| --- |
| pretty_name: FinBen · FinArg-ACC |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| task_categories: |
| - text-classification |
| size_categories: |
| - n<1K |
| source_datasets: |
| - extended |
| tags: |
| - finance |
| - benchmark |
| - thefinai |
| - finben |
| - flare |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: query |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: text |
| dtype: string |
| - name: choices |
| sequence: string |
| - name: gold |
| dtype: int64 |
| splits: |
| - name: test |
| num_bytes: 549300 |
| num_examples: 969 |
| download_size: 177802 |
| dataset_size: 549300 |
| extra_gated_heading: Request access to FinBen · FinArg-ACC |
| 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-NC-SA 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 · FinArg-ACC |
|
|
| 📄 [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 | argument unit classification | |
| | Original dataset | FinArg-ACC (Sy et al., 2023) | |
| | Evaluation metric | F1, Accuracy | |
| | Source license | CC BY-NC-SA 4.0 | |
| | Language | en | |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TheFinAI/en-finarg-ecc-auc", split="test") |
| print(ds[0]) |
| ``` |
|
|
| ## Example prompt |
|
|
| ```text |
| Analyze sentences from earnings conference calls and identify their argumentative function. Each sentence is either a premise, offering evidence or reasoning, or a claim, asserting a conclusion or viewpoint. Return only premise or claim. |
| Text: And the cost of that then gets amortized to those countries. |
| Answer: |
| ``` |
|
|
| ## Dataset Structure |
|
|
| | Split | Rows | |
| |---|---| |
| | `test` | 969 | |
|
|
| | Field | Description | |
| |---|---| |
| | `id` | Example id | |
| | `query` | Full instruction prompt given to the model | |
| | `answer` | Gold answer / label text | |
| | `text` | Raw input text (without instruction) | |
| | `choices` | Label space | |
| | `gold` | Index of the gold label in `choices` | |
|
|
| ## License |
|
|
| The original data is released under **CC BY-NC-SA 4.0** (FinBen paper, Table 2). |
|
|
| ## Citation |
|
|
| Please cite FinBen and the original dataset (FinArg-ACC (Sy et al., 2023)): |
|
|
| ```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}, |
| } |
| ``` |
|
|
