Datasets:

TheFinAI
/

en-ner

pretty_name: FinBen · NER
license: cc-by-sa-3.0
language:
  - en
task_categories:
  - token-classification
size_categories:
  - n<1K
source_datasets:
  - extended
tags:
  - finance
  - benchmark
  - thefinai
  - finben
  - flare
dataset_info:
  features:
    - name: query
      dtype: string
    - name: answer
      dtype: string
    - name: label
      sequence: string
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 470523
      num_examples: 408
    - name: valid
      num_bytes: 101644
      num_examples: 103
    - name: test
      num_bytes: 156592
      num_examples: 98
  download_size: 224350
  dataset_size: 728759
extra_gated_heading: Request access to FinBen · NER
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-SA 3.0) 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 · NER

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

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


Task	named entity recognition
Original dataset	NER (Alvarado et al., 2015)
Evaluation metric	Entity F1
Source license	CC BY-SA 3.0
Language	en

Quick Start

from datasets import load_dataset

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

Example prompt

In the sentences extracted from financial agreements in U.S. SEC filings, identify the named entities that represent a person ('PER'), an organization ('ORG'), or a location ('LOC'). The required answer format is: 'entity name, entity type'.
Text: Subordinated Loan Agreement - Silicium de Provence SAS and Evergreen Solar Inc . 7 - December 2007 [ HERBERT SMITH LOGO ] ..............................…

Dataset Structure

Split	Rows
`train`	408
`test`	98
`valid`	103

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

License

The original data is released under CC BY-SA 3.0 (FinBen paper, Table 2).

Citation

Please cite FinBen and the original dataset (NER (Alvarado et al., 2015)):

@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},
}