README.md

---
pretty_name: FinBen · CD (causal detection)
license: cc-by-4.0
language:
- en
task_categories:
- token-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: label
    sequence: string
  - name: token
    sequence: string
  splits:
  - name: test
    num_bytes: 620510
    num_examples: 226
  download_size: 194949
  dataset_size: 620510
extra_gated_heading: Request access to FinBen · CD (causal detection)
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 · CD (causal detection)

📄 [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 | causal detection |
| Original dataset | CD (Mariko et al., 2020) |
| Evaluation metric | F1, Entity F1 |
| Source license | CC BY 4.0 |
| Language | en |

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("TheFinAI/en-cd", split="test")
print(ds[0])
```

## Example prompt

```text
Your job in this task is to perform sequence labeling on a provided text section, marking the chunks that represent the cause of an event and the effects that result from it. For each token in the text, assign a label to indicate its role in representing cause or effect. The labels you should use are 'B-CAUSE', 'I-CAUSE', 'B-EFFECT', 'I-EFFECT', and 'O'. A 'B-' prefix is used to denote the beginni…
```

## Dataset Structure

| Split | Rows |
|---|---|
| `test` | 226 |

| Field | Description |
|---|---|
| `id` | Example id |
| `query` | Full instruction prompt given to the model |
| `answer` | Gold answer / label text |
| `text` | Raw input text (without instruction) |
| `label` | Gold label(s) |
| `token` |  |

## License

The original data is released under **CC BY 4.0** (FinBen paper, Table 2).

## Citation

Please cite FinBen and the original dataset (CD (Mariko et al., 2020)):

```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},
}
```