English Evaluation Dataset
Collection
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FinBen · TSA
📄 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 | TSA (Cortis et al., 2017) |
| Evaluation metric | F1, Accuracy |
| Source license | CC BY-NC-SA 4.0 |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-tsa", split="test")
print(ds[0])
Example prompt
Given the following financial text, return a sentiment score for Ashtead as a floating-point number ranging from -1 (indicating a very negative or bearish sentiment) to 1 (indicating a very positive or bullish sentiment), with 0 designating neutral sentiment. Return only the numerical score first, follow it with a brief reasoning behind your score.
Text: Ashtead to buy back shares, full-year profi…
Dataset Structure
| Split | Rows |
|---|---|
test |
561 |
| Field | Description |
|---|---|
id |
Example id |
query |
Full instruction prompt given to the model |
answer |
Gold answer / label text |
text |
Raw input text (without instruction) |
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 (TSA (Cortis et al., 2017)):
@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},
}
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