| --- |
| pretty_name: FinBen · German (credit scoring) |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - text-classification |
| size_categories: |
| - 1K<n<10K |
| source_datasets: |
| - extended |
| tags: |
| - finance |
| - benchmark |
| - thefinai |
| - finben |
| - flare |
| dataset_info: |
| features: |
| - name: id |
| dtype: int64 |
| - name: query |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: choices |
| sequence: string |
| - name: gold |
| dtype: int64 |
| - name: text |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 2416596 |
| num_examples: 700 |
| - name: valid |
| num_bytes: 345236 |
| num_examples: 100 |
| - name: test |
| num_bytes: 690454 |
| num_examples: 200 |
| download_size: 426827 |
| dataset_size: 3452286 |
| extra_gated_heading: Request access to FinBen · German (credit scoring) |
| extra_gated_description: This FinBen task is released by The Fin AI for research. Access is granted automatically after you complete this short form. |
| extra_gated_button_content: Agree and access |
| extra_gated_prompt: By accessing this dataset you agree to the license of the original source (CC BY 4.0) and to cite the FinBen paper and the original dataset in any resulting publication. |
| extra_gated_fields: |
| Full name: text |
| Affiliation: text |
| Country: country |
| Intended use: |
| type: select |
| options: |
| - Research |
| - Education |
| - Commercial evaluation |
| - Other |
| I agree to the terms above and will cite the papers: checkbox |
| --- |
| |
| # FinBen · German (credit scoring) |
|
|
| 📄 [Paper](https://arxiv.org/abs/2402.12659) · 💻 [Code](https://github.com/The-FinAI/FinBen) · 🏆 [Leaderboard](https://huggingface.co/spaces/TheFinAI/Open-FinLLM-Leaderboard) · 🌐 [The Fin AI](https://thefin.ai) |
|
|
| > Part of **FinBen** — *FinBen: A Holistic Financial Benchmark for Large Language Models* ([arXiv:2402.12659](https://arxiv.org/abs/2402.12659)). |
|
|
| | | | |
| |---|---| |
| | Task | credit scoring | |
| | Original dataset | German (Hofmann, 1994) | |
| | Evaluation metric | F1, MCC | |
| | Source license | CC BY 4.0 | |
| | Language | en | |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TheFinAI/en-german", split="test") |
| print(ds[0]) |
| ``` |
|
|
| ## Example prompt |
|
|
| ```text |
| Assess the creditworthiness of a customer using the following table attributes for financial status. Respond with either 'good' or 'bad'. And the table attributes including 13 categorical attributes and 7 numerical attributes are as follows: |
| X1: Status of existing checking account (qualitative): A11: smaller than 0 DM, A12: bigger than 0 DM but smaller than 200 DM, A13: bigger than 200 DM OR sala… |
| ``` |
|
|
| ## Dataset Structure |
|
|
| | Split | Rows | |
| |---|---| |
| | `train` | 700 | |
| | `test` | 200 | |
| | `valid` | 100 | |
|
|
| | Field | Description | |
| |---|---| |
| | `id` | Example id | |
| | `query` | Full instruction prompt given to the model | |
| | `answer` | Gold answer / label text | |
| | `choices` | Label space | |
| | `gold` | Index of the gold label in `choices` | |
| | `text` | Raw input text (without instruction) | |
|
|
| ## License |
|
|
| The original data is released under **CC BY 4.0** (FinBen paper, Table 2). |
|
|
| ## Citation |
|
|
| Please cite FinBen and the original dataset (German (Hofmann, 1994)): |
|
|
| ```bibtex |
| @misc{xie2024finbenholisticfinancialbenchmark, |
| title={FinBen: A Holistic Financial Benchmark for Large Language Models}, |
| author={Qianqian Xie and Weiguang Han and Zhengyu Chen and Ruoyu Xiang and Xiao Zhang and Yueru He and Mengxi Xiao and Dong Li and Yongfu Dai and Duanyu Feng and Yijing Xu and Haoqiang Kang and Ziyan Kuang and Chenhan Yuan and Kailai Yang and Zheheng Luo and Tianlin Zhang and Zhiwei Liu and Guojun Xiong and Zhiyang Deng and Yuechen Jiang and Zhiyuan Yao and Haohang Li and Yangyang Yu and Gang Hu and Jiajia Huang and Xiao-Yang Liu and Alejandro Lopez-Lira and Benyou Wang and Yanzhao Lai and Hao Wang and Min Peng and Sophia Ananiadou and Jimin Huang}, |
| year={2024}, |
| eprint={2402.12659}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2402.12659}, |
| } |
| ``` |
|
|
