company Fundamental Reasoning) | 480 |
| 财务报告审计 (Financial Statement Auditing) | 438 |
| 产业趋势洞察 (Industry Trend Insights) | 365 |

### Image Types

| `image_type` | Count | Description |
|--------------|------:|-------------|
| `pure_image` | 858 | Chart/table screenshots |
| `mixed` | 569 | Text + image combined |
| `pure_text` | 553 | Text-only financial documents |
| `splice_distractor` | 213 | Spliced distractor samples |
| `single_video` | 160 | Single-video frame sequences |
| `multi_video` | 150 | Multi-video frame sequences |
| `stacked` | 11 | Stacked layout samples |
| `video_image` | 1 | Video + image hybrid |

### Difficulty Levels

| Level | Count |
|-------|------:|
| L1 | 606 |
| L2 | 975 |
| L3 | 440 |
| L4 | 494 |

## Repository Structure

```text
.
├── README.md
├── unifineval-data.csv       # Metadata (questions, answers, paths)
└── organized_dataset/        # Images organized by type and question_id
    ├── mixed/
    │   └── mixed_130/
    │       └── 0004.png
    ├── pure_image/
    ├── single_video/
    └── ...
```

## Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `question_id` | string | Unique question identifier |
| `round` | string | Round index for multi-round questions (empty for single-round) |
| `question` | string | Question text |
| `answer` | string | Reference answer |
| `question_type` | string | `问答题` (single-round) or `多轮问答题` (multi-round) |
| `image_type` | string | Modality/layout category (see table above) |
| `image_path` | string | Relative path to image(s) from repo root; multiple paths separated by `; ` |
| `profile` | string | Evaluator profile (`expert`) |
| `scenario` | string | Financial scenario category |
| `difficulty` | string | Difficulty level (`L1`–`L4`) |
| `language` | string | `cn` or `en` |

## Usage

### Load metadata

```python
import pandas as pd
from pathlib import Path

repo_root = Path(".")  # or downloaded HF repo path
df = pd.read_csv(repo_root / "unifineval-data.csv")
print(df.head())
```

### Resolve image paths

```python
import re
from pathlib import Path

def split_image_paths(raw: str) -> list[str]:
    if not raw or not str(raw).strip():
        return []
    return [p.strip() for p in re.split(r"\s*;\s*|\s*\|\s*", str(raw).strip()) if p.strip()]

repo_root = Path(".")
row = df.iloc[0]
image_files = [repo_root / p for p in split_image_paths(row["image_path"])]
for img in image_files:
    assert img.is_file(), img
```

### Download from Hugging Face Hub

```python
from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="AIFin-Lab/UniFinEval",
    repo_type="dataset",
)
```

## Evaluation Notes

- **Multi-image rows**: 311 samples contain multiple images in `image_path` (semicolon-separated). Feed all frames/images to the model when evaluating video-derived tasks.
- **Multi-round rows**: Questions sharing the same `question_id` with different `round` values belong to the same multi-turn conversation.
- **Zero-shot / CoT**: See the paper and [GitHub repository](https://github.com/aifinlab/UniFinEval) for evaluation protocols.

## Limitations

- This release contains **2,515** curated samples with verified local image paths. The full benchmark described in the paper includes additional QA pairs across modalities.
- Images are derived from financial research reports, earnings materials, and video frames. Users should comply with applicable copyright and usage policies.

## Citation

If you use UniFinEval, please cite:

```bibtex
@article{unifineval2025,
  title   = {UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos},
  author  = {},  % fill in from paper
  journal = {},  % fill in from paper
  year    = {2025}
}
```

## License

This dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).  
Please replace the license above if your institution requires a different one.

## Upload (maintainers)

From the project root:

```bash
export HF_TOKEN=hf_xxx   # never commit this token

# Default: try Tsinghua mirror, auto-fallback to hf-mirror.com if unavailable
bash upload_to_hf.sh

# Or use hf-mirror directly (recommended in China)
bash upload_to_hf.sh --mirror hf-mirror
```

Dry run (validate paths only):

```bash
python3 upload_to_hf.py --dry-run
```

**Note:** Tsinghua TUNA (`mirrors.tuna.tsinghua.edu.cn/huggingface`) no longer provides a working Hugging Face mirror (HTTP 404). The upload script will automatically fall back to [hf-mirror.com](https://hf-mirror.com) unless `--no-fallback` is set.