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FinBen · FPB (Financial PhraseBank)

📄 Paper · 💻 Code · 🏆 Leaderboard · 🌐 The Fin AI

Part of FinBen — FinBen: A Holistic Financial Benchmark for Large Language Models (arXiv:2402.12659).

Task sentiment analysis
Original dataset FPB (Malo et al., 2014)
Evaluation metric F1, Accuracy
Source license CC BY-SA 3.0
Language en

Quick Start

from datasets import load_dataset

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

Example prompt

Analyze the sentiment of this statement extracted from a financial news article. Provide your answer as either negative, positive, or neutral.
Text: The new agreement , which expands a long-established cooperation between the companies , involves the transfer of certain engineering and documentation functions from Larox to Etteplan .
Answer:

Dataset Structure

Split Rows
train 3,100
validation 776
test 970
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-SA 3.0 (FinBen paper, Table 2).

Citation

Please cite FinBen and the original dataset (FPB (Malo et al., 2014)):

@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},
}

@article{malo2014good,
  title={Good debt or bad debt: Detecting semantic orientations in economic texts},
  author={Malo, Pekka and Sinha, Ankur and Korhonen, Pekka and Wallenius, Jyrki and Takala, Pyry},
  journal={Journal of the Association for Information Science and Technology},
  volume={65}, number={4}, pages={782--796}, year={2014}
}
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Paper for TheFinAI/en-fpb